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01NAEX_HW2_bridge_strength_experiment_finalising.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/aleksejalex/55f93f1fbda86945616d6f0def60e5ec/01naex_hw2_bridge_strength_experiment_finalising.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "e5e8ba8e" | |
| }, | |
| "source": [ | |
| "# 01NAEX - HW2: Paper Bridge Strength Experiment" | |
| ], | |
| "id": "e5e8ba8e" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "d592b19b" | |
| }, | |
| "source": [ | |
| "**Team Name:**\n", | |
| " - Drocárová Soňa\n", | |
| " - Fucsiková Tereza\n", | |
| " - Kreanová Jazmína\n", | |
| " - Molnárová Soňa\n", | |
| " - Gaj Aleksej\n" | |
| ], | |
| "id": "d592b19b" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "d0da3d99" | |
| }, | |
| "source": [ | |
| "## Assignment" | |
| ], | |
| "id": "d0da3d99" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9xg0tsbLzmrF" | |
| }, | |
| "source": [ | |
| "Design an experiment to measure the maximum weight a paper bridge can hold before collapsing, examining the dependence on 6 factors. Due to time constraints, you are encouraged to use a $\\frac{1}{2}$ fraction design: $2^{6-1}$ or a $\\frac{1}{4}$ fraction design: $2^{6-2}$, or a full factorial design.\n", | |
| "The response variable is the maximum weight the paper bridge can hold. The factors examined are:\n", | |
| " * Number of Paper Layers - 2 layers vs. 4 layers (numerical variable)\n", | |
| " * Bridge Length - Short vs. Long (numerical variable)\n", | |
| " * Bridge Width - Narrow vs. Wide (numerical variable)\n", | |
| " * Type of Construction - 1 fold vs. 3 folds (numerical variable)\n", | |
| " * Weight Distribution Method - Center load vs. Distributed load (factor variable)\n", | |
| " * The Height of the Bridge Above the Valley (numerical variable)\n", | |
| "\n", | |
| "Due to the nature of this experiment, consider safety and practical aspects of bridge construction and testing in a classroom setting." | |
| ], | |
| "id": "9xg0tsbLzmrF" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "a8893415" | |
| }, | |
| "source": [ | |
| "## Experiment Design Questions" | |
| ], | |
| "id": "a8893415" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "3445cae6" | |
| }, | |
| "source": [ | |
| "\n", | |
| " 1. How will you define the low and high levels for each variable, both numerical and factor? What and how will you record data?\n", | |
| " 2. What are the generators used in the design and why did you choose them? Explain the resolution and alias structure for the chosen design.\n", | |
| " 3. Calculate the individual effects and their interactions.\n", | |
| " 4. Determine which factors and their interactions appear significant. Use appropriate plots (main effects plot, interaction plot, boxplots) for data visualization.\n", | |
| " 5. Perform an analysis of variance and find a valid model for the experiment.\n", | |
| " 6. If feasible, incorporate measurements at the 'center points' of the numerical variables and analyze their effects.\n", | |
| " 7. Construct a regression model using the coded quantitative variables and any significant factor variables.\n", | |
| " 8. For two chosen numerical variables, create a contour plot to find the optimal values for maximizing bridge strength under the additional condition of 3 layers, minimal width 6cm and minimal length of 22 cm.\n", | |
| " " | |
| ], | |
| "id": "3445cae6" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "bc95332d" | |
| }, | |
| "source": [ | |
| "## Additional Notes" | |
| ], | |
| "id": "bc95332d" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "9a17d7c5" | |
| }, | |
| "source": [ | |
| "\n", | |
| " - Consider practical limitations of your classroom setting.\n", | |
| " - Ensure that all measurements are taken randomly.\n", | |
| " - Discuss the implications of your findings in the context of the experiment.\n", | |
| " " | |
| ], | |
| "id": "9a17d7c5" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 49, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "pvRGF3CnNDKZ", | |
| "outputId": "175b2a4d-8a8e-429c-f74f-8d96c7322e58" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Requirement already satisfied: rpy2 in /usr/local/lib/python3.10/dist-packages (3.4.2)\n", | |
| "Requirement already satisfied: cffi>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from rpy2) (1.16.0)\n", | |
| "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from rpy2) (3.1.2)\n", | |
| "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from rpy2) (2023.3.post1)\n", | |
| "Requirement already satisfied: tzlocal in /usr/local/lib/python3.10/dist-packages (from rpy2) (5.2)\n", | |
| "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.10.0->rpy2) (2.21)\n", | |
| "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->rpy2) (2.1.3)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# import R into Python\n", | |
| "!pip install rpy2" | |
| ], | |
| "id": "pvRGF3CnNDKZ" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 50, | |
| "metadata": { | |
| "id": "lQOnVXWcNG5N", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "a7fff97c-4f9a-4287-e2fb-91b7b69724cf" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "The rpy2.ipython extension is already loaded. To reload it, use:\n", | |
| " %reload_ext rpy2.ipython\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%load_ext rpy2.ipython" | |
| ], | |
| "id": "lQOnVXWcNG5N" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 51, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "y1O4B3sbrD2b", | |
| "outputId": "fabf26cf-21ff-446c-96ad-7d4e91577e4d" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
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| "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n", | |
| "\n", | |
| "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: downloaded 752 KB\n", | |
| "\n", | |
| "\n", | |
| "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n", | |
| "\n", | |
| "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n", | |
| "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The downloaded source packages are in\n", | |
| "\t‘/tmp/RtmpiJRn85/downloaded_packages’\n", | |
| "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n", | |
| "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[[1]]\n", | |
| " [1] \"pid\" \"rsm\" \"reticulate\" \"FrF2\" \"DoE.base\" \n", | |
| " [6] \"conf.design\" \"grid\" \"lattice\" \"MASS\" \"lubridate\" \n", | |
| "[11] \"forcats\" \"stringr\" \"dplyr\" \"purrr\" \"readr\" \n", | |
| "[16] \"tidyr\" \"tibble\" \"ggplot2\" \"tidyverse\" \"tools\" \n", | |
| "[21] \"stats\" \"graphics\" \"grDevices\" \"utils\" \"datasets\" \n", | |
| "[26] \"methods\" \"base\" \n", | |
| "\n", | |
| "[[2]]\n", | |
| " [1] \"pid\" \"rsm\" \"reticulate\" \"FrF2\" \"DoE.base\" \n", | |
| " [6] \"conf.design\" \"grid\" \"lattice\" \"MASS\" \"lubridate\" \n", | |
| "[11] \"forcats\" \"stringr\" \"dplyr\" \"purrr\" \"readr\" \n", | |
| "[16] \"tidyr\" \"tibble\" \"ggplot2\" \"tidyverse\" \"tools\" \n", | |
| "[21] \"stats\" \"graphics\" \"grDevices\" \"utils\" \"datasets\" \n", | |
| "[26] \"methods\" \"base\" \n", | |
| "\n", | |
| "[[3]]\n", | |
| " [1] \"pid\" \"rsm\" \"reticulate\" \"FrF2\" \"DoE.base\" \n", | |
| " [6] \"conf.design\" \"grid\" \"lattice\" \"MASS\" \"lubridate\" \n", | |
| "[11] \"forcats\" \"stringr\" \"dplyr\" \"purrr\" \"readr\" \n", | |
| "[16] \"tidyr\" \"tibble\" \"ggplot2\" \"tidyverse\" \"tools\" \n", | |
| "[21] \"stats\" \"graphics\" \"grDevices\" \"utils\" \"datasets\" \n", | |
| "[26] \"methods\" \"base\" \n", | |
| "\n", | |
| "[[4]]\n", | |
| " [1] \"pid\" \"rsm\" \"reticulate\" \"FrF2\" \"DoE.base\" \n", | |
| " [6] \"conf.design\" \"grid\" \"lattice\" \"MASS\" \"lubridate\" \n", | |
| "[11] \"forcats\" \"stringr\" \"dplyr\" \"purrr\" \"readr\" \n", | |
| "[16] \"tidyr\" \"tibble\" \"ggplot2\" \"tidyverse\" \"tools\" \n", | |
| "[21] \"stats\" \"graphics\" \"grDevices\" \"utils\" \"datasets\" \n", | |
| "[26] \"methods\" \"base\" \n", | |
| "\n", | |
| "[[5]]\n", | |
| " [1] \"pid\" \"rsm\" \"reticulate\" \"FrF2\" \"DoE.base\" \n", | |
| " [6] \"conf.design\" \"grid\" \"lattice\" \"MASS\" \"lubridate\" \n", | |
| "[11] \"forcats\" \"stringr\" \"dplyr\" \"purrr\" \"readr\" \n", | |
| "[16] \"tidyr\" \"tibble\" \"ggplot2\" \"tidyverse\" \"tools\" \n", | |
| "[21] \"stats\" \"graphics\" \"grDevices\" \"utils\" \"datasets\" \n", | |
| "[26] \"methods\" \"base\" \n", | |
| "\n", | |
| "[[6]]\n", | |
| " [1] \"pid\" \"rsm\" \"reticulate\" \"FrF2\" \"DoE.base\" \n", | |
| " [6] \"conf.design\" \"grid\" \"lattice\" \"MASS\" \"lubridate\" \n", | |
| "[11] \"forcats\" \"stringr\" \"dplyr\" \"purrr\" \"readr\" \n", | |
| "[16] \"tidyr\" \"tibble\" \"ggplot2\" \"tidyverse\" \"tools\" \n", | |
| "[21] \"stats\" \"graphics\" \"grDevices\" \"utils\" \"datasets\" \n", | |
| "[26] \"methods\" \"base\" \n", | |
| "\n", | |
| "[[7]]\n", | |
| " [1] \"pid\" \"rsm\" \"reticulate\" \"FrF2\" \"DoE.base\" \n", | |
| " [6] \"conf.design\" \"grid\" \"lattice\" \"MASS\" \"lubridate\" \n", | |
| "[11] \"forcats\" \"stringr\" \"dplyr\" \"purrr\" \"readr\" \n", | |
| "[16] \"tidyr\" \"tibble\" \"ggplot2\" \"tidyverse\" \"tools\" \n", | |
| "[21] \"stats\" \"graphics\" \"grDevices\" \"utils\" \"datasets\" \n", | |
| "[26] \"methods\" \"base\" \n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "list_of_packages <- c(\"tidyverse\",\"MASS\", \"lattice\", \"FrF2\", \"reticulate\", \"rsm\", \"pid\")\n", | |
| "missing_packages <- list_of_packages[!(list_of_packages %in% installed.packages()[,\"Package\"])]\n", | |
| "missing_packages\n", | |
| "\n", | |
| "system(\"sudo apt-get install libgmp-dev\")\n", | |
| "install.packages(\"gmp\")\n", | |
| "system(\"sudo apt-get install libgmp-dev\")\n", | |
| "install.packages(\"partitions\")\n", | |
| "system(\"sudo apt-get install libgmp-dev\")\n", | |
| "install.packages(\"DoE.base\")\n", | |
| "\n", | |
| "\n", | |
| "if(length(missing_packages)) install.packages(missing_packages)\n", | |
| "lapply(list_of_packages, library, character.only = TRUE)" | |
| ], | |
| "id": "y1O4B3sbrD2b" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "2PcOk2YxXNzh" | |
| }, | |
| "source": [ | |
| "#1. How will you define the low and high levels for each variable, both numerical and factor? What and how will you record data?" | |
| ], | |
| "id": "2PcOk2YxXNzh" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "5HqPeqJXXWmZ" | |
| }, | |
| "source": [ | |
| "* **(A) Bridge Width** (numerical variable): *low* 4.5 vs. *high* 6.5 \\[cm]\n", | |
| "* **(B) Number of Paper Layers** (numerical variable): *low* 1 vs. *hig*h 2\n", | |
| "* **(C) Type of construction** (numerical variable): *low* 3 vs. *high* 5 \\[folds]\n", | |
| "* **(D) Reinforcement - office clamps** (factor variable): *low* 0 = without clamps vs. *high* 1 = with clamps\n", | |
| "* **(E) Weight Distribution Method** (factor variable): *low* 0 = Center load vs. *high* 1 = Distributed load\n", | |
| "* **(F) Day of the experiment** (numerical variable): *low* 1 vs. *hig*h 2\n", | |
| "---\n", | |
| "* **Constants**: Bridge Height (3.5 cm), Bridge Length (3.5 cm), Props (2.5cm)\n", | |
| "* **Response**: The maximum number of batteries the paper bridge can hold\n", | |
| "---\n", | |
| "To design an experiment we are using the $\\frac{1}{2}$ fraction design: $2^{6-1}$." | |
| ], | |
| "id": "5HqPeqJXXWmZ" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 52, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "USWPCcTlXcV7", | |
| "outputId": "30087ff5-60dd-497e-b22d-784f8d69e545" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " height width layers folds length clamps weight_distrib max_num_of_batteries\n", | |
| "1 3.5 4.5 1 5 24.5 0 0 8\n", | |
| "2 3.5 4.5 1 5 24.5 0 1 8\n", | |
| "3 3.5 4.5 1 5 24.5 1 1 8\n", | |
| "4 3.5 4.5 1 5 24.5 1 0 4\n", | |
| "5 3.5 4.5 2 5 24.5 0 1 25\n", | |
| "6 3.5 4.5 2 5 24.5 0 0 16\n", | |
| " support_length exp_day\n", | |
| "1 2.5 2\n", | |
| "2 2.5 1\n", | |
| "3 2.5 2\n", | |
| "4 2.5 1\n", | |
| "5 2.5 2\n", | |
| "6 2.5 1\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "url = \"https://gist.githubusercontent.com/aleksejalex/8c6f79713b6adab830b96974a9bf7aad/raw/965be061b72984f791ec8fee12a26e07d6a354ab/NAEX-bridge-CP.csv\"\n", | |
| "df_raw = read.table(url, header=TRUE, sep=\",\")\n", | |
| "head(df_raw)" | |
| ], | |
| "id": "USWPCcTlXcV7" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 53, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "sHp6cT9OXj7Y", | |
| "outputId": "3caee72c-8057-41c2-8cbc-eb915405fa04" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " width layers folds clamps weight_distrib exp_day max_num_of_batteries\n", | |
| "1 4.5 1 5 0 0 2 8\n", | |
| "2 4.5 1 5 0 1 1 8\n", | |
| "3 4.5 1 5 1 1 2 8\n", | |
| "4 4.5 1 5 1 0 1 4\n", | |
| "5 4.5 2 5 0 1 2 25\n", | |
| "6 4.5 2 5 0 0 1 16\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "# remove center points\n", | |
| "columns_to_keep <- c(2, 3, 4, 6, 7, 10, 8)\n", | |
| "rows_to_delete <- df_raw$width == 5.5\n", | |
| "df <- df_raw[!rows_to_delete, columns_to_keep]\n", | |
| "head(df)" | |
| ], | |
| "id": "sHp6cT9OXj7Y" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 54, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "bhNVs0rXXl5l", | |
| "outputId": "b4d5f639-7521-4bd0-8d87-152312ba2cbb" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " width layers folds clamps weight_distrib exp_day max_num_of_batteries\n", | |
| "1 4.5 1 5 0 0 2 8\n", | |
| "2 4.5 1 5 0 1 1 8\n", | |
| "3 4.5 1 5 1 1 2 8\n", | |
| "4 4.5 1 5 1 0 1 4\n", | |
| "5 4.5 2 5 0 1 2 25\n", | |
| "6 4.5 2 5 0 0 1 16\n", | |
| "7 4.5 2 5 1 1 1 28\n", | |
| "8 4.5 2 5 1 0 2 13\n", | |
| "9 4.5 1 3 0 1 2 26\n", | |
| "10 4.5 1 3 0 0 1 6\n", | |
| "11 4.5 1 3 1 1 1 12\n", | |
| "12 4.5 1 3 1 0 2 2\n", | |
| "13 4.5 2 3 0 1 1 36\n", | |
| "14 4.5 2 3 0 0 2 25\n", | |
| "15 4.5 2 3 1 1 2 48\n", | |
| "16 4.5 2 3 1 0 1 17\n", | |
| "17 6.5 2 5 0 1 1 36\n", | |
| "18 6.5 2 5 0 0 2 15\n", | |
| "19 6.5 2 5 1 1 2 24\n", | |
| "20 6.5 2 5 1 0 1 6\n", | |
| "21 6.5 1 5 0 1 2 22\n", | |
| "22 6.5 1 5 0 0 1 5\n", | |
| "23 6.5 1 5 1 1 1 10\n", | |
| "24 6.5 1 5 1 0 2 5\n", | |
| "25 6.5 2 3 0 1 2 45\n", | |
| "26 6.5 2 3 0 0 1 13\n", | |
| "27 6.5 2 3 1 1 1 18\n", | |
| "28 6.5 2 3 1 0 2 9\n", | |
| "29 6.5 1 3 0 1 1 26\n", | |
| "30 6.5 1 3 0 0 2 8\n", | |
| "31 6.5 1 3 1 1 2 9\n", | |
| "32 6.5 1 3 1 0 1 2\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "data <- df %>% transmute(width = as.factor(width),\n", | |
| " layers = as.factor(layers),\n", | |
| " folds = as.factor(folds),\n", | |
| " clamps = as.factor(clamps),\n", | |
| " weight_distrib = as.factor(weight_distrib),\n", | |
| " exp_day = as.factor(exp_day),\n", | |
| " max_num_of_batteries = as.numeric(max_num_of_batteries))\n", | |
| "\n", | |
| "print(data)" | |
| ], | |
| "id": "bhNVs0rXXl5l" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 55, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "GHDaIilaXnk_", | |
| "outputId": "9c6c3b8d-91b2-48cd-a2c5-3b490dd3962f" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " width layers folds clamps weight_distrib exp_day max_num_of_batteries\n", | |
| "1 -1 -1 -1 -1 -1 -1 8\n", | |
| "2 1 -1 -1 -1 -1 1 8\n", | |
| "3 -1 1 -1 -1 -1 1 8\n", | |
| "4 1 1 -1 -1 -1 -1 4\n", | |
| "5 -1 -1 1 -1 -1 1 25\n", | |
| "6 1 -1 1 -1 -1 -1 16\n", | |
| "7 -1 1 1 -1 -1 -1 28\n", | |
| "8 1 1 1 -1 -1 1 13\n", | |
| "9 -1 -1 -1 1 -1 1 26\n", | |
| "10 1 -1 -1 1 -1 -1 6\n", | |
| "11 -1 1 -1 1 -1 -1 12\n", | |
| "12 1 1 -1 1 -1 1 2\n", | |
| "13 -1 -1 1 1 -1 -1 36\n", | |
| "14 1 -1 1 1 -1 1 25\n", | |
| "15 -1 1 1 1 -1 1 48\n", | |
| "16 1 1 1 1 -1 -1 17\n", | |
| "17 -1 -1 -1 -1 1 1 36\n", | |
| "18 1 -1 -1 -1 1 -1 15\n", | |
| "19 -1 1 -1 -1 1 -1 24\n", | |
| "20 1 1 -1 -1 1 1 6\n", | |
| "21 -1 -1 1 -1 1 -1 22\n", | |
| "22 1 -1 1 -1 1 1 5\n", | |
| "23 -1 1 1 -1 1 1 10\n", | |
| "24 1 1 1 -1 1 -1 5\n", | |
| "25 -1 -1 -1 1 1 -1 45\n", | |
| "26 1 -1 -1 1 1 1 13\n", | |
| "27 -1 1 -1 1 1 1 18\n", | |
| "28 1 1 -1 1 1 -1 9\n", | |
| "29 -1 -1 1 1 1 1 26\n", | |
| "30 1 -1 1 1 1 -1 8\n", | |
| "31 -1 1 1 1 1 -1 9\n", | |
| "32 1 1 1 1 1 1 2\n", | |
| "class=design, type= FrF2.generators \n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "m <- FrF2(2^(6-1), 6, replications = 1, randomize = FALSE, generators = c(\"ABCDE\"), factor.names = c(\"width\", \"layers\", \"folds\", \"clamps\", \"weight_distrib\", \"exp_day\"))\n", | |
| "num_of_bat <- data[\"max_num_of_batteries\"]\n", | |
| "m <- add.response(m, num_of_bat)\n", | |
| "\n", | |
| "response.names(m)\n", | |
| "m" | |
| ], | |
| "id": "GHDaIilaXnk_" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "cSOQp7GnXqQL" | |
| }, | |
| "source": [ | |
| "# 2. What are the generators used in the design and why did you choose them? Explain the resolution and alias structure for the chosen design.\n" | |
| ], | |
| "id": "cSOQp7GnXqQL" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "n1_fkR5cXyqz" | |
| }, | |
| "source": [ | |
| "**Generators**\n", | |
| "\n", | |
| "The use of the generator will reduce the number of experimental runs, which grows exponentially as the number of factors and levels increases. This allows us to explore the factor space efficiently by choosing an informative subset of the full model. It is achieved by setting one factor at its high level. In our case, the generator was set up as ABCDE, which is a common choice for a $2^{6-1}$ factorial design. Which means F is set at its high level.\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "**Resolution and Alias Structure**\n", | |
| "\n", | |
| "The resolution indicates how well the design can estimate the main effects and interactions. In our case, we have a Resolution V design, which means that two-factor interactions are aliased with three-factor interactions." | |
| ], | |
| "id": "n1_fkR5cXyqz" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "phjKyLjKYE0X" | |
| }, | |
| "source": [ | |
| "# 3. Calculate the individual effects and their interactions." | |
| ], | |
| "id": "phjKyLjKYE0X" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 56, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "mCcfTjy6YHkF", | |
| "outputId": "051733e7-ac93-41c7-83b2-17fe726c3f0d" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " Effects\n", | |
| "(Intercept) 33.4375\n", | |
| "width1 -14.1875\n", | |
| "layers1 -6.5625\n", | |
| "folds1 3.4375\n", | |
| "clamps1 4.3125\n", | |
| "weight_distrib1 -1.8125\n", | |
| "exp_day1 0.4375\n", | |
| "width1:layers1 1.8125\n", | |
| "width1:folds1 0.0625\n", | |
| "layers1:folds1 2.6875\n", | |
| "width1:clamps1 -3.0625\n", | |
| "layers1:clamps1 -1.9375\n", | |
| "folds1:clamps1 1.5625\n", | |
| "width1:weight_distrib1 -1.6875\n", | |
| "layers1:weight_distrib1 -4.3125\n", | |
| "folds1:weight_distrib1 -13.3125\n", | |
| "clamps1:weight_distrib1 -3.4375\n", | |
| "width1:exp_day1 -1.1875\n", | |
| "layers1:exp_day1 -0.5625\n", | |
| "folds1:exp_day1 1.1875\n", | |
| "clamps1:exp_day1 1.8125\n", | |
| "weight_distrib1:exp_day1 -3.0625\n", | |
| "width1:layers1:folds1 -2.1875\n", | |
| "width1:layers1:clamps1 1.1875\n", | |
| "width1:folds1:clamps1 0.4375\n", | |
| "layers1:folds1:clamps1 1.0625\n", | |
| "width1:layers1:weight_distrib1 4.3125\n", | |
| "width1:folds1:weight_distrib1 4.0625\n", | |
| "layers1:folds1:weight_distrib1 -0.5625\n", | |
| "width1:clamps1:weight_distrib1 2.4375\n", | |
| "layers1:clamps1:weight_distrib1 -0.6875\n", | |
| "folds1:clamps1:weight_distrib1 -1.6875\n", | |
| "width1:layers1:exp_day1 NA\n", | |
| "width1:folds1:exp_day1 NA\n", | |
| "layers1:folds1:exp_day1 NA\n", | |
| "width1:clamps1:exp_day1 NA\n", | |
| "layers1:clamps1:exp_day1 NA\n", | |
| "folds1:clamps1:exp_day1 NA\n", | |
| "width1:weight_distrib1:exp_day1 NA\n", | |
| "layers1:weight_distrib1:exp_day1 NA\n", | |
| "folds1:weight_distrib1:exp_day1 NA\n", | |
| "clamps1:weight_distrib1:exp_day1 NA\n", | |
| "width1:layers1:folds1:clamps1 NA\n", | |
| "width1:layers1:folds1:weight_distrib1 NA\n", | |
| "width1:layers1:clamps1:weight_distrib1 NA\n", | |
| "width1:folds1:clamps1:weight_distrib1 NA\n", | |
| "layers1:folds1:clamps1:weight_distrib1 NA\n", | |
| "width1:layers1:folds1:exp_day1 NA\n", | |
| "width1:layers1:clamps1:exp_day1 NA\n", | |
| "width1:folds1:clamps1:exp_day1 NA\n", | |
| "layers1:folds1:clamps1:exp_day1 NA\n", | |
| "width1:layers1:weight_distrib1:exp_day1 NA\n", | |
| "width1:folds1:weight_distrib1:exp_day1 NA\n", | |
| "layers1:folds1:weight_distrib1:exp_day1 NA\n", | |
| "width1:clamps1:weight_distrib1:exp_day1 NA\n", | |
| "layers1:clamps1:weight_distrib1:exp_day1 NA\n", | |
| "folds1:clamps1:weight_distrib1:exp_day1 NA\n", | |
| "width1:layers1:folds1:clamps1:weight_distrib1 NA\n", | |
| "width1:layers1:folds1:clamps1:exp_day1 NA\n", | |
| "width1:layers1:folds1:weight_distrib1:exp_day1 NA\n", | |
| "width1:layers1:clamps1:weight_distrib1:exp_day1 NA\n", | |
| "width1:folds1:clamps1:weight_distrib1:exp_day1 NA\n", | |
| "layers1:folds1:clamps1:weight_distrib1:exp_day1 NA\n", | |
| "width1:layers1:folds1:clamps1:weight_distrib1:exp_day1 NA\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "lm_coeffs <- lm(max_num_of_batteries ~ width*layers*folds*clamps*weight_distrib*exp_day, data=m)\n", | |
| "effects <-as.data.frame(2*lm_coeffs$coefficients)\n", | |
| "names(effects)[1] <- 'Effects'\n", | |
| "effects" | |
| ], | |
| "id": "mCcfTjy6YHkF" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "4RkpcaMLYOVH" | |
| }, | |
| "source": [ | |
| "# 4. Determine which factors and their interactions appear significant. Use appropriate plots (main effects plot, interaction plot, boxplots) for data visualization." | |
| ], | |
| "id": "4RkpcaMLYOVH" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 57, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 497 | |
| }, | |
| "id": "K4paw7fIqv95", | |
| "outputId": "117f80c7-16b3-4523-d547-d638cb155ac2" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "MEPlot(m)" | |
| ], | |
| "id": "K4paw7fIqv95" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "JJcHvfJ1qywN" | |
| }, | |
| "source": [ | |
| "Main effects plot suggests, that factors width and layers have the biggest effect on the maximum weight." | |
| ], | |
| "id": "JJcHvfJ1qywN" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 58, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 497 | |
| }, | |
| "id": "bO1eZWMOq3BA", | |
| "outputId": "d7a1262f-7ff1-4e51-8202-cbdfb7e3298b" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "IAPlot(m)" | |
| ], | |
| "id": "bO1eZWMOq3BA" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "oUa2Cc1xq6ww" | |
| }, | |
| "source": [ | |
| "The interaction plot suggest there might be some interaction between weight distribution and all clamps, number of folds layers and experiment day. Another possible interaction suggested by the plot is between clamps and experiment day" | |
| ], | |
| "id": "oUa2Cc1xq6ww" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 59, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 497 | |
| }, | |
| "id": "75UGLcS_q_uD", | |
| "outputId": "b8118f94-5354-4654-f027-bc7dfcb7afeb" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "par(mfrow=c(2,3))\n", | |
| "boxplot(m$max_num_of_batteries ~ m$width, main = \"Width\")\n", | |
| "boxplot(m$max_num_of_batteries ~ m$layers, main = \"Layers\")\n", | |
| "boxplot(m$max_num_of_batteries ~ m$folds, main = \"Folds\")\n", | |
| "boxplot(m$max_num_of_batteries ~ m$clamps, main = \"Clamps\")\n", | |
| "boxplot(m$max_num_of_batteries ~ m$weight_distrib, main = \"Weight Distribution\")\n", | |
| "boxplot(m$max_num_of_batteries ~ m$exp_day, main = \"Experiment Day\")" | |
| ], | |
| "id": "75UGLcS_q_uD" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Fi8OfpA6rDel" | |
| }, | |
| "source": [ | |
| "There appears to be a difference in the median number of batteries supported at the two levels of width. There also seems to be slight difference in the median values for the number of layers, with the higher level supporting more batteries." | |
| ], | |
| "id": "Fi8OfpA6rDel" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "FH5m8l1tYTKb" | |
| }, | |
| "source": [ | |
| "# 5. Perform an analysis of variance and find a valid model for the experiment." | |
| ], | |
| "id": "FH5m8l1tYTKb" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 60, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "sCLseh35stpA", | |
| "outputId": "080ac9f5-9bd2-4a88-e7df-11be28cf3d7c" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " Df Sum Sq Mean Sq F value Pr(>F) \n", | |
| "width 1 1610.3 1610.3 38.564 0.000100 ***\n", | |
| "layers 1 344.5 344.5 8.251 0.016599 * \n", | |
| "folds 1 94.5 94.5 2.264 0.163333 \n", | |
| "clamps 1 148.8 148.8 3.563 0.088409 . \n", | |
| "weight_distrib 1 26.3 26.3 0.629 0.445992 \n", | |
| "exp_day 1 1.5 1.5 0.037 0.851969 \n", | |
| "width:layers 1 26.3 26.3 0.629 0.445992 \n", | |
| "width:folds 1 0.0 0.0 0.001 0.978713 \n", | |
| "width:clamps 1 75.0 75.0 1.797 0.209747 \n", | |
| "width:weight_distrib 1 22.8 22.8 0.546 0.477108 \n", | |
| "width:exp_day 1 11.3 11.3 0.270 0.614529 \n", | |
| "layers:folds 1 57.8 57.8 1.384 0.266702 \n", | |
| "layers:clamps 1 30.0 30.0 0.719 0.416242 \n", | |
| "layers:weight_distrib 1 148.8 148.8 3.563 0.088409 . \n", | |
| "layers:exp_day 1 2.5 2.5 0.061 0.810499 \n", | |
| "folds:clamps 1 19.5 19.5 0.468 0.509564 \n", | |
| "folds:weight_distrib 1 1417.8 1417.8 33.954 0.000167 ***\n", | |
| "folds:exp_day 1 11.3 11.3 0.270 0.614529 \n", | |
| "clamps:weight_distrib 1 94.5 94.5 2.264 0.163333 \n", | |
| "clamps:exp_day 1 26.3 26.3 0.629 0.445992 \n", | |
| "weight_distrib:exp_day 1 75.0 75.0 1.797 0.209747 \n", | |
| "Residuals 10 417.6 41.8 \n", | |
| "---\n", | |
| "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "summary(lm_full<-lm(max_num_of_batteries ~ (width+layers+folds+clamps+weight_distrib+exp_day)^2, data=m))\n", | |
| "lm_full %>% aov %>% summary" | |
| ], | |
| "id": "sCLseh35stpA" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 61, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ssFft0bls5SB", | |
| "outputId": "727dab21-cd07-4ebc-e59b-007f647b23ee" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " Df Sum Sq Mean Sq F value Pr(>F) \n", | |
| "width 1 1610.3 1610.3 44.345 6.90e-07 ***\n", | |
| "layers 1 344.5 344.5 9.488 0.00512 ** \n", | |
| "clamps 1 148.8 148.8 4.097 0.05422 . \n", | |
| "weight_distrib 1 26.3 26.3 0.724 0.40332 \n", | |
| "folds 1 94.5 94.5 2.603 0.11972 \n", | |
| "weight_distrib:folds 1 1417.8 1417.8 39.044 1.86e-06 ***\n", | |
| "layers:weight_distrib 1 148.8 148.8 4.097 0.05422 . \n", | |
| "Residuals 24 871.5 36.3 \n", | |
| "---\n", | |
| "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "lm_reduced<-lm(max_num_of_batteries~width+layers+clamps+weight_distrib+folds+folds:weight_distrib+layers:weight_distrib,data=m)\n", | |
| "lm_reduced %>% aov %>% summary" | |
| ], | |
| "id": "ssFft0bls5SB" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 62, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "PvIPzlrYs5dt", | |
| "outputId": "8e83e629-7543-49f8-a920-1a54a9afcc61" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "Call:\n", | |
| "lm.default(formula = max_num_of_batteries ~ width + layers + \n", | |
| " clamps + weight_distrib + folds + folds:weight_distrib + \n", | |
| " layers:weight_distrib, data = m)\n", | |
| "\n", | |
| "Residuals:\n", | |
| " Min 1Q Median 3Q Max \n", | |
| "-8.2500 -3.0469 -0.7813 3.0469 13.8750 \n", | |
| "\n", | |
| "Coefficients:\n", | |
| " Estimate Std. Error t value Pr(>|t|) \n", | |
| "(Intercept) 16.7187 1.0653 15.695 4.04e-14 ***\n", | |
| "width1 -7.0938 1.0653 -6.659 6.90e-07 ***\n", | |
| "layers1 -3.2812 1.0653 -3.080 0.00512 ** \n", | |
| "clamps1 2.1563 1.0653 2.024 0.05422 . \n", | |
| "weight_distrib1 -0.9062 1.0653 -0.851 0.40332 \n", | |
| "folds1 1.7188 1.0653 1.613 0.11972 \n", | |
| "weight_distrib1:folds1 -6.6563 1.0653 -6.249 1.86e-06 ***\n", | |
| "layers1:weight_distrib1 -2.1563 1.0653 -2.024 0.05422 . \n", | |
| "---\n", | |
| "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", | |
| "\n", | |
| "Residual standard error: 6.026 on 24 degrees of freedom\n", | |
| "Multiple R-squared: 0.8131,\tAdjusted R-squared: 0.7586 \n", | |
| "F-statistic: 14.91 on 7 and 24 DF, p-value: 2.384e-07\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "summary(lm_reduced)" | |
| ], | |
| "id": "PvIPzlrYs5dt" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 63, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Yr73gcMltArB", | |
| "outputId": "962bc52f-a3c3-4e07-86b0-57eced225a41" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "NULL\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "# full_model <- lm(max_num_of_batteries ~ (width + layers + folds + clamps + weight_distribution + experiment_day)^2, data=m)\n", | |
| "\n", | |
| "# # Perform backward selection using BIC instead of AIC\n", | |
| "# backward_model_bic <- stepAIC(full_model, direction=\"backward\", k=log(nrow(df)))" | |
| ], | |
| "id": "Yr73gcMltArB" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 64, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "9oLW6jmNtA2M", | |
| "outputId": "226c58e3-be58-414e-804f-725db6d5ac6d" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "NULL\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "# summary(backward_model_bic)\n", | |
| "# backward_model_bic%>% aov %>% summary" | |
| ], | |
| "id": "9oLW6jmNtA2M" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0ER8oeu0YTbr" | |
| }, | |
| "source": [ | |
| "# 6. If feasible, incorporate measurements at the 'center points' of the numerical variables and analyze their effects." | |
| ], | |
| "id": "0ER8oeu0YTbr" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 65, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "KHtD6A2BtTDn", | |
| "outputId": "ec6a82a9-d378-40fd-b7fa-4ec91a05e7a0" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " width layers folds clamps weight_distrib max_num_of_batteries exp_day\n", | |
| "1 4.5 1 5 0 0 8 2\n", | |
| "2 4.5 1 5 0 1 8 1\n", | |
| "3 4.5 1 5 1 1 8 2\n", | |
| "4 4.5 1 5 1 0 4 1\n", | |
| "5 4.5 2 5 0 1 25 2\n", | |
| "6 4.5 2 5 0 0 16 1\n", | |
| "7 4.5 2 5 1 1 28 1\n", | |
| "8 4.5 2 5 1 0 13 2\n", | |
| "9 4.5 1 3 0 1 26 2\n", | |
| "10 4.5 1 3 0 0 6 1\n", | |
| "11 4.5 1 3 1 1 12 1\n", | |
| "12 4.5 1 3 1 0 2 2\n", | |
| "13 4.5 2 3 0 1 36 1\n", | |
| "14 4.5 2 3 0 0 25 2\n", | |
| "15 4.5 2 3 1 1 48 2\n", | |
| "16 4.5 2 3 1 0 17 1\n", | |
| "17 6.5 2 5 0 1 36 1\n", | |
| "18 6.5 2 5 0 0 15 2\n", | |
| "19 6.5 2 5 1 1 24 2\n", | |
| "20 6.5 2 5 1 0 6 1\n", | |
| "21 6.5 1 5 0 1 22 2\n", | |
| "22 6.5 1 5 0 0 5 1\n", | |
| "23 6.5 1 5 1 1 10 1\n", | |
| "24 6.5 1 5 1 0 5 2\n", | |
| "25 6.5 2 3 0 1 45 2\n", | |
| "26 6.5 2 3 0 0 13 1\n", | |
| "27 6.5 2 3 1 1 18 1\n", | |
| "28 6.5 2 3 1 0 9 2\n", | |
| "29 6.5 1 3 0 1 26 1\n", | |
| "30 6.5 1 3 0 0 8 2\n", | |
| "31 6.5 1 3 1 1 9 2\n", | |
| "32 6.5 1 3 1 0 2 1\n", | |
| "33 5.5 1 4 0 0 6 1\n", | |
| "34 5.5 1 4 0 1 20 1\n", | |
| "35 5.5 1 4 1 0 6 1\n", | |
| "36 5.5 1 4 1 1 14 1\n", | |
| "37 5.5 2 4 0 0 20 1\n", | |
| "38 5.5 2 4 0 1 27 1\n", | |
| "39 5.5 2 4 1 0 13 1\n", | |
| "40 5.5 2 4 1 1 34 1\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "# Create a dataframe containing center points\n", | |
| "df_new = df_raw[, c(2, 3, 4, 6, 7, 8, 10)]\n", | |
| "\n", | |
| "# Define factors\n", | |
| "df_new[, c(4, 5, 7)] = lapply(df_new[, c(4, 5, 7)], as.factor)\n", | |
| "df_new" | |
| ], | |
| "id": "KHtD6A2BtTDn" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 66, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "RSjCuFV6tTP2", | |
| "outputId": "a44ee024-b81c-40bc-97f1-30faf1ef7def" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " Df Sum Sq Mean Sq F value Pr(>F) \n", | |
| "width 1 26.3 26.3 1.003 0.32982 \n", | |
| "layers 1 1703.0 1703.0 65.006 2.19e-07 ***\n", | |
| "folds 1 148.8 148.8 5.679 0.02839 * \n", | |
| "clamps 1 308.0 308.0 11.758 0.00299 ** \n", | |
| "weight_distrib 1 1918.2 1918.2 73.220 9.28e-08 ***\n", | |
| "exp_day 1 50.4 50.4 1.924 0.18231 \n", | |
| "width:layers 1 94.5 94.5 3.608 0.07364 . \n", | |
| "width:folds 1 94.5 94.5 3.608 0.07364 . \n", | |
| "width:clamps 1 148.8 148.8 5.679 0.02839 * \n", | |
| "width:weight_distrib 1 22.8 22.8 0.870 0.36341 \n", | |
| "width:exp_day 1 1.5 1.5 0.058 0.81170 \n", | |
| "layers:folds 1 22.8 22.8 0.870 0.36341 \n", | |
| "layers:clamps 1 5.6 5.6 0.215 0.64865 \n", | |
| "layers:weight_distrib 1 126.0 126.0 4.810 0.04166 * \n", | |
| "layers:exp_day 1 14.0 14.0 0.535 0.47392 \n", | |
| "folds:clamps 1 30.0 30.0 1.146 0.29847 \n", | |
| "folds:weight_distrib 1 75.0 75.0 2.864 0.10782 \n", | |
| "folds:exp_day 1 38.3 38.3 1.461 0.24238 \n", | |
| "clamps:weight_distrib 1 11.0 11.0 0.421 0.52471 \n", | |
| "clamps:exp_day 1 14.0 14.0 0.535 0.47392 \n", | |
| "weight_distrib:exp_day 1 13.1 13.1 0.499 0.48908 \n", | |
| "Residuals 18 471.6 26.2 \n", | |
| "---\n", | |
| "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "# ANOVA\n", | |
| "lm_center = lm(max_num_of_batteries ~ .^2, data = df_new)\n", | |
| "lm_center %>% aov %>% summary" | |
| ], | |
| "id": "RSjCuFV6tTP2" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 67, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "YAT0prIitTYJ", | |
| "outputId": "d83d6761-ea8d-4533-cef7-2abf97e0ceb2" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " Df Sum Sq Mean Sq F value Pr(>F) \n", | |
| "width 1 26.3 26.3 0.877 0.35611 \n", | |
| "layers 1 1703.0 1703.0 56.813 1.39e-08 ***\n", | |
| "folds 1 148.8 148.8 4.963 0.03304 * \n", | |
| "clamps 1 308.0 308.0 10.276 0.00305 ** \n", | |
| "weight_distrib 1 1918.2 1918.2 63.992 3.93e-09 ***\n", | |
| "layers:weight_distrib 1 126.0 126.0 4.204 0.04859 * \n", | |
| "width:clamps 1 148.8 148.8 4.963 0.03304 * \n", | |
| "Residuals 32 959.2 30.0 \n", | |
| "---\n", | |
| "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "# Extract the significant effects\n", | |
| "lm_center = lm(max_num_of_batteries ~ width + layers + folds + clamps + weight_distrib + layers:weight_distrib + width:clamps, data = df_new)\n", | |
| "lm_center %>% aov %>% summary" | |
| ], | |
| "id": "YAT0prIitTYJ" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "I06HH7jutTsC" | |
| }, | |
| "source": [ | |
| "Outside of `clamps`, `weight_distrib` and `exp_day` it seemed useful to consider the variables as numeric. After adding center points, the only significant effects were observed in variables `layers`, `weight_distrib`, `clamps` and `folds` provided that we are cunducting the analysis on significance level of 0.05. Interactions `width:clamps` and `layers:weight_distrib` were also recognized as significant." | |
| ], | |
| "id": "I06HH7jutTsC" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "eWpofTt1YTtQ" | |
| }, | |
| "source": [ | |
| "# 7. Construct a regression model using the coded quantitative variables and any significant factor variables." | |
| ], | |
| "id": "eWpofTt1YTtQ" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 68, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "MV9JspKMOXBd", | |
| "outputId": "7d3a2cae-d480-412f-848d-256a65665d63" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.14.0)\n", | |
| "Requirement already satisfied: numpy>=1.18 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.23.5)\n", | |
| "Requirement already satisfied: scipy!=1.9.2,>=1.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.11.4)\n", | |
| "Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.5.3)\n", | |
| "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (0.5.3)\n", | |
| "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (23.2)\n", | |
| "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0->statsmodels) (2.8.2)\n", | |
| "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0->statsmodels) (2023.3.post1)\n", | |
| "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!pip install --upgrade statsmodels" | |
| ], | |
| "id": "MV9JspKMOXBd" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 69, | |
| "metadata": { | |
| "id": "YoB08a5pOe0v" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# imports\n", | |
| "import numpy as np\n", | |
| "import scipy as scp\n", | |
| "import statsmodels as sm\n", | |
| "import statsmodels.api as sma\n", | |
| "import statsmodels.formula.api as smf\n", | |
| "import seaborn as sbn\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import pandas as pd\n", | |
| "from scipy import stats" | |
| ], | |
| "id": "YoB08a5pOe0v" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 70, | |
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| "height": 206 | |
| }, | |
| "id": "EmCeQifPOeg0", | |
| "outputId": "31433071-78cc-4757-de22-5244309fcbe9" | |
| }, | |
| "outputs": [ | |
| { | |
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| " height width layers folds length clamps weight_distrib \\\n", | |
| "0 3.5 4.5 1 5 24.5 0 0 \n", | |
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| "3 3.5 4.5 1 5 24.5 1 0 \n", | |
| "4 3.5 4.5 2 5 24.5 0 1 \n", | |
| "\n", | |
| " max_num_of_batteries support_length exp_day \n", | |
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| " margin-bottom: 4px;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-22ee0637-099e-4274-ac27-71a3698b90c4 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-22ee0637-099e-4274-ac27-71a3698b90c4');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| "\n", | |
| "\n", | |
| "<div id=\"df-b9133a52-29e7-4f21-a1b1-8a0dcccf3c61\">\n", | |
| " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b9133a52-29e7-4f21-a1b1-8a0dcccf3c61')\"\n", | |
| " title=\"Suggest charts\"\n", | |
| " style=\"display:none;\">\n", | |
| "\n", | |
| "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <g>\n", | |
| " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n", | |
| " </g>\n", | |
| "</svg>\n", | |
| " </button>\n", | |
| "\n", | |
| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
| " --bg-color: #E8F0FE;\n", | |
| " --fill-color: #1967D2;\n", | |
| " --hover-bg-color: #E2EBFA;\n", | |
| " --hover-fill-color: #174EA6;\n", | |
| " --disabled-fill-color: #AAA;\n", | |
| " --disabled-bg-color: #DDD;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-quickchart {\n", | |
| " --bg-color: #3B4455;\n", | |
| " --fill-color: #D2E3FC;\n", | |
| " --hover-bg-color: #434B5C;\n", | |
| " --hover-fill-color: #FFFFFF;\n", | |
| " --disabled-bg-color: #3B4455;\n", | |
| " --disabled-fill-color: #666;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-b9133a52-29e7-4f21-a1b1-8a0dcccf3c61 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 70 | |
| } | |
| ], | |
| "source": [ | |
| "url = \"https://gist.githubusercontent.com/aleksejalex/8c6f79713b6adab830b96974a9bf7aad/raw/965be061b72984f791ec8fee12a26e07d6a354ab/NAEX-bridge-CP.csv\"\n", | |
| "\n", | |
| "data = pd.read_csv(url)\n", | |
| "data.head(5)" | |
| ], | |
| "id": "EmCeQifPOeg0" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 71, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 300 | |
| }, | |
| "id": "aDeha_V4OeNE", | |
| "outputId": "10d03e25-e713-4571-d891-187e1f374e6d" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " height width layers folds length clamps \\\n", | |
| "count 40.0 40.000000 40.00000 40.000000 40.0 40.00000 \n", | |
| "mean 3.5 5.500000 1.50000 4.000000 24.5 0.50000 \n", | |
| "std 0.0 0.905822 0.50637 0.905822 0.0 0.50637 \n", | |
| "min 3.5 4.500000 1.00000 3.000000 24.5 0.00000 \n", | |
| "25% 3.5 4.500000 1.00000 3.000000 24.5 0.00000 \n", | |
| "50% 3.5 5.500000 1.50000 4.000000 24.5 0.50000 \n", | |
| "75% 3.5 6.500000 2.00000 5.000000 24.5 1.00000 \n", | |
| "max 3.5 6.500000 2.00000 5.000000 24.5 1.00000 \n", | |
| "\n", | |
| " weight_distrib max_num_of_batteries support_length exp_day \n", | |
| "count 40.00000 40.000000 40.0 40.000000 \n", | |
| "mean 0.50000 16.875000 2.5 1.400000 \n", | |
| "std 0.50637 11.699633 0.0 0.496139 \n", | |
| "min 0.00000 2.000000 2.5 1.000000 \n", | |
| "25% 0.00000 8.000000 2.5 1.000000 \n", | |
| "50% 0.50000 13.500000 2.5 1.000000 \n", | |
| "75% 1.00000 25.000000 2.5 2.000000 \n", | |
| "max 1.00000 48.000000 2.5 2.000000 " | |
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| " <div>\n", | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>height</th>\n", | |
| " <th>width</th>\n", | |
| " <th>layers</th>\n", | |
| " <th>folds</th>\n", | |
| " <th>length</th>\n", | |
| " <th>clamps</th>\n", | |
| " <th>weight_distrib</th>\n", | |
| " <th>max_num_of_batteries</th>\n", | |
| " <th>support_length</th>\n", | |
| " <th>exp_day</th>\n", | |
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| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>40.0</td>\n", | |
| " <td>40.000000</td>\n", | |
| " <td>40.00000</td>\n", | |
| " <td>40.000000</td>\n", | |
| " <td>40.0</td>\n", | |
| " <td>40.00000</td>\n", | |
| " <td>40.00000</td>\n", | |
| " <td>40.000000</td>\n", | |
| " <td>40.0</td>\n", | |
| " <td>40.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>3.5</td>\n", | |
| " <td>5.500000</td>\n", | |
| " <td>1.50000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>24.5</td>\n", | |
| " <td>0.50000</td>\n", | |
| " <td>0.50000</td>\n", | |
| " <td>16.875000</td>\n", | |
| " <td>2.5</td>\n", | |
| " <td>1.400000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.905822</td>\n", | |
| " <td>0.50637</td>\n", | |
| " <td>0.905822</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.50637</td>\n", | |
| " <td>0.50637</td>\n", | |
| " <td>11.699633</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.496139</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>3.5</td>\n", | |
| " <td>4.500000</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>24.5</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>2.5</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>3.5</td>\n", | |
| " <td>4.500000</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>24.5</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>0.00000</td>\n", | |
| " <td>8.000000</td>\n", | |
| " <td>2.5</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>3.5</td>\n", | |
| " <td>5.500000</td>\n", | |
| " <td>1.50000</td>\n", | |
| " <td>4.000000</td>\n", | |
| " <td>24.5</td>\n", | |
| " <td>0.50000</td>\n", | |
| " <td>0.50000</td>\n", | |
| " <td>13.500000</td>\n", | |
| " <td>2.5</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>3.5</td>\n", | |
| " <td>6.500000</td>\n", | |
| " <td>2.00000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>24.5</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>2.5</td>\n", | |
| " <td>2.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>3.5</td>\n", | |
| " <td>6.500000</td>\n", | |
| " <td>2.00000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>24.5</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>1.00000</td>\n", | |
| " <td>48.000000</td>\n", | |
| " <td>2.5</td>\n", | |
| " <td>2.000000</td>\n", | |
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| " <div class=\"colab-df-buttons\">\n", | |
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| " border: none;\n", | |
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| " .colab-df-convert:hover {\n", | |
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| " .colab-df-buttons div {\n", | |
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| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
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| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
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| " <script>\n", | |
| " const buttonEl =\n", | |
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| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-8f79080f-7396-486f-95e2-179e95d423e7');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
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| "<div id=\"df-74bffc6f-9125-476d-b552-fb08941f9049\">\n", | |
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| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
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| " }\n", | |
| "\n", | |
| " .colab-df-quickchart {\n", | |
| " background-color: var(--bg-color);\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: var(--fill-color);\n", | |
| " height: 32px;\n", | |
| " padding: 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart:hover {\n", | |
| " background-color: var(--hover-bg-color);\n", | |
| " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: var(--button-hover-fill-color);\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-quickchart-complete:disabled,\n", | |
| " .colab-df-quickchart-complete:disabled:hover {\n", | |
| " background-color: var(--disabled-bg-color);\n", | |
| " fill: var(--disabled-fill-color);\n", | |
| " box-shadow: none;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-spinner {\n", | |
| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
| " }\n", | |
| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " }\n", | |
| " 20% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 30% {\n", | |
| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " }\n", | |
| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " 90% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " }\n", | |
| " }\n", | |
| "</style>\n", | |
| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-74bffc6f-9125-476d-b552-fb08941f9049 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| " })();\n", | |
| " </script>\n", | |
| "</div>\n", | |
| " </div>\n", | |
| " </div>\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 71 | |
| } | |
| ], | |
| "source": [ | |
| "data.describe()" | |
| ], | |
| "id": "aDeha_V4OeNE" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 72, | |
| "metadata": { | |
| "id": "PbGFISdaOd6K" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def factorize(df, variable_name):\n", | |
| " \"\"\"\n", | |
| " this function taks dataframe and a name of var that has only 2 or 3 values.\n", | |
| " It sets those values to {-1, 1} for 2-valued vars and to {-1, 0, 1} for 3-valued vars.\n", | |
| " \"\"\"\n", | |
| " unique_values = df[variable_name].unique() # Check the number of unique values in the variable\n", | |
| " num_unique_values = len(unique_values)\n", | |
| "\n", | |
| " if num_unique_values == 2:\n", | |
| " # Assign -1 to the lower value and +1 to the higher value\n", | |
| " lower_value, higher_value = unique_values\n", | |
| " df[variable_name] = df[variable_name].apply(lambda x: -1 if x == lower_value else 1)\n", | |
| "\n", | |
| " elif num_unique_values == 3:\n", | |
| " # Assign -1 to the lowest value, 0 to the middle value, and +1 to the highest value\n", | |
| " sorted_values = sorted(unique_values)\n", | |
| " df[variable_name] = df[variable_name].apply(lambda x: sorted_values.index(x) - 1)\n", | |
| "\n", | |
| " else:\n", | |
| " print(f\"The variable '{variable_name}' does not have exactly two or three unique values.\")\n", | |
| "\n", | |
| " return df" | |
| ], | |
| "id": "PbGFISdaOd6K" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 73, | |
| "metadata": { | |
| "id": "Ljp6TGEdOdnu" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "df = data.copy()\n", | |
| "#print(\"Original DataFrame:\")\n", | |
| "#print(df)\n", | |
| "\n", | |
| "df2 = factorize(df, 'folds')\n", | |
| "df2 = factorize(df2, 'width')\n", | |
| "df2 = factorize(df2, 'layers')\n", | |
| "df2 = factorize(df2, 'clamps')\n", | |
| "df2 = factorize(df2, 'weight_distrib')\n", | |
| "\n", | |
| "#print(\"\\nDataFrame after factorization:\")\n", | |
| "#print(df2)\n", | |
| "data2= df2.copy()" | |
| ], | |
| "id": "Ljp6TGEdOdnu" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 74, | |
| "metadata": { | |
| "id": "rtr1_Rf4OdVM" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "data2.drop(\"height\", axis=1, inplace=True)\n", | |
| "data2.drop(\"length\", axis=1, inplace=True)\n", | |
| "data2.drop(\"support_length\", axis=1, inplace=True)\n", | |
| "data2.drop(\"exp_day\", axis=1, inplace=True)" | |
| ], | |
| "id": "rtr1_Rf4OdVM" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 75, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "id": "r2Q1SaW3OtBZ", | |
| "outputId": "abac8475-d548-4187-d114-942aaf23040f" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " width layers folds clamps weight_distrib max_num_of_batteries\n", | |
| "0 -1 -1 1 -1 -1 8\n", | |
| "1 -1 -1 1 -1 1 8\n", | |
| "2 -1 -1 1 1 1 8\n", | |
| "3 -1 -1 1 1 -1 4\n", | |
| "4 -1 1 1 -1 1 25\n", | |
| "5 -1 1 1 -1 -1 16\n", | |
| "6 -1 1 1 1 1 28\n", | |
| "7 -1 1 1 1 -1 13\n", | |
| "8 -1 -1 -1 -1 1 26\n", | |
| "9 -1 -1 -1 -1 -1 6\n", | |
| "10 -1 -1 -1 1 1 12\n", | |
| "11 -1 -1 -1 1 -1 2\n", | |
| "12 -1 1 -1 -1 1 36\n", | |
| "13 -1 1 -1 -1 -1 25\n", | |
| "14 -1 1 -1 1 1 48\n", | |
| "15 -1 1 -1 1 -1 17\n", | |
| "16 1 1 1 -1 1 36\n", | |
| "17 1 1 1 -1 -1 15\n", | |
| "18 1 1 1 1 1 24\n", | |
| "19 1 1 1 1 -1 6\n", | |
| "20 1 -1 1 -1 1 22\n", | |
| "21 1 -1 1 -1 -1 5\n", | |
| "22 1 -1 1 1 1 10\n", | |
| "23 1 -1 1 1 -1 5\n", | |
| "24 1 1 -1 -1 1 45\n", | |
| "25 1 1 -1 -1 -1 13\n", | |
| "26 1 1 -1 1 1 18\n", | |
| "27 1 1 -1 1 -1 9\n", | |
| "28 1 -1 -1 -1 1 26\n", | |
| "29 1 -1 -1 -1 -1 8\n", | |
| "30 1 -1 -1 1 1 9\n", | |
| "31 1 -1 -1 1 -1 2\n", | |
| "32 0 -1 0 -1 -1 6\n", | |
| "33 0 -1 0 -1 1 20\n", | |
| "34 0 -1 0 1 -1 6\n", | |
| "35 0 -1 0 1 1 14\n", | |
| "36 0 1 0 -1 -1 20\n", | |
| "37 0 1 0 -1 1 27\n", | |
| "38 0 1 0 1 -1 13\n", | |
| "39 0 1 0 1 1 34" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-20b0db22-150f-4259-9855-0e97c9d0518f\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>width</th>\n", | |
| " <th>layers</th>\n", | |
| " <th>folds</th>\n", | |
| " <th>clamps</th>\n", | |
| " <th>weight_distrib</th>\n", | |
| " <th>max_num_of_batteries</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
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| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>25</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>16</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>28</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>13</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>26</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>6</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>12</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>36</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>25</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>48</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15</th>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>17</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>16</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>36</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>15</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>18</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>24</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>19</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>6</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>20</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>22</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>21</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>22</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>10</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>23</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>5</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>24</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>45</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>13</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>26</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>18</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>27</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>9</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>28</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>26</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>29</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>8</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>30</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>9</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>31</th>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>-1</td>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>32</th>\n", | |
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| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 75 | |
| } | |
| ], | |
| "source": [ | |
| "data2" | |
| ], | |
| "id": "r2Q1SaW3OtBZ" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 76, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "U69DN5EWOsZJ", | |
| "outputId": "278e8ad4-9daf-4ad7-8b41-854a6fc62afb" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " OLS Regression Results \n", | |
| "================================================================================\n", | |
| "Dep. Variable: max_num_of_batteries R-squared: 0.806\n", | |
| "Model: OLS Adj. R-squared: 0.764\n", | |
| "Method: Least Squares F-statistic: 19.05\n", | |
| "Date: Mon, 04 Dec 2023 Prob (F-statistic): 9.32e-10\n", | |
| "Time: 21:41:45 Log-Likelihood: -121.78\n", | |
| "No. Observations: 40 AIC: 259.6\n", | |
| "Df Residuals: 32 BIC: 273.1\n", | |
| "Df Model: 7 \n", | |
| "Covariance Type: nonrobust \n", | |
| "=========================================================================================\n", | |
| " coef std err t P>|t| [0.025 0.975]\n", | |
| "-----------------------------------------------------------------------------------------\n", | |
| "Intercept 16.8750 0.898 18.785 0.000 15.045 18.705\n", | |
| "width -0.9062 1.004 -0.902 0.374 -2.952 1.140\n", | |
| "layers 6.5250 0.898 7.263 0.000 4.695 8.355\n", | |
| "folds -2.1563 1.004 -2.147 0.039 -4.202 -0.110\n", | |
| "clamps -2.7750 0.898 -3.089 0.004 -4.605 -0.945\n", | |
| "weight_distrib 6.9250 0.898 7.709 0.000 5.095 8.755\n", | |
| "weight_distrib:folds -1.5313 1.004 -1.525 0.137 -3.577 0.515\n", | |
| "layers:weight_distrib 1.7750 0.898 1.976 0.057 -0.055 3.605\n", | |
| "==============================================================================\n", | |
| "Omnibus: 2.286 Durbin-Watson: 1.587\n", | |
| "Prob(Omnibus): 0.319 Jarque-Bera (JB): 1.357\n", | |
| "Skew: -0.034 Prob(JB): 0.507\n", | |
| "Kurtosis: 3.900 Cond. No. 1.12\n", | |
| "==============================================================================\n", | |
| "\n", | |
| "Notes:\n", | |
| "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# OLS 1\n", | |
| "formula_temp = \"max_num_of_batteries ~ width + layers + folds + clamps + weight_distrib + weight_distrib:folds + layers:weight_distrib\"\n", | |
| "\n", | |
| "# fit\n", | |
| "model_temp = smf.ols(formula=formula_temp, data=data2).fit()\n", | |
| "print(model_temp.summary())" | |
| ], | |
| "id": "U69DN5EWOsZJ" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 77, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 489 | |
| }, | |
| "id": "VzhNdKInOr3x", | |
| "outputId": "e68cb01c-6a66-4e76-e5c7-76df59f7abca" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 840x480 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=(7,4), dpi=120)\n", | |
| "plt.subplot(1,2,1)\n", | |
| "plt.scatter(model_temp.fittedvalues, model_temp.resid)\n", | |
| "plt.xlabel('Fitted Values')\n", | |
| "plt.ylabel('Residuals')\n", | |
| "plt.title(\"Residual plot\")\n", | |
| "plt.axhline(y=0, color='r', linestyle='--')\n", | |
| "ax = plt.subplot(1,2,2)\n", | |
| "stats.probplot(model_temp.resid, dist=\"norm\", plot = ax)\n", | |
| "plt.grid()\n", | |
| "\n", | |
| "plt.savefig(\"residuals_of_OLS.png\", dpi=300)\n", | |
| "plt.show()" | |
| ], | |
| "id": "VzhNdKInOr3x" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 78, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ovhKtGhUOdDg", | |
| "outputId": "e434d9f5-40f9-400e-ec65-e7c27bd95e1a" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "H0: variance of error in OLS model is constant.\n", | |
| "White Test Statistic: 35.055735285612016, p-value: 0.03822867049836392\n", | |
| "Can't reject H_0 (Have to accept H_0). \n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# White test (for heteroscedasticity)\n", | |
| "from statsmodels.stats.diagnostic import het_white\n", | |
| "\n", | |
| "print(f\"H0: variance of error in OLS model is constant.\")\n", | |
| "white_test_statistic, white_p_value, _, _ = het_white(model_temp.resid, model_temp.model.exog)\n", | |
| "print(f'White Test Statistic: {white_test_statistic}, p-value: {white_p_value}')\n", | |
| "\n", | |
| "alpha = 0.01 # significance level\n", | |
| "if white_p_value < alpha:\n", | |
| " # we dont have enough evidence to accept H_0\n", | |
| " print(\"Reject H_0 (Have to accept H_1). \")\n", | |
| "else:\n", | |
| " # significant evidence to reject H_0, i.e. to accept H_1\n", | |
| " print(\"Can't reject H_0 (Have to accept H_0). \")" | |
| ], | |
| "id": "ovhKtGhUOdDg" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 79, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "LTpUCGJFOc0D", | |
| "outputId": "295c848a-8a23-4252-caa8-5ad0b7dbdb38" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "H0: sample (residuals) came from normal distribution.\n", | |
| "Shapiro-Wilk Test: Statistic=0.9857189059257507, p-value=0.8855799436569214\n", | |
| "Can't reject H_0 (Have to accept H_0). \n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# Normality of residuals - Shapiro-Wilk test\n", | |
| "# (can be important especially in case small sample size)\n", | |
| "print(f\"H0: sample (residuals) came from normal distribution.\")\n", | |
| "from scipy.stats import shapiro\n", | |
| "stat, p_value = shapiro(model_temp.resid)\n", | |
| "print(f'Shapiro-Wilk Test: Statistic={stat}, p-value={p_value}')\n", | |
| "\n", | |
| "alpha = 0.05 # significance level\n", | |
| "if p_value < alpha:\n", | |
| " # we dont have enough evidence to accept H_0\n", | |
| " print(\"Reject H_0 (Have to accept H_1). \")\n", | |
| "else:\n", | |
| " # significant evidence to reject H_0, i.e. to accept H_1\n", | |
| " print(\"Can't reject H_0 (Have to accept H_0). \")" | |
| ], | |
| "id": "LTpUCGJFOc0D" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 80, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 237 | |
| }, | |
| "id": "aImOsPwoOcln", | |
| "outputId": "59a4fd50-2326-4b21-b81a-880cb4f72f94" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Variable VIF\n", | |
| "0 width 1.002082\n", | |
| "1 layers 1.134897\n", | |
| "2 folds 1.011785\n", | |
| "3 clamps 1.024399\n", | |
| "4 weight_distrib 1.151943\n", | |
| "5 max_num_of_batteries 1.325105" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-33615003-e368-4bf9-82c3-ece4761ffee7\" class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Variable</th>\n", | |
| " <th>VIF</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>width</td>\n", | |
| " <td>1.002082</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>layers</td>\n", | |
| " <td>1.134897</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>folds</td>\n", | |
| " <td>1.011785</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>clamps</td>\n", | |
| " <td>1.024399</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>weight_distrib</td>\n", | |
| " <td>1.151943</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>max_num_of_batteries</td>\n", | |
| " <td>1.325105</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <div class=\"colab-df-buttons\">\n", | |
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| " .colab-df-buttons div {\n", | |
| " margin-bottom: 4px;\n", | |
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| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
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| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
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| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-33615003-e368-4bf9-82c3-ece4761ffee7');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
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| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
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| "<style>\n", | |
| " .colab-df-quickchart {\n", | |
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| " --disabled-fill-color: #AAA;\n", | |
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| " [theme=dark] .colab-df-quickchart {\n", | |
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| " .colab-df-quickchart-complete:disabled,\n", | |
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| " background-color: var(--disabled-bg-color);\n", | |
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| " border: 2px solid var(--fill-color);\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " animation:\n", | |
| " spin 1s steps(1) infinite;\n", | |
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| "\n", | |
| " @keyframes spin {\n", | |
| " 0% {\n", | |
| " border-color: transparent;\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
| " border-left-color: var(--fill-color);\n", | |
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| " border-color: transparent;\n", | |
| " border-left-color: var(--fill-color);\n", | |
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| " border-left-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " border-right-color: var(--fill-color);\n", | |
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| " 40% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-top-color: var(--fill-color);\n", | |
| " }\n", | |
| " 60% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
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| " 80% {\n", | |
| " border-color: transparent;\n", | |
| " border-right-color: var(--fill-color);\n", | |
| " border-bottom-color: var(--fill-color);\n", | |
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| " 90% {\n", | |
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| " border-bottom-color: var(--fill-color);\n", | |
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| "\n", | |
| " <script>\n", | |
| " async function quickchart(key) {\n", | |
| " const quickchartButtonEl =\n", | |
| " document.querySelector('#' + key + ' button');\n", | |
| " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", | |
| " quickchartButtonEl.classList.add('colab-df-spinner');\n", | |
| " try {\n", | |
| " const charts = await google.colab.kernel.invokeFunction(\n", | |
| " 'suggestCharts', [key], {});\n", | |
| " } catch (error) {\n", | |
| " console.error('Error during call to suggestCharts:', error);\n", | |
| " }\n", | |
| " quickchartButtonEl.classList.remove('colab-df-spinner');\n", | |
| " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", | |
| " }\n", | |
| " (() => {\n", | |
| " let quickchartButtonEl =\n", | |
| " document.querySelector('#df-df789e19-95cf-480c-952e-bcdeab48c2b0 button');\n", | |
| " quickchartButtonEl.style.display =\n", | |
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| " </div>\n", | |
| " </div>\n" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 80 | |
| } | |
| ], | |
| "source": [ | |
| "# VIF\n", | |
| "# =is a measure that quantifies the extent of multicollinearity in a set of predictor variables within a regression model.\n", | |
| "# Multicollinearity occurs when two or more independent variables in a model are highly correlated,\n", | |
| "# making it difficult to isolate the individual effect of each variable on the dependent variable.\n", | |
| "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", | |
| "\n", | |
| "vif = pd.DataFrame()\n", | |
| "vif[\"Variable\"] = data2.columns\n", | |
| "vif[\"VIF\"] = [variance_inflation_factor(data2.values, i) for i in range(data2.shape[1])]\n", | |
| "#print(vif)\n", | |
| "vif" | |
| ], | |
| "id": "aImOsPwoOcln" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 81, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 36 | |
| }, | |
| "id": "601rourIOcW1", | |
| "outputId": "007571c0-74a8-4783-b106-89e192cc4196" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'=> we see almost no or very slight correlation between vars used in our model. '" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 81 | |
| } | |
| ], | |
| "source": [ | |
| "\"\"\"=> we see almost no or very slight correlation between vars used in our model. \"\"\"" | |
| ], | |
| "id": "601rourIOcW1" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 82, | |
| "metadata": { | |
| "id": "5aw6Y8lAPfks" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import pickle\n", | |
| "\n", | |
| "with open('statsmodels_ols_model.pkl', 'wb') as f:\n", | |
| " pickle.dump(model_temp, f)" | |
| ], | |
| "id": "5aw6Y8lAPfks" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 83, | |
| "metadata": { | |
| "id": "iplUDcXWPfTx" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%%R\n", | |
| "#if (!requireNamespace(\"reticulate\", quietly = TRUE)) install.packages(\"reticulate\")\n", | |
| "#library(reticulate)\n", | |
| "model <- reticulate::py_load_object(\"statsmodels_ols_model.pkl\")" | |
| ], | |
| "id": "iplUDcXWPfTx" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 84, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "cbjcUb8dPe6I", | |
| "outputId": "b363bf21-2b97-478c-c5f2-e36540aca4c3" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Intercept 16.87500\n", | |
| "width -0.90625\n", | |
| "layers 6.52500\n", | |
| "folds -2.15625\n", | |
| "clamps -2.77500\n", | |
| "weight_distrib 6.92500\n", | |
| "weight_distrib:folds -1.53125\n", | |
| "layers:weight_distrib 1.77500\n", | |
| "dtype: float64\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "# Access coefficients\n", | |
| "coefficients <- reticulate::py_get_attr(model, \"params\")\n", | |
| "print(coefficients)" | |
| ], | |
| "id": "cbjcUb8dPe6I" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 85, | |
| "metadata": { | |
| "id": "yVl0mTwyPepZ" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%%R\n", | |
| "coefficients <- reticulate::py_get_attr(model, \"params\")\n", | |
| "std_errors <- reticulate::py_get_attr(model, \"bse\")\n", | |
| "t_values <- reticulate::py_get_attr(model, \"tvalues\")\n", | |
| "p_values <- reticulate::py_get_attr(model, \"pvalues\")" | |
| ], | |
| "id": "yVl0mTwyPepZ" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 86, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "1KQa2V46PeP_", | |
| "outputId": "d48f01d9-3ed2-4daf-ab38-e7740148529e" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Intercept 16.87500\n", | |
| "width -0.90625\n", | |
| "layers 6.52500\n", | |
| "folds -2.15625\n", | |
| "clamps -2.77500\n", | |
| "weight_distrib 6.92500\n", | |
| "weight_distrib:folds -1.53125\n", | |
| "layers:weight_distrib 1.77500\n", | |
| "dtype: float64\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "coefficients" | |
| ], | |
| "id": "1KQa2V46PeP_" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 87, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "bRmAFOqxPd_X", | |
| "outputId": "9af358a0-4efd-4b7c-9fb0-1d0c856d5507" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "[1] -0.90625\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "coefficients$width" | |
| ], | |
| "id": "bRmAFOqxPd_X" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "FJB-a1d8YT9G" | |
| }, | |
| "source": [ | |
| "# 8. For two chosen numerical variables, create a contour plot to find the optimal values for maximizing bridge strength under the additional condition of 3 layers, minimal width 6cm and minimal length of 22 cm." | |
| ], | |
| "id": "FJB-a1d8YT9G" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 88, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "bzHGHhkLD5m8", | |
| "outputId": "04d7288b-7d12-4bc2-9fd3-205a737e0ad5" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| " width layers folds clamps weight_distrib exp_day max_num_of_batteries\n", | |
| "1 4.5 1 5 0 0 2 8\n", | |
| "2 4.5 1 5 0 1 1 8\n", | |
| "3 4.5 1 5 1 1 2 8\n", | |
| "4 4.5 1 5 1 0 1 4\n", | |
| "5 4.5 2 5 0 1 2 25\n", | |
| "6 4.5 2 5 0 0 1 16\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "data8 <- df %>% transmute(width = as.numeric(width),\n", | |
| " layers = as.numeric(layers),\n", | |
| " folds = as.numeric(folds),\n", | |
| " clamps = as.factor(clamps),\n", | |
| " weight_distrib = as.factor(weight_distrib),\n", | |
| " exp_day = as.factor(exp_day),\n", | |
| " max_num_of_batteries = as.numeric(max_num_of_batteries))\n", | |
| "\n", | |
| "head(data8)" | |
| ], | |
| "id": "bzHGHhkLD5m8" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 89, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "GLchhkt_g6dk", | |
| "outputId": "6ba3d624-d2c7-4239-a818-34983bf49cbc" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "\n", | |
| "Call:\n", | |
| "lm.default(formula = max_num_of_batteries ~ width * layers, data = data8)\n", | |
| "\n", | |
| "Residuals:\n", | |
| " Min 1Q Median 3Q Max \n", | |
| "-14.750 -6.219 -1.562 2.875 24.250 \n", | |
| "\n", | |
| "Coefficients:\n", | |
| " Estimate Std. Error t value Pr(>|t|)\n", | |
| "(Intercept) -26.625 33.008 -0.807 0.427\n", | |
| "width 4.250 5.905 0.720 0.478\n", | |
| "layers 32.219 20.876 1.543 0.134\n", | |
| "width:layers -3.437 3.734 -0.920 0.365\n", | |
| "\n", | |
| "Residual standard error: 10.56 on 28 degrees of freedom\n", | |
| "Multiple R-squared: 0.33,\tAdjusted R-squared: 0.2582 \n", | |
| "F-statistic: 4.597 on 3 and 28 DF, p-value: 0.009731\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "ctrlm<-lm(max_num_of_batteries~width*layers, data=data8)\n", | |
| "summary(ctrlm)" | |
| ], | |
| "id": "GLchhkt_g6dk" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 90, | |
| "metadata": { | |
| "id": "UWIdh44bh4K0" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%%R\n", | |
| "rg <- list(width=seq(3.5,7.5,by=.05),layers=seq(1,5,by=1),data=data8)\n", | |
| "new8 <- expand.grid(rg)\n", | |
| "new8$max_num_of_batteries <- predict(ctrlm,new8)" | |
| ], | |
| "id": "UWIdh44bh4K0" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 91, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 497 | |
| }, | |
| "id": "QfjLgrT9h4Eb", | |
| "outputId": "33f0abde-6384-4680-ba4f-76bff145e825" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "contourplot(max_num_of_batteries ~ width*layers, new8, region = TRUE, xlab=\"A Width\",ylab=\"B Layers\",main=\"Contour plot of Maximal Number of Batteries\")\n", | |
| "#points(, col=\"red\", pch=17)\n" | |
| ], | |
| "id": "QfjLgrT9h4Eb" | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "p01abBHBtBTI" | |
| }, | |
| "source": [ | |
| "The contour plot was constructed for continuous variables width an layers affecting maximal number of batteries. We can notice, that for a width 6cm and 3 layers of paper is a weight capacity of a bridge in region around 50 batteries. Our Bridge constructors did not change the length of a bridge, so that condition is omitted." | |
| ], | |
| "id": "p01abBHBtBTI" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 92, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "CFW7Wh8UlfPu", | |
| "outputId": "25f56481-a39b-4c84-85d2-e77d70f89e45" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "NULL\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "#install.packages(\"rsm\")\n", | |
| "#library(\"rsm\")" | |
| ], | |
| "id": "CFW7Wh8UlfPu" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 93, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "znhCkEvingnK", | |
| "outputId": "d3d4b0af-fce1-4362-97df-f4e50664d1f9" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "NULL\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "#install.packages(\"pid\")\n", | |
| "#library(\"pid\")" | |
| ], | |
| "id": "znhCkEvingnK" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 94, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 977 | |
| }, | |
| "id": "GmIg-sXHh4AL", | |
| "outputId": "e5e17269-88e3-4d75-8824-d49d1230e17f" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "contourPlot(ctrlm,\n", | |
| " xlab=\"width\",ylab=\"layers\",\n", | |
| " main=\"Contour plot of Maz number of batteries\",\n", | |
| " xlim=c(3.5, 7.5),\n", | |
| " ylim=c(1, 5),\n", | |
| " colour.function=terrain.colors)" | |
| ], | |
| "id": "GmIg-sXHh4AL" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 95, | |
| "metadata": { | |
| "id": "PJce5P5Kla4u" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%%R\n", | |
| "rsm_bt<-rsm(max_num_of_batteries~FO(width,layers)+TWI(width,layers), data=new8)" | |
| ], | |
| "id": "PJce5P5Kla4u" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 96, | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 497 | |
| }, | |
| "id": "Md-9Rc6FlbkS", | |
| "outputId": "634a406e-c38f-4ce5-9b01-d2fc0c39adde" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "%%R\n", | |
| "persp(rsm_bt, width~layers, zlab = \"max_num_of_batteries\", main=\"Response surface\")" | |
| ], | |
| "id": "Md-9Rc6FlbkS" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 96, | |
| "metadata": { | |
| "id": "iYnC_5X-RDvc" | |
| }, | |
| "outputs": [], | |
| "source": [], | |
| "id": "iYnC_5X-RDvc" | |
| } | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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