rudeboybert

# Multiple linear regression

## Regression table

Instructions:

- Indicate which model you selected: parallel slopes or interaction.
- Fit your selected regression model and display the regression table in the code 
block below
- Modify the template formula below from ModernDive 6.1.2 to match your model (this formula is written in the LaTeX typesetting language for printing mathematical formulas)

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REMIX EXAMPLE PROJECT

Remix example project is present when Remix loads very first time or there are no files existing in the File Explorer. 
It contains 3 directories:

1. 'contracts': Holds three contracts with different complexity level, denoted with number prefix in file name.
2. 'scripts': Holds two scripts to deploy a contract. It is explained below.
3. 'tests': Contains one test file for 'Ballot' contract with unit tests in Solidity.

SCRIPTS

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# Based on data from "House Prices: Advanced Regression Techniques" Kaggle Competition
# https://www.kaggle.com/c/house-prices-advanced-regression-techniques
# YouTube demo can be found here: 

library(tidyverse)
library(rpart)
library(Metrics)

# Reload house prices data
train <- read_csv("https://rudeboybert.github.io/SDS293/static/train.csv")

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/* Show all databases in on scidb.smith.edu server. */
SHOW DATABASES;

/* Load the imdb database. */
USE imdb;

/* Show the names of all tables in imdb. */
SHOW TABLES;

/* 

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library(dplyr)

# Create two example data frames:
dfA <- tibble(
  ID = c(76, 79, 9),
  age = c(21, 23, 24)
)
dfB <- tibble(
  ID = c(79, 76, 11),
  state = c("MA", "OR", "DE")

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library(ggplot2)
library(dplyr)
library(lubridate)
library(ical)

# For a screencast demo on creating a calendar and exporting it to .ics file format
# see this video: https://youtu.be/vLlR4lBWAoc

# Locate your .ics calendar file on your computer and change what's in quotation marks
calendar_data <- "192.ics" %>% 

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library(tidyverse)
# Install version 0.2.0.9000 or higher
# devtools::install_github("moderndive/moderndive")
library(moderndive)

# Chapter 8: "Virtual" sampling from a bowl of red/white balls
# Population:
bowl
# Sample from population:
bowl %>% 

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#------------------------------------------------------------------------------
# Lec21: 2019/04/22
#------------------------------------------------------------------------------
library(tidyverse)
library(glmnet)
library(broom)
library(modelr)
library(ISLR)

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#------------------------------------------------------------------------------
# Lec14: 2019/03/25
#------------------------------------------------------------------------------
library(tidyverse)
# Pre-process iris dataset
iris <- iris %>%
  # Convert to tibble data frame:
  as_tibble() %>%
  # Add identification variable to uniquely identify each row:
  rownames_to_column(var="ID")

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library(tidyverse)
library(moderndive)

# Convert to tibble:
mtcars <- mtcars %>% 
  rownames_to_column()

# Fit lm
mpg_model <- lm(mpg ~ hp, data = mtcars)