aschleg

# Simple function for fitting a regression line with two predictor variables.
# Function takes two arguments:
#   x must be a matrix or dataframe with only two columns (variables)
#   y must be a vector containing the response values of the same length
# Output is similar to the lm() function
# Function used as a demonstration in post on multiple regression here: http://wp.me/p4aZEo-5Lh

two.predictor.regression <- function(x, y) {
  y <- as.matrix(y)
  if (ncol(y) > 1) {

aschleg

# Function for calculating the confidence (mean) and prediction intervals of a fitted linear regression model.
# Replicates the predict() function with arguments 'confidence' and 'prediction'
# Used in post demonstrating the confidence and prediction intervals in linear regression: http://www.aaronschlegel.com/notebook/confidence-prediction-intervals/

# Takes three arguments:
#   x = vector of independent variable data points
#   y = vector of dependent variable data points
#   pred.x = value of x to find predicted y

conf.pred.intervals <- function(x, y, pred.x) {

aschleg

# Quick function for performing linear regression with only one predictor variable.
# Takes two arguments, x and y. Variables must have same length.
# Output will be similar to output of lm() with one predictor.
# Gist created as part of post on linear regression:  http://www.aaronschlegel.com/notebook/simple-linear-regression-models-r/

simple.linear.coef <- function(x, y) {
  # Find length of x to get sample size. Assuming x and y have the same sample size.
  n <- length(x)
  
  if (n != length(y))

aschleg

games.howell <- function(grp, obs) {
  
  #Create combinations
  combs <- combn(unique(grp), 2)
  
  # Statistics that will be used throughout the calculations:
  # n = sample size of each group
  # groups = number of groups in data
  # Mean = means of each group sample
  # std = variance of each group sample

aschleg

library(readr)
library(caret)

# Enter csv file path. If the data is in xls or xlsx, you will need to use read.table or the read_table function
# in the readr package
data <- read_csv("") 
attach(data)
summary(data)

#Enter column name with categorical variables you want to model and predict between the empty quotations.

aschleg

from xlwings import Workbook, Range, Sheet
import pandas as pd
import os

# Alternative method to split an Excel worksheet into multiple sheets based on a column name.
# The script will prompt four questions to enter in the required information. The workbook will then open and
# split the prompted worksheet into separate worksheets based on the desired column name.
# To run, open the command prompt and enter the command python Split_Excel_Worksheet_v2.py

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

aschleg

from xlwings import Workbook, Range, Sheet
import pandas as pd

# Split Excel data in one worksheet into multiple worksheets based on column name.

# Copy this file into the same location as the Excel workbook with the worksheet you wish to split.
# Download the zip of the xlwings Github repo here: https://github.com/ZoomerAnalytics/xlwings and copy the
# xlwings.bas file from the xlwings folder. Import the xlwings.bas file into your Excel workbook by entering ALT+F11
# and then going to File, Import File and clicking on the file.
# Import the Split_Excel_Worksheet.bas file and run by going to the Developer tab on the Excel Ribbon, click Macros,

aschleg

from xlwings import Workbook, Range
import pandas as pd
import os
import re

# Script to merge a folder containing Excel workbooks into a single workbook.
# The folder should only contain Excel workbooks and must all either be in csv, xls or xlsx format
# To run, open the command prompt and enter the command python Merge_Excel_Workbooks.py

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

aschleg

library(caret)

ginzberg <- read.csv('D:/Google_Drive/Google_Drive/Resources/Datasets/Rdata/car/Ginzberg.csv')
ginzberg <- ginzberg[2:4]

train <- createDataPartition(y = ginzberg$depression,
                                p = 0.50,
                                list = FALSE)

training <- ginzberg[ train,]

aschleg

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