Homework 7

library(tidyverse)

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library(lmtest)

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library(MASS)

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library(dplyr)
RLS_Model<-read_csv("RLS Public Use File Feb.csv")

## Rows: 36908 Columns: 654
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## dbl (631): P_SUID, YEAR_FROM_CODATE, HAPPY, SATIS_A, SATIS_B, CHNG_A, CHNG_B...
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1. Linear Model

RLS_Model_2<-lm(QB2C~RELTRAD+RELIMP+IDEO, data=RLS_Model)
plot(RLS_Model_2, which=1)

raintest(RLS_Model_2)

## 
##  Rainbow test
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## data:  RLS_Model_2
## Rain = 0.90164, df1 = 18454, df2 = 18450, p-value = 1

2. Independence of Errors

library(car)

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durbinWatsonTest(RLS_Model_2)

##  lag Autocorrelation D-W Statistic p-value
##    1    -0.001060806      2.002121   0.892
##  Alternative hypothesis: rho != 0

3. Homoscedasticity

bptest(RLS_Model_2)

## 
##  studentized Breusch-Pagan test
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## data:  RLS_Model_2
## BP = 368.86, df = 3, p-value < 2.2e-16

RLS_Model_3<-lm(QB2C~RELTRAD+RELIMP+IDEO, data=RLS_Model)

plot(RLS_Model_3, which=3)

bptest(RLS_Model_3)

## 
##  studentized Breusch-Pagan test
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## data:  RLS_Model_3
## BP = 368.86, df = 3, p-value < 2.2e-16

4. Normality of Residuals

plot(RLS_Model_2, which=2)

plot(RLS_Model_3, which=2)

5. No Multicolinearity

RLS_Model_4<-lm(QB2C~RELTRAD+RELIMP+IDEO+RELPER+MEMB, data=RLS_Model)

vif(RLS_Model_4)

##  RELTRAD   RELIMP     IDEO   RELPER     MEMB 
## 1.031135 1.131340 1.007328 1.052644 1.104573

I think that my models meet all the assumptions. I need to filter out the “99” placeholder for n/a resutls to clear out the charts and make them “prettier” and more readable for the viewer. But all my models/charts seem to follow the expected pathways without violating any of the assumptions. I was also extremely happy wit the low amounts of mulitcolinearity wich helps me show my variables are good and not “over-related” to each other in a correlation sense. This means that the data should not be skewed overall from the variables I have selected in the aboce data set.

As a note the Shapiro scale would not work on my dataset due to numbers being over 5000. It had to be removed to knit the file.