library(readxl)
library(tidyverse)
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library(ggplot2)
RLS<-read.csv("RLS Public Use File Feb.csv")
RLS_Example<-lm(RLS$QB2C~RLS$RELTRAD)
summary(RLS_Example)
## 
## Call:
## lm(formula = RLS$QB2C ~ RLS$RELTRAD)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -2.735 -2.730 -1.734 -1.687 95.702 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.735e+00  8.069e-02   46.29   <2e-16 ***
## RLS$RELTRAD -4.853e-07  7.240e-07   -0.67    0.503    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.04 on 36906 degrees of freedom
## Multiple R-squared:  1.218e-05,  Adjusted R-squared:  -1.492e-05 
## F-statistic: 0.4494 on 1 and 36906 DF,  p-value: 0.5026

In this above data set we see that the depdent variable QB2C, which is support for environmentalist polcies, and the indepedent variable RELTRAD, the religious tradition that a person identifies with, have a p-value 0.50, which means there is a standard devation of 50. The Estimated realtion is -4.853e-07, which also shows a large number with a negative correlation. From the above data, the results of this linear model show that the correlation between religious tradition and support for environmental related policy is not strong.

plot(RLS_Example, which=1)

This do not meet the linear regression model at all. This chart would assume that there is no correlation between the two variables.

RLS_Example2<-lm(QB2C~RELTRAD+RELIMP+RELPER, data=RLS)
summary(RLS_Example2)
## 
## Call:
## lm(formula = QB2C ~ RELTRAD + RELIMP + RELPER, data = RLS)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -13.434  -2.571  -1.755  -1.643  96.778 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.462e+00  8.646e-02  40.038  < 2e-16 ***
## RELTRAD     -1.500e-06  7.330e-07  -2.047   0.0407 *  
## RELIMP       6.821e-02  1.095e-02   6.230 4.70e-10 ***
## RELPER       4.263e-02  8.508e-03   5.010 5.45e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.02 on 36904 degrees of freedom
## Multiple R-squared:  0.002081,   Adjusted R-squared:  0.002 
## F-statistic: 25.66 on 3 and 36904 DF,  p-value: < 2.2e-16

The above chart shows that RELTRAD, one’s religious tradition has a low correlation while RELIMP & RELPER, which represent levels of religiosity show high correlation to the dependent variable in question. Overall, the p-value for the entire model shows high accuracy since it is 2.23-16.

plot(RLS_Example2, which=1)

The line above does showe a correlation between two of the variables, RELIMP & RELPER.