Linear Regression with R, on IQ for Gini and Linguistic Diversity

Reusing the code posted for Correlations within with Hofstede's Cultural Values, Diversity, GINI, and IQ, the same data can be used for linear regression:

Example Code

 
 # LM - Linear Regression  
 
 # Load the data into a matrix  
 oecdData <- read.table("OECD - Quality of Life.csv", header = TRUE, sep = ",")  
 
 # Access the vectors  
 v1 <- oecdData$IQ  
 v2 <- oecdData$HofstederPowerDx  
 v3 <- oecdData$HofstederMasculinity  
 v4 <- oecdData$HofstederIndividuality  
 v5 <- oecdData$HofstederUncertaintyAvoidance  
 v6 <- oecdData$Diversity_Ethnic  
 v7 <- oecdData$Diversity_Linguistic  
 v8 <- oecdData$Diversity_Religious  
 v9 <- oecdData$Gini  
 
 # IQ ~ Gini  
 relation1 <- lm(v1 ~ v9)  
 print(relation1)  
 print(summary(relation1))  

 # IQ ~ Diversity_Linguistic  
 relation2 <- lm(v1 ~ v7)  
 print(relation2)  
 print(summary(relation2))  

Example Results

 > # Access the vectors   
 + v1 <- oecdData$IQ  
 + v7 <- oecdData$Diversity_Linguistic  
 + v9 <- oecdData$Gini  
 +   
 + # IQ ~ Gini   
 + relation1 <- lm(v1 ~ v9)  
 + print(relation1)  
 + print(summary(relation1))  
 +   
 + # IQ ~ Diversity_Linguistic   
 + relation2 <- lm(v1 ~ v7)  
 + print(relation2)  
 + print(summary(relation2))  
 Call:  

 lm(formula = v1 ~ v9)  
 Coefficients:  
 (Intercept)      v9   
   107.1884   -0.2487   

 Call:  

 lm(formula = v1 ~ v9)  

 Residuals:  
   Min   1Q       Median   3Q   Max   
 -6.3842 -2.4489 -0.0381 1.9954 6.6707   

 Coefficients:  
          Estimate Std. Error  t value Pr(>|t|)    
 (Intercept) 107.1884   4.7379 22.624  1.01e-15 ***  
 v9          -0.2487    0.1472 -1.689  0.107    
 ---  
 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
 Residual standard error: 3.27 on 20 degrees of freedom  
  (3 observations deleted due to missingness)  
 Multiple R-squared: 0.1248,     Adjusted R-squared: 0.08109   
 F-statistic: 2.853 on 1 and 20 DF, p-value: 0.1067  

 Call:  

 lm(formula = v1 ~ v7)  

 Coefficients:  
 (Intercept)      v7   
   99.0895    0.8328   

 Call:  
 lm(formula = v1 ~ v7)  

 Residuals:  
   Min   1Q      Median   3Q   Max   
 -7.1145 -1.5080 0.0149 2.3003 6.9105
   
 Coefficients:  
          Estimate Std. Error t value Pr(>|t|)    
 (Intercept) 99.0895   1.1035 89.793  <2e-16 ***  
 v7           0.8328   3.7034  0.225  0.824    
 ---  
 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  
 
 Residual standard error: 3.491 on 20 degrees of freedom  
  (3 observations deleted due to missingness)  
 Multiple R-squared: 0.002522,     Adjusted R-squared: -0.04735   
 F-statistic: 0.05056 on 1 and 20 DF, p-value: 0.8244  

Example Plot

Sample Data