VOTING IN US ELECTION ANALYSIS

Voting in US election analysis



Voting in US election analysis

Introduction

The following section is divided into two parts - part A and part B. Part A explains the results and interpretation of US elections 2008 data and provides a detailed analysis with the supporting tables generated through SPSS and formatted in MS Excel 2007. Moreover, Part B entails the qualitative research methods to be (i) more robust and have (ii) greater explanatory power.

Part A

Part A shows the analysis of the provided data of US elections 2008. The results of the tables implies that growth and constant have significant impact upon the voting behavior. On the other hand, inflation does not have statistical significant impact on the voting behavior. Based on the analysis, it can be concluded that economic activities have a significant impact on the voting behavior of residents. The details of the results are given below

The box beneath is the standard SPSS regression output. For multiple linear regression, this former box is not of much interest, because it merely lists the single variable we are consuming as a predictor. Later for multiple regression this shall appear everybody the variables within our models and it can also appear the puts of variables for numerous models at once.

 

Table 1: Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

inflation, growtha

.

Enter

a. All requested variables entered.

 

b. Dependent Variable: vote

 

The model summary box arrives next. In it one shall encounter R, R2 and the standard error of estimate, which is the square root of the mean squared error, or MSE. Here we interpret R as the correlation of the Y scores with the predicted values.

The adjusted R2 is consumed within multiple regression. It is adjusted towards allowing for the consume of many predictors - merely adding many Xs can elevate one's R2, so this importance is adjusted downwards a little towards penalize for just “hunting around” for notable predictors.

Table 2:Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.721a

0.52

0.474

4.90786

2.503

a. Predictors: (Constant), inflation, growth

b. Dependent Variable: vote

Note that within the footnote the model summary SPSS it tells what predictors are relevant for the R and R2, even though within this instance we have two predictor. If we were to operate multiple linear regressions, we would get a list of numerous tables.

The word “constant” within parentheses refers towards the intercept. This is printed because it is possible towards drag SPSS not towards estimate an intercept. This is alone done within exotic situations - for most regressions, and everybody of the regressions we shall operate, we shall allow SPSS towards estimate the intercept term.

The box beneath displays the “ANOVA table” for the regression analysis. ANOVA stands for Analysis Of Variance - specifically the analysis of variation within the Y scores. Here we see the two sums of squares introduced within class - the regression and residual (or error) sums of squares. The variance of the residuals (or errors) is the importance of the mean square error or MSE—here it is 24.087.

 When compare the importance of the MSE towards ...