Regression Analysis of Community Satisfaction Index (CSI) (Case Study Sample)
The provided assignment involves the analysis of a regression model used to predict the community satisfaction index (CSI) based on various factors such as police, local transportation, parks, garbage collection, income, and age. The regression model exhibits a strong positive correlation between the CSI and the explanatory variables.source..
The Case Study Problem
The purpose of the regression model in this case is to predict the community satisfaction index (CSI) based on several factors, such as police, local transportation, parks, garbage collection, income, and age. The statistics of the regression model indicate a strong positive correlation between the CSI and the explanatory variables, with an R value of 0.900362201. The R Square value of 0.810652093 shows that about 81% of the variance in the CSI can be explained by the explanatory variables.
The Adjusted R Square value of 0.723260751, adjusted for the number of explanatory variables, indicates that approximately 72% of the variance in the CSI can be explained by the variables. The Standard Error value of 1.211682525 represents the average deviation of observed CSI scores from the predicted scores.
The ANOVA table shows that the Sum of Squares (SS) value of 81.71373096 represents the total variance in the CSI scores. The Mean Squares (MS) value of 13.61895516 represents the average amount of variance in the CSI scores that can be attributed to each explanatory variable.
Now, let's examine the coefficients for the Explanatory Variables. The Intercept coefficient value of 0.71803167 indicates the predicted CSI
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