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1 Testing for Multiple Regression Jodi Vanderwier Walden University RSCH 8201 February 5, 2022

2 Part I This week’s assignment continues to work with multiple regression. Multiple regression shows the researcher and estimate of how more than one independent variable affects one dependent variable (Frankfort-Nachmias et al., 2021). There must also be a linear relationship between the outcome variable and the independent variable. Multicollinearity occurs when independent variables in a regression model are correlated. If there is a high degree of correlation between the variables, there may be problems when trying to interpret the results (Frost, 2021). Does the Unites States democracy have an impact on the problems with public health clinics and problems within the public schools? Using the Afrobarmeter dataset for multiple linear regression, the dependent variable used is democracy in the US and the independent variables used are problems with the public health clinics and problems in public schools. The regression model produced an R-squared value of ( R 2= .003) finding a weak effect, implying the variations in the United States democracy had minimal impact on public health clinic or public schools. The Durban Watson assumption also shows that there is no autocorrelation ( DW =1.969) between democracy and public health care and public schools. The two-way ANOVA shows a statistic of ( F =6.160) and a significance of ( p =.002) p <.001, rejecting the null. The coefficients provided a significance in the constant of ( p =.000) with the collinearity statistics .532,VIF 1.880, and Cook’s Distance showing a mean of .000 all are indicative of no correlation or weakness between the dependent and independent variables.

3 Based on my results, there is no correlation between the democracy in the United States and public health care or public schooling. Provided with this information, it would lead the researcher to believe that problems within the public school system and the public health care system are generated within each state and the counties that lie within. To make these entities more appealing the plea for help must begin at the county or state level. Part II Continuing with multiple regression but adding dummy variables, I chose to use the HS Long Study dataset. For the dependent variable I used the number of AP math classes offered. The two dummy variables I chose were school location (students’ school) and the number of AP classes offered (APClass). Does the location of the school bear any effect on the amount of AP classes offered, specifically, AP math classes? The regression model produced an R-square value of .024( R 2 =.024) does not have any significance nor was significance found in the Durban Watson statistic of 2.028 ( DW =2.028). The insignificance of the regression model shows that the location of the school has a weak effect on AP classes, specifically AP math classes. The two-way ANOVA showed as statistic of ( F =21.548) with a significance of (p=.000) p <.001rejecting the null. The coefficients provided a significance in the constant of ( p =.000) with the collinearity statistics .998, VIF 1.002, and Cook’s Distance showing a mean of .001 all are indicative of no correlation or weakness between the dependent and independent variables. Based on the results, the location of the school has no bearing on the amount of AP math classes or AP classes in general. The results provide the researcher with information that the

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4 location of the schools do not have an impact on AP courses and therefore, the researcher may need to make the sample smaller to include how many students take these classes and are there teachers that are properly trained to teach the AP classes that are offered.

5 References Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2021). Social statistics for a diverse society (9th ed.). SAGE Publications, Inc. Frost, J. (2021). Multicollinearity in regression analysis: Problems, detection, and solutions . Statistics By Jim. Retrieved February 5, 2022, from https://statisticsbyjim.com/regression/multicollinearity-in-regression-analysis/

6 Appendix I.

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10 Appendix II.

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