Assignment Instructions

Using the “dataset1.sav” dataset, perform a two-way MANOVA with post-hoc analysis to determine the effect of age category (agecat4) and job satisfaction (satjob2) on the combined dependent variable of hours worked per week (hrs1) and years of education (educ). Before performing any analysis, be sure to (1) screen for missing data and outliers, taking necessary steps to reduce the missing data / outliers and (2) test.

1.Using the “dataset1.sav” dataset, perform a two-way MANOVA with post-hoc analysis to determine the effect of age category (agecat4) and job satisfaction (satjob2) on the combined dependent variable of hours worked per week (hrs1) and years of education (educ). Before performing any analysis, be sure to (1) screen for missing data and outliers, taking necessary steps to reduce the missing data / outliers and (2) test the assumeptions of normality and linearity of the dependent variables. Once your data has been cleaned and prior to conducting the final MANOVA, be sure to test of homogeneity of variance-covariance.

2.Using the “dataset1.sav” dataset, perform a two-way MANCOVA with post-hoc analysis to determine the effect of age category (agecat4) and job satisfaction (satjob2) on the combined dependent variable of hours worked per week (hrs1) and years of education (educ) while controlling for an individual’s income (rincom91). Before performing any analysis, be sure to (1) screen for missing data and outliers, taking necessary steps to reduce the missing data / outliers and (2) test the assumeptions of normality and linearity of the dependent variables. Once your data has been cleaned and prior to conducting the final MANCOVA, be sure to perform a preliminary MANCOVA to test the assumptions of homogeneity of variance-covariance and homogeneity of regression slopes.

3.Using the “dataset2.sav” dataset, examine the indicated relationships. Be sure to include the coefficient of determination (r2) if applicable.

a.Examine the relationship between salary and years of service for all respondents.
b.Examine relationship between salary and years of education for all respondents.
c.Examine relationship between salary and job classification for all respondents.

4.Using the “experim.sav” dataset, examine the relationship between depression at time 1 (pretest) and depression at time 3 (test at 3 months later) for males and then again for females. Be sure to include the coefficient of determination (r2) if applicable. Please test if there are significant differences in the two correlations (males vs. females).

5.Using the “dataset2.sav” dataset, predict the salary from years of education.
a.Write-up the results of your regression model.
b.What is the simple linear regression model (equation) developed?
c.Using the equation from sub-question “b”, what salary would you predict for someone with a high school education (12 years of school)?
d.Using the equation from sub-question “b”, what salary would you predict for someone with a college education (16 years of school)?
e.Using the equation from sub-question “b”, what salary would you predict for someone with a graduate / professional education (19 years of school)?

6.Using the “country-a.sav” dataset, predict female life expectancy from loge number of doctors per 10,000 people (lndocs).
a.Write-up the results of your regression model.
b.What is the simple linear regression model (equation) developed?
c.Using the equation from sub-question “b”, calculate the female life expectancy for the a per 10,000 rate of 2.58 doctors.
d.Using the equation from sub-question “b”, calculate the female life expectancy for the a per 10,000 rate of 12.99 doctors.
e.Using the equation from sub-question “b”, calculate the female life expectancy for the a per 10,000 rate of 16.23 doctors.

Using the “dataset1.sav” dataset, perform a two-way MANOVA with post-hoc analysis to determine the effect of age category (agecat4) and job satisfaction (satjob2) on the combined dependent variable of hours worked per week (hrs1) and years of education (educ). Before performing any analysis, be sure to (1) screen for missing data and outliers, taking necessary steps to reduce the missing data / outliers and (2) test

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