Answering Exercises for Chapter 3

I need someone to help me answering the Exercises for Chapter 3
Just these questions for chapter 3
depending on the book’s explanation, and the uploaded data.

This exercise utilizes the data set schools-a.sav, which can be downloaded from this website:

www.routledge.com/9781138289734

1.    You are interested in investigating if being above or below the median income (medloinc) impacts ACT means (act94) for schools. Complete the necessary steps to examine univariate grouped data in order to respond to the questions below. Although deletions and/or transformations may be implied from your examination, all steps will examine original variables.

a.    How many participants have missing values for medloinc and act94?

b.    Is there a severe split in frequencies between groups?

c.    What are the cutoff values for outliers in each group?

d.    Which outlying cases should be deleted for each group?

e.    Analyzing histograms, normal Q-Q plots, and tests of normality, what is your conclusion regard-ing normality? If a transformation is necessary, which one would you use?

f.    Do the results from Levenes test for equal variances indicate homogeneity of variance? Explain.

2.    Examination of the variable of scienc93 indicates a substantial to severe positively skewed distribu-tion. Transform this variable using the two most appropriate methods. After examining the distribu-tions for these transformed variables, which produced the better alteration?

3.    You are interested in studying predictors (math94me, loinc93, and read94me) of the percentage grad-uating in 1994 (grad94).

a.    Examine univariate normality for each variable. What are your conclusions about the distribu-tions? What transformations should be conducted?

b.    After making the necessary transformations, examine multivariate outliers using Mahalanobis dis-tance. Which cases should be deleted?
c.    After deleting the multivariate outliers, examine multivariate normality and linearity by creating a Scatterplot Matrix.

 

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