#1 E & F

#2

#3 A,B and D

Chapter 3 Pre-Analysis Data Screening Exercises for Chapter 3 This exercise utilizes the data set schools-a.sav, which can be downloaded from this website: www.Pyrczak.com/data 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? NONE b. Is there a severe split in frequencies between groups? NO 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 Levene's Test of Equal Variances indicate homogeneity of variance? Explain. 2. Examination of the variable of science 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? Inverse = 1 / sci q s 3. You are interested in studying predictors (math94me, loinc93, and read94me) of the percentage graduating in 1994 (grad94). 94 , Ionic as skewed If Is severe stew ticg or inverse Treats normalit a. Examine univariate normality for each variable. What are your conclusions about the distribu- tions? What transformations should be conducted? not uni normality - not multi. nov. J no mares skewed Rest less srewed To meet After making the necessary transformations, examine multivariate outliers using Mahalanobis dis- tance. Which cases should be deleted? . I Multivanent - do not delet outliers be guys outivers - last is greater TI. SD Than 3.5 outier # 64 is outlier MAH OF 9 . 49 outlier is multiravianos outlick ( weird combinationsName Type Width Decimals Label Values Missing Columns Align Measure Role school String 16 0 School name None None 20 Left No.. Input N loinc93 Numeric 6 Percent low inc... None None 8 Right Scale Input W lep93 Numeric 6 None None Right Scale Input lep94 Numeric 8 1 None None Right Input 5 grad93 Numeric 6 1 None None 8 Right Scale Input 6 grad94 Numeric 8 1 None None Right Scale Input 7 act94 Numeric 8 None None Right Scale Input 8 act93 Numeric None None 8 Right Scale Input 9 pctact93 Numeric 1 percent taking ... None None 8 Right Scale Input 10 pctact94 Numeric 1 percent taking ... None None 8 Right Scale Input 11 math93 Numeric W 0 None None 8 Right Scale Input 12 math94me Numeric 0 None None 8 Right Scale Input 13 mathch94 Numeric 0 None None 8 Right Scale Input 14 read93 Numeric W 0 None None 8 Right Scale Input 15 read94me Numeric 3 0 None None 8 Right Scale Input 16 readch94 Numeric 8 0 None None 8 Right Scale Input 17 science3 Numeric W None None 8 Right Scale Input 18 sci94me Numeric None None 8 Right Scale Input 19 scich94 Numeric 8 0 change in % me... None None Right Scale Input 20 id Numeric 8 2 None None 8 Right Nominal Input 21 medloinc Numeric 0 above or below ... {0, below th... None 8 Right Nominal Input 22 MAH_1 Numeric 11 5 Mahalanobis Di... None None 13 Right Scale Input 23 filter_s Numeric 1 0 MAH_1