Question description<\/p>\n
Part A<\/p>\n
Some questions in Part A require that you
\naccess data from Statistics for People Who (Think
\nThey) Hate Statistics. This data is available on the student website under
\nthe Student Text Resources link.<\/p>\n
Use the following data to answer Questions
\n1a and 1b. <\/p>\n
Total no. of problems correct
\n(out of a possible 20)<\/p>\n
Attitude toward test taking
\n(out of a possible 100)<\/p>\n
17<\/p>\n
94<\/p>\n
13<\/p>\n
73<\/p>\n
12<\/p>\n
59<\/p>\n
15<\/p>\n
80<\/p>\n
16<\/p>\n
93<\/p>\n
14<\/p>\n
85<\/p>\n
16<\/p>\n
66<\/p>\n
16<\/p>\n
79<\/p>\n
18<\/p>\n
77<\/p>\n
19<\/p>\n
91<\/p>\n
Compute the Pearson
\nproduct-moment correlation coefficient by hand and show all your work.Construct a scatterplot for
\nthese 10 values by hand. Based on the scatterplot, would you predict the
\ncorrelation to be direct or indirect? Why?Rank the following correlation coefficients
\non strength of their relationship (list the weakest first):<\/p>\n
+.71<\/p>\n
+.36<\/p>\n
\u2013.45<\/p>\n
.47<\/p>\n
\u2013.62<\/p>\n
Use IBM\u00ae SPSS\u00ae software
\nto determine the correlation between hours of studying and grade point average for
\nthese honor students. Why is the correlation so low?<\/p>\n
Hours of studying<\/p>\n
GPA<\/p>\n
23<\/p>\n
3.95<\/p>\n
12<\/p>\n
3.90<\/p>\n
15<\/p>\n
4.00<\/p>\n
14<\/p>\n
3.76<\/p>\n
16<\/p>\n
3.97<\/p>\n
21<\/p>\n
3.89<\/p>\n
14<\/p>\n
3.66<\/p>\n
11<\/p>\n
3.91<\/p>\n
18<\/p>\n
3.80<\/p>\n
9<\/p>\n
3.89<\/p>\n
Look at the following table. What type of
\ncorrelation coefficient would you use to examine the relationship between
\nethnicity (defined as different categories) and political affiliation? How
\nabout club membership (yes or no) and high school GPA? Explain why you selected
\nthe answers you did.<\/p>\n
Level of Measurement and Examples<\/p>\n
Variable
\nX<\/p>\n
Variable Y<\/p>\n
Type of correlation<\/p>\n
Correlation
\nbeing computed<\/p>\n
Nominal (voting preference, such as Republican or
\nDemocrat)<\/p>\n
Nominal (gender, such as male or female)<\/p>\n
Phi coefficient<\/p>\n
The correlation between voting preference and gender<\/p>\n
Nominal (social class, such as high, medium, or
\nlow)<\/p>\n
Ordinal (rank in high school graduating class)<\/p>\n
Rank biserial coefficient <\/p>\n
The correlation between social class and rank in
\nhigh school<\/p>\n
Nominal (family configuration, such as intact or
\nsingle parent)<\/p>\n
Interval (grade point average)<\/p>\n
Point biserial<\/p>\n
The correlation between family configuration and
\ngrade point average<\/p>\n
Ordinal (height converted to rank)<\/p>\n
Ordinal (weight converted to rank)<\/p>\n
Spearman rank correlation coefficient<\/p>\n
The correlation between height and weight<\/p>\n
Interval (number of problems solved)<\/p>\n
Interval (age in years)<\/p>\n
Pearson product-moment correlation coefficient<\/p>\n
The correlation between number of problems solved
\nand the age in years<\/p>\n
When two variables are correlated (such as
\nstrength and running speed), it also means that they are associated with one
\nanother. But if they are associated with one another, then why does one not
\ncause the other?Given the following information, use Table
\nB.4 in Appendix B of Statistics
\nfor People Who (Think They) Hate Statistics to determine whether the correlations are significant and how you
\nwould interpret the results.The correlation between
\nspeed and strength for 20 women is .567. Test these results at the .01 level
\nusing a one-tailed test.The correlation between the
\nnumber correct on a math test and the time it takes to complete the test is
\n\u2013.45. Test whether this correlation is significant for 80 children at the .05
\nlevel of significance. Choose either a one- or a two-tailed test and justify
\nyour choice.The correlation between
\nnumber of friends and grade point average (GPA) for 50 adolescents is .37. Is
\nthis significant at the .05 level for a two-tailed test?Use the data in Ch. 15 Data Set 3 to answer
\nthe questions below. Do this one manually or use IBM\u00ae SPSS\u00ae software.Compute the correlation
\nbetween income and level of education.Test for the significance
\nof the correlation.What argument can you make
\nto support the conclusion that lower levels of education cause low income?Use the following data set to answer the
\nquestions. Do this one manually.Compute the correlation
\nbetween age in months and number of words known.Test for the significance
\nof the correlation at the .05 level of significance.Recall what you learned in
\nCh. 5 of Salkind
\n(2011)about correlation coefficients and interpret
\nthis correlation.<\/p>\n
Age in months<\/p>\n
Number of
\nwords known<\/p>\n
12<\/p>\n
6<\/p>\n
15<\/p>\n
8<\/p>\n
9<\/p>\n
4<\/p>\n
7<\/p>\n
5<\/p>\n
18<\/p>\n
14<\/p>\n
24<\/p>\n
18<\/p>\n
15<\/p>\n
7<\/p>\n
16<\/p>\n
6<\/p>\n
21<\/p>\n
12<\/p>\n
15<\/p>\n
17<\/p>\n
How does linear regression differ from
\nanalysis of variance?Betsy is interested in predicting how many
\n75-year-olds will develop Alzheimer\u2019s disease and is using level of education
\nand general physical health graded on a scale from 1 to 10 as predictors. But
\nshe is interested in using other predictor variables as well. Answer the
\nfollowing questions.What criteria should she
\nuse in the selection of other predictors? Why?Name two other predictors
\nthat you think might be related to the development of Alzheimer\u2019s disease.With the four predictor
\nvariables (level of education, general physical health, and the two new ones
\nthat you name), draw out what the model of the regression equation would look
\nlike.Joe Coach was curious to know if the
\naverage number of games won in a year predicts Super Bowl performance (win or
\nlose). The x variable was the average
\nnumber of games won during the past 10 seasons. The y variable was whether the team ever won the Super Bowl during the
\npast 10 seasons. Refer to the following data set:<\/p>\n
Team<\/p>\n
Average no.
\nof wins over 10 years<\/p>\n
Bowl? (1
\n= yes and 0 = no)<\/p>\n
Savannah
\nSharks<\/p>\n
12<\/p>\n
1<\/p>\n
Pittsburgh
\nPelicans<\/p>\n
11<\/p>\n
0<\/p>\n
Williamstown
\nWarriors<\/p>\n
15<\/p>\n
0<\/p>\n
Bennington
\nBruisers<\/p>\n
12<\/p>\n
1<\/p>\n
Atlanta
\nAngels<\/p>\n
13<\/p>\n
1<\/p>\n
Trenton
\nTerrors<\/p>\n
16<\/p>\n
0<\/p>\n
Virginia
\nVipers<\/p>\n
15<\/p>\n
1<\/p>\n
Charleston
\nCrooners<\/p>\n
9<\/p>\n
0<\/p>\n
Harrisburg
\nHeathens<\/p>\n
8<\/p>\n
0<\/p>\n
Eaton
\nEnergizers<\/p>\n
12<\/p>\n
1<\/p>\n
How would you assess the
\nusefulness of the average number of wins as a predictor of whether a team ever
\nwon a Super Bowl?What\u2019s the advantage of
\nbeing able to use a categorical variable (such as 1 or 0) as a dependent
\nvariable?What other variables might
\nyou use to predict the dependent variable, and why would you choose them?From Salkind (2011). Copyright \u00a9 2012 SAGE. All Rights Reserved.
\nAdapted with permission.<\/p>\n
Part B<\/p>\n
Some questions in Part B require
\nthat you access data from Using
\nSPSS for Windows and Macintosh. This data is available on the student website under the
\nStudent Text Resources link. The data for this exercise is in thedata file
\nnamed Lesson 33 Exercise File 1.
\nPeter was interested in determining if children who hit a
\nbobo doll more frequently would display more or less aggressive behavior on the
\nplayground. He was given permission to observe 10 boys in a nursery school
\nclassroom. Each boy was encouraged to hit a bobo doll for 5 minutes. The number
\nof times each boy struck the bobo doll was recorded (bobo). Next, Peter
\nobserved the boys on the playground for an hour and recorded the number of
\ntimes each boy struck a classmate (peer).
\nConduct a linear regression to predict the
\nnumber of times a boy would strike a classmate from the number of times the boy
\nhit a bobo doll. From the output, identify the following:Slope associated with the
\npredictorAdditive constant for the
\nregression equationMean number of times they
\nstruck a classmateCorrelation between the
\nnumber of times they hit the bobo doll and the number of times they struck a
\nclassmateStandard error of estimate<\/p>\n","protected":false},"excerpt":{"rendered":"
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