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Fundamental Statistics for the Behavioral Sciences 8th Edition David C. Howell - Solutions

The data relevant to Exercise 9.13 are the test scores and SAT-V scores for the 28 people in the group that did not read the passage. These data areMake a scatterplot of these data and draw by eye the best-fitting straight line through thepoints.
Compute the correlation coefficient for the data in Exercise 9.14. Is this correlation significant, and what does it mean to say that it is (or is not) significant?
Interpret the results from Exercises 9.11–9.13.
Expand on Exercise 9.17 to interpret the conclusion that the correlations were not significantly different.
Calculate the correlations among all numeric variables in Exercise 9.1 using SPSS.In Exercise 9.1
Plot and calculate the correlation for the relationship between ADDSC and GPA for the data in Appendix D. Is this relationship significant?
Assume that a set of data contains a curvilinear relationship between X and Y (the best-fitting line is slightly curved). Would it ever be appropriate to calculate r on these data?
Several times in this chapter I referred to the fact that a correlation based on a small sample might not be reliable. (a) What does “reliable” mean in this context? (b) Why might a correlation based on a small sample not be reliable?
What reasons might explain the finding that the amount of money that a country spends on health care is not correlated with life expectancy?
Considering the data relating height to weight in Figure 9.7, what effect would systematic reporting biases from males and females have on our conclusions?
Draw a figure using a small number of data points to illustrate the argument that you could have a negative relationship between weight and height within each gender and yet still have a positive relationship for the combined data.
Sketch a rough diagram to illustrate the point made in the section on heterogeneous subsamples about the relationship between cholesterol consumption and cardiovascular disease for males and females.
The chapter referred to a study by Wong who showed that the incidence of heart disease varied as a function of solar radiation. What does this have to say about any causal relationship we might infer between the consumption of red wine and a lower incidence of heart disease?
David Lane at Rice University has an interesting example of a study involving correlation. This can be found at www.ruf.rice.edu/~lane/case_studies/physical_strength/index.html. Work through this example and draw your own conclusions from the data. (For now, ignore the material on regression.)
Use MYSTAT or another program to reproduce the results shown in Figure 9.11.
What can we conclude from the data on infant mortality?
In Exercise 9.1 the percentage of mothers over 40 does not appear to be important, and yet it is a risk factor in other societies. Why do you think that this might be?
Two predictors of infant mortality seem to be significant. If you could find a way to use both of them as predictors simultaneously, what do you think you would find?
From the previous exercises, do you think that we are able to conclude that low income causes infant mortality?
Infant mortality is a very serious problem to society. Why would psychologists be interested in this problem any more than people in other professions?
The following data are from 10 health-planning districts in Vermont. Y is the percentage of live births #2,500 grams. X1 is the fertility rate for women #17 or $35 years of age. (X1 is known as the “high-risk fertility rate.”) X2 is the percentage of births to unmarried women. Compute the
Draw a diagram (or diagrams) to illustrate Exercise 10.10.
Make up a set of five data points (pairs of scores) that have an intercept of 0 and a slope of 1. (There are several ways to solve this problem, so think about it a bit.)
Take the data that you just created in Exercise 10.12 and add 2.5 to each Y value. Plot the original data and the new data. On the same graph, superimpose the regression lines. (a) What has happened to the slope and intercept? (b) What would happen to the correlation?
Generate Y^ and (Y 2 Y^ ) for the first five cases of the data in Table 10.2.
Using the data in Appendix D, compute the regression equation for predicting GPA from ADDSC.
In the chapter we saw a study by Trzesniewski et al. (2008) on trends in narcissism scores overtime. They also reported data on self-enhancement, which is the tendency to hold unrealistically positive views of oneself. The measure of self-enhancement (SelfEn) was obtained by asking students to
Why would we ever care if a slope is significantly different from 0?
The following data represent the actual heights and weights referred to in Chapter 9 for male college students.(a) Make a scatterplot of the data. (b) Calculate the regression equation of weight predicted from height for these data. Interpret the slope and the intercept. (c) What is the correlation
Calculate the standard error of estimate for the regression equation in Exercise 10.1.In Exercise 10.1.
The following data are the actual heights and weights referred to in Chapter 9 of femalecollege students:(a) Make a scatterplot of the data. (b) Calculate the regression coefficients for these data. Interpret the slope and the intercept. (c) What is the correlation coefficient for these data? (d)
Using your own height and the appropriate regression equation from Exercise 10.19 or 10.20, predict your own weight. (If you are uncomfortable reporting your own weight, predict mine— I am 5980 and weigh 156 pounds—well, at least I would like to think so.) (a) How much is your actual weight
Use your scatterplot of the data for students of your own gender and observe the size of the residuals.
Given a male and a female student who are both 5960, how much would they be expected to differ in weight?
In Chapter 3 I presented data on the speed of deciding whether a briefly presented image was the same image as the one to its left or whether it was a reversed image. However, I worry that the trials are not independent, because I was the only subject and gave all of the responses. Use the data
Write a paragraph summarizing the results in Figure 10.3 that is comparable to the paragraph in Chapter 9, Section 9.15 (Summary and Conclusions of Course Evaluation Study) describing the results of the correlational analysis.
Wainer (1997) presented data on the relationship between hours of TV watching and mean scores on the 1990 National Assessment of Educational Progress (NAEP) for eighth-grade mathematics assessment. The data follow, separated for boys and girls.(a) Plot the relationship between Hours Watched and
You probably were startled to see the very neat relationships in Exercise 10.27. There was almost no variability about the regression line. I would, as a first approximation, guess that the relationship between television hours watched and standardized test performance would contain roughly as much
Draw a scatter diagram (of 10 points) on a sheet of paper that represents a moderately positive correlation between the variables. Now drop your pencil at random on this scatter diagram. (a) If you think of your pencil as a regression line, what aspect of the regression line are you changing as you
If, as a result of ongoing changes in the role of women in society, we saw a change in the age of childbearing such that the high-risk fertility rate jumped to 70 in Exercise 10.1, what would we predict for the incidence of birthweight ,2,500 grams?
The data file named Galton.dat on this book’s website contains Galton’s data on heights of parents and children discussed in the section on regression to the mean. In these data, Galton multiplied mothers’ and daughters’ heights by 1.08 to give them the same mean as male heights, and then
Repeat the steps in Exercise 10.31 using LazStats or MYSTAT. In Exercise 10.31 (a) Regress child height against parent height. (b) Calculate the predicted height for children on the basis of parental height. (c) The data file contains a variable called Quartile ranging from 1 to 4, with 1 being the
Again referring to Exercise 9.1 in Chapter 9, how does what you know about regression contribute to your understanding of infant health in developing countries?
Using the data in Table 10.2, predict the Symptom score for a stress level of 45.
The mean Stress score in Table 10.2 was 21.467. What would your prediction be for a Stress score of 21.467? How does this compare to the mean Symptom score?
A psychologist studying perceived €œquality of life€ in a large number of cities (N 5 150) came up with the following equation using mean temperature (Temp), median income in $1,000 (Income), per capita expenditure on social services (SocSer), and population density (Popul) as predictors.(a)
Mireault (1990) studied students whose parent had died during their childhood, students who came from divorced families, and students who came from intact families. Among other things, she collected data on their current perceived sense of vulnerability to future loss (PVLoss), their level of
Interpret the results of the analysis in Exercise 11.10. In Exercise 11.10. Mireault (1990) studied students whose parent had died during their childhood, students who came from divorced families, and students who came from intact families. Among other things, she collected data on their current
The data set Harass.dat, included on this book’s website, contains data on 343 cases created
In the previous question I was surprised that the frequency of behavior was not related to the likelihood of its being reported. Suggest why this might be.
In the text I have recommended against the use of stepwise procedures for multiple regression, whereby we systematically hunt among the variables to predict some sort of optimal equation. (a) Explain why I would make such a recommendation. (b) How, then, could I justify asking you to do just that
Use the table of random numbers (Table E.9 in the Appendix) to generate data for 10 cases on six variables, and label the first variable Y and the following variables X1, X2, X3, X4, and X5. Now use any regression program to predict Y from all five predictors using the complete data set with 10
Now restrict the data set in Exercise 11.15 to eight, then six, then five cases, and record the changing values of R. Remember that these are only random data.
The file at https://www.uvm.edu/~dhowell/fundamentals8/DataFiles/Fig9-7.dat. contains Ryan’s height and weight data discussed in connection with Table 9.1. Gender is coded 1 5 male and 2 5 female. Compare the simple regression of Weight predicted from Height with the multiple correlation of
In Exercise 11.17, we ran a multiple regression with Gender as a predictor. Now run separate regressions for males and females. The file at https://www.uvm.edu/~dhowell/fundamentals8/DataFiles/Fig9-7.dat. contains Ryan’s height and weight data discussed in connection with Table 9.1. Gender is
Sethi and Seligman (1993) examined the relationship between optimism and religious conservatism by interviewing over 600 subjects from a variety of religious organizations. We can regress Optimism on three variables dealing with religiosity. These are the influence of religion on their daily lives
A great source of data and an explanation to go with it is an Internet site called the Data and Story Library (DASL) maintained by Carnegie Mellon University. Go to that site and examine the example on the relationship between brain size and intelligence. Use multiple regression to predict
Why would you think that it would be wise to include Gender in that regression?
Since you have the DASL data on brain size, note that it also includes the variables of height and weight. Predict weight from height and sex and compare with the answer for Exercise 11.17.
In examples like the Guber study on the funding of education, we frequently speak of variables like PctSAT as “nuisance variables.” In what sense is that usage reasonable here, and in what sense is it somewhat misleading?
In several places in the chapter I have shoved aside the intercept by saying that we really don’t care about it. If we don’t care about it, why do we include it?
Using the data from Section 11.7 on the relationship between symptoms and distress in cancer patients, compute the predicted values of Distress2 using Distress1 and BlamPer. Correlate those values with the obtained values of Distress2 and show that this is equal to the multiple correlation
In Exercise 9.1, we saw data on infant mortality and a number of other variables. There you predicted infant mortality from income. There is reason to believe that infants of young mothers are at increased risk, and there is considerable evidence that infant mortality can be reduced by the use of
In Exercise 11.2, which variables make a significant contribution to the prediction ofOptimism as judged by the test on their slopes?In Exercise 11.2,
In Exercise 11.2, the column headed “Tolerance” (which you have not seen before) gives you 1 minus the squared multiple correlation of that predictor with all other predictors. What can you now say about the relationships among the set of predictors?
Using the following (random) data, demonstrate what happens to the multiple correlation when you drop out cases from the data set (e.g., use 15 cases, then 10, 6, 5, 4).
Calculate the adjusted R2 for the 15 cases in Exercise 11.6. Twice in this chapter I said that we were going to ignore the adjusted R2, even though it is a perfectly legitimate statistic. Can you tell what it is “adjusting” for?
The state of Vermont is divided into 10 health-planning districts, which correspond roughly to counties. The following data represent the percentage of live births of babies weighing under 2,500 grams (Y), the fertility rate for females 17 years of age or younger (X1), total highrisk fertility rate
Using the output from Exercise 11.8, interpret the results as if they were significant. (What is one of the reasons that this current analysis is not likely to be significant, even if those relationships are reliable in the populations?)
The following numbers represent 100 random numbers drawn from a rectangular population with a mean of 4.5 and a standard deviation of 2.6. Plot the distribution of these digits.
In Exercise 5.21 we saw, among other things, the weight gain of each of 29 anorexic girls who received cognitive behavioral therapy. What null hypothesis would we likely be testing in this situation?
The data referred to in Exercise 12.10 (in pounds gained) follow. Run the appropriate t test and draw the appropriate conclusions.
Compute 95% confidence limits on m for the data in Exercise 12.11.In Exercise 12.11.
Compute a measure of effect size for the data in Exercise 12.11.In Exercise 12.11.
For the IQ data on females in Appendix D (data set Add.dat on the website), test the null hypothesis that µ female = 100.
In Exercise 12.14 you probably solved for t instead of z. Why was that necessary? In Exercise 12.14 For the IQ data on females in Appendix D (data set Add.dat on the website), test the null hypothesis that µ female = 100.
Describe the procedures that you would go through to reproduce the results in Figure 12.4.
In Section 12.3 we ran a t test to test the hypothesis that young children under stress give what they perceive to be more socially desirable answers on an anxiety measure than normal children do. We never really tested the hypothesis that they report lower levels of anxiety. For the data on these
Compute the 95% confidence limits on mean anxiety for the data in Exercise 12.17.
Are the confidence limits that you calculated in Exercise 12.18 consistent with the results of the t test in Exercise 12.17?
I drew 50 samples of 5 scores each from the same population that the data in Exercise 12.1 came from, and calculated the mean of each sample. The means are shown below. Plot the distribution of these means.
Write a brief paragraph describing the research project in Exercise 12.17 and its results.
Use one of the free statistics programs that we have been using and reproduce the results for Exercises 12.11 and 12.12. This is an example where it is probably easier to enter the data by hand.
Compare the means and the standard deviations for the distribution of digits in Exercise 12.1 and the sampling distribution of the mean in Exercise 12.2. (a) What would the Central Limit Theorem lead you to expect in this situation? (b) Do the data correspond to what you would predict?
In what way would the result in Exercise 12.2 differ if you had drawn more samples of size 5?
In what way would the result in Exercise 12.2 differ if you had drawn 50 samples of size 15?
In Table 11.1 in Chapter 11 we saw data on the state means of students who took the SAT exam. The mean Verbal SAT for North Dakota was 515. The standard deviation was not reported. Assume that 238 students took that exam. (a) Is this result consistent with the idea that North Dakota’s students
Why do the data in Exercise 12.6 not really speak to the issue of whether education in North Dakota is generally in good shape?
Using the data from Table 11.1, compute the 95% confidence limits on the Pupil/Teacher ratio across the 50 states.
You would probably be nervous about inferring a population estimate and a confidence interval for the mean U.S. SAT Combined score from the data in Table 11.1, but you are probably much less worried about your confidence limits on Pupil/Teacher ratio in Exercise 12.8. Why would this be?
Hout, Duncan, and Sobel (1987) reported on the relative sexual satisfaction of married couples.They asked each member of 91 married couples to rate the degree to which they agreed with €œSex is fun for me and my partner€ on a four-point scale ranging from €œnever or
Compute a measure of effect size for the data in Exercise 13.6, and tell what this measure indicates.In Exercise 13.6The data are presented below, in fmol/ml:
Give an example of an experiment in which using related samples would be ill-advised because of carry-over effects.
Using the data in Table 13.2, ask whether people’s first guess is usually better than their second guess. (This parallels advice that you often receive about test taking to the effect that you should not go back and change a guessed answer unless you are sure that your new answer is correct. Vul
Assume that the mean and the standard deviation of the difference scores in Exercise 13.6 would remain the same if we added more subjects. How many subjects would we need to obtain a t that is significant at a 5 .01 (two-tailed)? (The difference was significant at a =.05, but no at a =.01.) (We
Modify the data in Exercise 13.6 by shifting the entries in the “12 hour” column so as to increase the relationship between the two variables. Run a t test on the modified data and notice the effect on t. (You could never do this with real data, because paired scores must be kept together, but
In Section 13.4 I explained that by removing subject-to-subject variability from the data, related- samples designs prevent this variability from influencing the data on which the t test is run. This increases our ability to reject a false null hypothesis. Explain in your own words why this is so.
Whether or not you found a significant difference in Exercise 13.13, Vul and Pashler did. But are first guesses better than the average of the two?
If there was reason to believe that carry-over effects could influence the data on guessing behavior, how might we control such effects?
What would happen if we took the data from the anorexia example in Table 13.1 and reexpressed the dependent variable in kilograms instead of pounds?

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