Practical Multivariate Analysis 6th Edition Abdelmonem Afifi, Susanne May, Virginia A. Clark, Robin Donatello - Solutions

Unlock the potential of your studies with the comprehensive solutions for "Practical Multivariate Analysis 6th Edition" by Abdelmonem Afifi, Susanne May, Virginia A. Clark, and Robin Donatello. Access step-by-step answers and solved problems with our exclusive online answers key, solution manual, and solutions pdf. Dive deep into the textbook's complex topics with our chapter solutions and test bank, designed to enhance your understanding. Whether you're seeking questions and answers or the instructor manual, find everything you need for free download. Elevate your learning experience with our expertly crafted resources.

Using the Parental HIV data set (see Appendix A), plot a histogram, a boxplot, and a normal probability plot for the variable AGESMOKE. This variable is the age in years when the respondent started smoking. If the respondent did not start smoking, AGESMOKE was assigned a value of zero. Decide what
Repeat Problem 5.7 with weights expressed in ounces instead of pounds. How will your conclusions change? Obtain normal probability plots of the logarithm of mothers’ weights expressed in pounds and then in ounces, and compare.
Obtain a normal probability plot of the index given in Problem 5.8. Suppose that you had been ignorant of the lack of independence of these data and had treated them as if they were independent samples. Assess whether they are normally distributed.
Generate ten random normal deviates. Display a probability plot of these data. Suppose you didn’t know the origin of these data. Would you conclude they were normally distributed?What is your conclusion based on the Shapiro–Wilk test? Do ten observations provide sufficient information to check
The accompanying data are from the New York Stock Exchange Composite Index for the period August 9 through September 17, 1982. Run a program to plot these data in order to assess the lack of independence of successive observations. (Note that the data cover five days per week, except for a holiday
Obtain normal probability plots of mothers’ and fathers’ weights from the lung function data set described in Appendix A. Discuss whether or not you consider weight to be normally distributed in the population from which this sample is taken.
Take the logarithm of the CESD score plus 1 and compare the histograms of CESD and log(CESD + 1). (A small constant must be added to CESD because CESD can be zero.)
Use the two sets of 100 random numbers from Problem 5.4. Display boxplots of these two sets of values and state which of the three graphical methods (histograms, normal probability plots, and boxplots) are most useful in assessing normality.
Generate a set of 100 random normal deviates by using the random number generator in your software program of choice (R: rnorm, SAS: NORMAL, Stata: rnormal, SPSS:RV.NORMAL). Display a histogram and normal probability plot of these values. Square these numbers by using transformations. Compare
Repeat Problems 5.1 and 5.2, taking the square root of income.
Take the logarithm to base 10 of the income variable in the depression data set. Compare the histogram of income with the histogram of log(INCOME). Also, compare the normal probability plots of income and log(INCOME).
Using the depression data set described in Tables 3.4 and 3.5, create a variable equal to the negative of one divided by the cubic root of income. Display a normal probability plot of the new variable.
Using the mice data, create a profile plot for the average weight of mice per group over time.
Using a scatterplot matrix, repeat the Problem 4.8 for fathers’ measurements instead of those of the oldest child. Did you find the same pattern of relationships between body measurements and FEV1 in fathers as you did for the oldest child?
Using the parental HIV data set create a few visualizations to explore the relationship between the variables of interest listed below. In a few sentences describe the information about the given relationship you learn from each graph, and what specific features of the graph led you to that
Using the lung function data explore and describe the following relationships:a) How does the residential area affect the lung function of the parents?b) For the oldest child, plot the relationship between FEV1 and (i) age; (ii) height; (iii) weight using a scatterplot and lowess line.
Construct a scatterplot of income versus employment status from the depression data set. From the data in this table, decide if there are any adults whose incomes are unusual considering their employment status. Are there any adults in the data set whom you think are unusual?
For the lung cancer data set,a) produce a separate histogram of the variable Days for small and large tumor sizes (0 and 1 values of the variable Staget)b) compute a two-way frequency table of the variable Staget versus the variable Deathc) comment on the results of (a) and (b).
For the lung function data set, produce a two-way table of gender of child 1 versus gender of child 2 (for families with at least two children). Describe the distribution of genders for these families.
For the lung cancer data set,a) construct a histogram of the variable Daysb) for every other variable produce a frequency table of all possible values.
Construct histograms for mothers’ and fathers’ heights and weights from the lung function data set. Describe cases that you consider to be outliers.
For the lung function data set, create a new variable called AGEDIFF = (age of child 1) – (age of child 2) for families with at least two children. Produce a frequency count of this variable.Are there any negative values? Comment.
For the depression data set, determine if any of the variables have observations that do not fall within the ranges given in Table 3.4, codebook for depression data.
From the lung function data set, determine how many families have one child, two children, and three children between the ages of 7 and 18.
From the variables ACUTEILL and BEDDAYS described in Table 3.4, create a single variable that takes on the value 1 if the person has been both bedridden and acutely ill in the last two months and that takes on the value 0 otherwise.
In the Parental HIV data set, the variable LIVWITH (who the adolescent was living with) was coded 1=both parents, 2=one parent, and 3=other. Transform the data so it is coded 1=one parent, 2=two parents, and 3=other using the features available in the statistical package or spreadsheet program you
Consistency checks are sometimes performed to detect possible errors in the data. If a data set included information on sex, age, and use of contraceptive pill, describe a consistency check that could be used for this data set.
Combine the results from the following two questions into a single variable that measures the total number of days the individual has been sick during the time period.: This would allow one variable to be used for analysis involving this data.a. Have you been sick during the last two weeks?Yes, go
For the statistical package you intend to use, describe how you would add data from three more time periods for the same subjects to the depression data set.
Describe the person in the depression data set who has the highest total CESD score.
Transfer or read in a data set that was entered into a spreadsheet program into your statistical software package.
Using the data set entered in the previous problem, delete the P/E variable for the Dow Chemical company and D/E for Stauffer Chemical and Nalco Chemical in a way appropriate for the statistical package you are using. Then, use the missing value features in your statistical package to find the
Enter the data set given in Table 9.1, Chemical companies’ financial performance (Section 9.3), using a data entry program of your choice. Make a codebook for this data set.
Give an example from a field that you are familiar with of an increased sophistication of measuring that has resulted in a measurement that used to be ordinal now being interval.
The Parental HIV data set described in Appendix A includes the following variables: job status of mother (JOBMO, 1=employed, 2=unemployed, and 3=retired/disabled) and mother’s education (EDUMO, 1=did not complete high school, 2=high school diploma/GED, and 3=more than high school). Classify these
Data that are ordinal are often analyzed by methods that Stevens reserved for interval data.Give reasons why thoughtful investigators often do this.
Give an example of nominal, ordinal, interval, and ratio variables from a field of application you are familiar with.
If the RELIG variable described in Table 3.4 of this text was recoded 1 = Catholic, 2 = Protestant, 3 = Jewish, 4 = none, and 5 = other, would this meet the basic empirical operation as defined by Stevens for an ordinal variable?
From a field of statistical application (perhaps your own field of specialty), describe a data set and repeat the procedures described in Problem 2.3.
Repeat Problem 2.3 for the lung cancer data set described in Table 13.1.
For the chronic respiratory study data described in Appendix A, classify each variable according to Stevens’s scale and according to whether it is discrete or continuous. Pose two possible research questions and decide on the appropriate dependent and independent variables.
In a survey of users of a walk-in mental health clinic, data have been obtained on sex, age, household roster, race, education level (number of years in school), family income, reas for coming to the clinic, symptoms, and scores on a screening examination. The investigator wishes to determine what
Classify the following types of data by using Stevens’s measurement system: decibels of noise level, father’s occupation, parts per million of an impurity in water, density of a piece of bone, rating of a wine by one judge, net profit of a firm, and score on an aptitude test.
For the school example and GEE marginal approach we mentioned that the most appropriate correlation structure is exchangeable. Give reasons why the AR(1) or the unstructured options for correlation structure might not be appropriate for this setting.
For the mice data, instead of modeling weight linearly over time, model weight as a quadratic function of time by including a term for days as well as for days2. Which model (random intercept, random slope) and correlation structure(s) seem most appropriate for these data?Describe your reasoning
There might be siblings represented in the school data set. The factor parental education could potentially be considered another level in the hierarchical model. Give reasons for when and why this might or should (not) be addressed.
For the school data set (again, using math score as the outcome variable) start with fitting a model with the following fixed effects: SES centered around its respective school mean, school mean SES, public school (vs. private), hours spent on homework, race, gender, and the following random
For the school data set (again, using math score as the outcome variable) fit the following models:a) A random intercept model with random intercepts for schools and both the raw SES scores and the respective school means as fixed effects.b) The same model as in (a) except using SES centered around
For the school data set (again, using math score as the outcome variable) fit a random intercept model with random intercepts for schools and SES centered around its respective school mean as a fixed effect. What is the estimated within school slope for SES? Compute the estimated between school
For the school data set using math score as the outcome variable, fit the following models:a) A fixed effects model with schools and SES as fixed effects.b) The same as (a), but with each SES centered around its respective school mean.c) Compare the models in (a) and (b). What effect did centering
For the school data set generate box plots of SES by schools (similar to Figure 18.2). Interpret the graph with respect to potential differences in mean SES and differences in variability of SES across schools.
Three students have the following scores on their math, english and social science classes.Student 1: 87.3, 91.2 and 86.0, Student 2: 75.4, 81.3 and 79.6, Student 3: 98.8, 95.0 and 94.7.a) Using a spreadsheet program or a calculator, calculate the intra-class correlation coefficient.b) Change three
(Problem 17.9 continued) Compare the results you get from Problem 17.9 and Problem 12.30.
This problem uses the Northridge earthquake data set. Look for significant associations in homeowner status (V449), home evacuation status (V173), and status of home damage (V127)using a log-linear analysis.
Look for significant associations among the variables gender, currently living with(LIVWITH), the financial situation (FINSIT), and having enough money for food (MONFOOD)in the Parental HIV data using log-linear analyses. For the final model assess goodness-of-fit. Interpret the findings.
In Problem 17.5, eliminate the four-way interaction, then compare the two models: the one with and the one without Smokfu. Interpret the results.
Rerun the analyses given in Problem 17.4 adding a delta of 0.01, 0.1, and 0.5 to see if it changes any of the results.
Run a four-way log-linear analysis using the variables Stagen, Hist, Smokfu and Death in the lung cancer data set. Report on the significant associations that you find.
Perform a log-linear analysis using data from the lung cancer data set described in Section 13.3 and Appendix A. Check if there are any significant associations among Staget, Stagen and Hist. Which variable is resulting in a small number of observations in the cells?
Split the variable EDUCAT in the depression data set into two groups, those who completed high school and those who did not. Also split INCOME at 18 or less and greater than 18 (this is thousands of dollars). Run a log-linear analysis of SEX versus grouped EDUCAT versus grouped INCOME and describe
Run a log-linear analysis using the variables DRINK, CESD (split at ten or less), and TREAT from the depression data set to see if there is any significant association among these variables.
Using the variables AGE and CESD from the depression data, make two new categorical variables called CAGE and CCESD. For CAGE, group together all respondents that are 35 years old or less, 36 up to and including 55, and over 55 into three groups. Also group the CESD into two groups; those with a
For the Parental HIV data consider the subgroup of adolescents who have used marijuana. Perform a hierarchical cluster analysis separately for females on the following variables from the Parental HIV data: the sum of the variables describing the neighborhood (NGHB1–NGHB11), enough money for food
Repeat Problem 16.1, using the variables age, income, and education instead of the last seven variables.
Create a data set from the family lung function data described in Appendix A as follows. It will have three times the number of observations that the original data set has—the first third of the observations will be the mothers’ measurements, the second third those of the fathers, and the final
Repeat Problem 16.6, using the K-means method for K = 4. Compare the results with the four clusters produced in Problem 16.6.
Repeat Problem 16.6, using AREA as an additional clustering variable. Comment on your results.
For the family lung function data described in Appendix A, perform a hierarchical cluster analysis using the variables FEV1 and FVC for mothers, fathers, and oldest children. Compare the distribution of area residence in the resulting groups.
Describe how you would expect guards, forwards, and centers in basketball to cluster on the basis of size or other variables. Which variables should be measured?
For the accompanying small, hypothetical data set, plot the data by using methods given in this chapter, and perform both hierarchical and K-means clustering with K = 2. Cases X1 X2 1 11 10 2 3 8 10 9 11 345678 8538 +45 8 5 11 11 10 12
Perform a cluster analysis on the chemical company data in Table 9.1, using the K-means method for K = 2;3; 4.
For the situation described in Problem 8.7, modify the data for X1, X2,. . . , X9 as follows. For the first 25 cases, add 10 to X1, X2, X3. For the next 25 cases, add 10 to X4, X5, X6. For the next 25 cases, add 10 to X7, X8. For the last 25 cases, add 10 to X9.Now perform a cluster analysis to
For the depression data set, use the last seven variables to perform a cluster analysis producing two groups. Compare the distribution of CESD and cases in the groups. Compare also the distribution of sex in the groups. Try two different programs with different options. Comment on your results.
This problem uses the Northridge earthquake data set. Perform a factor analysis on the following items in the Brief Symptom Inventory (BSI): V346, V357, V364, V383, V390, V394, V351, V358, V385, V386, V391. Note that the first six items relate to the anxiety subscale of BSI and the next five items
Repeat Problem 15.8 using an oblique rotation. Do the substantive conclusions change?
Perform a factor analysis on all of the items of the Parental Bonding scale for the Parental HIV data (see Appendix A and the codebook). Retain two factors. Rotate the factors using an orthogonal rotation. Do the items with the highest loadings for each of the factors correspond to the items of the
For the depression data set, perform four factor analyses on the last seven variables DRINK–CHRONILL (Table 3.4 or 3.5). Use two different initial extraction methods and both orthogonal and oblique rotations. Interpret the results. Compare the results to those from Problem 14.1.
Separate the depression data set into two subgroups, men and women. Using four factors, repeat the factor analysis in Table 15.7. Compare the results of your two factor analyses to each other and to the results in Table 15.7.
For the data generated in Problem 8.7, perform four factor analyses, using two different initial extraction methods and both orthogonal and oblique rotations. Interpret the results.
Perform a factor analysis on the data in Table 9.1 and explain any insights this factor analysis gives you.
Another method of factor extraction, maximum likelihood, was mentioned in Section 15.6 but not discussed in detail. Use one of the packages which offers this option to analyze the data along with an oblique and orthogonal rotation. Compare the results with those in Problems 15.1 and 15.2, and
Repeat the analysis of Problem 15.1 and Table 15.7, but use an iterated principal factor solution instead of the principal components method. Compare the results.
The CESD scale items (C1–C20) from the depression data set in Chapter 3 were used to obtain the factor loadings listed in Table 15.7. The initial factor solution was obtained from the principal components method, and a varimax rotation was performed. Analyze this same data set by using an oblique
Perform a principal components analysis on all the items of the Parental Bonding scale for the Parental HIV data (see Appendix A and the codebook). How many principal components would you expect to retain for this scale? How many components should be retained based on the rules of thumb mentioned
Perform a principal components analysis on AGE and INCOME using the depression data set.Include all the additional data points listed in Problem 7.9(b). Plot the original variables and the principal components. Indicate the added points on the graph, and discuss the results.
Using the family lung function data, perform a principal components analysis on mother’s height, weight, age, FEV1, and FVC. Use the covariance matrix, then repeat using the correlation matrix. Compare the results and comment.
(Continuation of Problem 14.7.) Perform a regression of FEV1 for the oldest child on the principal components found in Problem 14.7. Compare the results to those from Problem 8.15.
Using the family lung function data, perform a principal components analysis on age, height, and weight for the oldest child.
Using the family lung function data described in Appendix A define a new variable RATIO= FEV1/FVC for the fathers. What is the correlation between RATIO and FEV1? Between RATIO and FVC? Perform a principal components analysis on FEV1 and FVC, plotting the results. Perform a principal components
Perform a principal components analysis on the data in Table 9.1(not including the variable P/E). Interpret the components. Then perform a regression analysis with P/E as the dependent variable, using the relevant principal components. Compare the results with those in Chapter 9.
(Continuation of Problem 14.3.) Perform the regression of Y on the principal components.Compare the results with the multiple regression of Y on X1 to X9.
For the data generated in Problem 8.7, perform a principal components analysis on X1;X2; : : : ;X9. Compare the results with what is known about the population.
(Continuation of Problem 14.1.) Perform a regression analysis of CASES on the last seven variables as well as on the principal components. What does the regression represent? Interpret the results.
For the depression data set described in Appendix A, perform a principal components analysis on the last seven variables DRINK–CHRONILL (Table 3.5). Interpret the results.
Perform a proportional hazards regression using the variables Staget, Treat, and Perfbl and stratify the model by Poinf. Compare the estimated hazard ratios to the ones obtained in Problem 13.9.
Evaluate graphically and statistically the proportional hazards assumption for the variables Perfbl, Poinf, and Treat in the model presented in Table 13.4 using the methods described in Section 13.8.
Define a variable Smokchng that measures change in smoking status between baseline and follow-up, so that Smokchng equals 1 if a person changes from being a smoker to being an ex-smoker and equals 0 otherwise.(a) Is there an association between Staget and Smokchng?(b) What effects does Smokchng
Repeat Problem 13.6assuming a proportional hazards model.
Assuming a log-linear model, do the effects of smoking status upon survival change depending on the tumor size at diagnosis?
Repeat Problem 13.4assuming a proportional hazards model.

Showing 400 - 500 of 2391

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Last

Practical Multivariate Analysis 6th Edition Abdelmonem Afifi, Susanne May, Virginia A. Clark, Robin Donatello - Solutions

Step by Step Answers