8.5: Exercises
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left#1\right}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Introduction to multiple regression
8.1 Baby weights, Part I. The Child Health and Development Studies investigate a range of topics. One study considered all pregnancies between 1960 and 1967 among women in the Kaiser Foundation Health Plan in the San Francisco East Bay area. Here, we study the relationship between smoking and weight of the baby. The variable smoke is coded 1 if the mother is a smoker, and 0 if not. The summary table below shows the results of a linear regression model for predicting the average birth weight of babies, measured in ounces, based on the smoking status of the mother.^{17}
Estimate  Std. Error  t value 
Pr(>t) 


(Intercept)  123.05  0.65  189.60 
0.0000 
smoke  8.94  1.03  8.65  0.0000 
The variability within the smokers and nonsmokers are about equal and the distributions are symmetric. With these conditions satisfied, it is reasonable to apply the model. (Note that we don't need to check linearity since the predictor has only two levels.)
 (a) Write the equation of the regression line.
 (b) Interpret the slope in this context, and calculate the predicted birth weight of babies born to smoker and nonsmoker mothers.
 (c) Is there a statistically signi cant relationship between the average birth weight and smoking?
8.2 Baby weights, Part II. Exercise 8.1 introduces a data set on birth weight of babies. Another variable we consider is parity, which is 0 if the child is the first born, and 1 otherwise. The summary table below shows the results of a linear regression model for predicting the average birth weight of babies, measured in ounces, from parity.
Estimate  Std. Error  t value 
Pr(>t) 


(Intercept)  120.07  0.60  199.94 
0.0000 
smoke  1.93  1.19  1.62  0.1052 
 (a) Write the equation of the regression line.
 (b) Interpret the slope in this context, and calculate the predicted birth weight of first borns and others.
 (c) Is there a statistically signi cant relationship between the average birth weight and parity?
^{17}Child Health and Development Studies, Baby weights data set.
8.3 Baby weights, Part III. We considered the variables smoke and parity, one at a time, in modeling birth weights of babies in Exercises 8.1 and 8.2. A more realistic approach to modeling infant weights is to consider all possibly related variables at once. Other variables of interest include length of pregnancy in days (gestation), mother's age in years (age), mother's height in inches (height), and mother's pregnancy weight in pounds (weight). Below are three observations from this data set.
bwt  gestation  parity  age  height  weight  smoke  

1  120  284  0  27  62  100  0 
2  113  282  0  33  64  135  0 
\(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\) 
1236  117  297  0  38  65  129  0 
The summary table below shows the results of a regression model for predicting the average birth weight of babies based on all of the variables included in the data set.
Estimate  Std. Error  t value 
Pr(>t) 


(Intercept)  80.41  14.35  5.60 
0.0000 
gestation  0.44  0.03  15.26 
0.0000 
parity  3.33  1.13  2.95 
0.0033 
age  0.01  0.09  0.10 
0.9170 
height  1.15  0.21  5.63 
0.0000 
weight  0.05  0.03  1.99 
0.0471 
smoke  8.40  0.95  8.81 
0.0000 
 (a) Write the equation of the regression line that includes all of the variables.
 (b) Interpret the slopes of gestation and age in this context.
 (c) The coefficient for parity is different than in the linear model shown in Exercise 8.2. Why might there be a difference?
 (d) Calculate the residual for the rst observation in the data set.
 (e) The variance of the residuals is 249.28, and the variance of the birth weights of all babies in the data set is 332.57. Calculate the R^{2} and the adjusted R^{2}. Note that there are 1,236 observations in the data set.
8.4 Absenteeism. Researchers interested in the relationship between absenteeism from school and certain demographic characteristics of children collected data from 146 randomly sampled students in rural New SouthWales, Australia, in a particular school year. Below are three observations from this data set.
eth  sex  lrn 
days 

1  0  1  1  2 
2  0  1  1  11 
\(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\)  \(\vdots\) 
146  1  0  0  37 
The summary table below shows the results of a linear regression model for predicting the average number of days absent based on ethnic background (eth: 0  aboriginal, 1  not aboriginal), sex (sex: 0  female, 1  male), and learner status (lrn: 0  average learner, 1  slow learner).^{18}
Estimate  Std. Error  t value 
Pr(>t) 

(Intercept)  18.93  2.57  7.37 
0.0000 
eth  9.11  2.60  3.51 
0.0000 
sex  3.10  2.64  1.18 
0.2411 
lrn  2.15  2.65  0.81  0.4177 
(a) Write the equation of the regression line.
(b) Interpret each one of the slopes in this context.
(c) Calculate the residual for the rst observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school.
(d) The variance of the residuals is 240.57, and the variance of the number of absent days for all students in the data set is 264.17. Calculate the R^{2} and the adjusted R^{2}. Note that there are 146 observations in the data set.
8.5 GPA. A survey of 55 Duke University students asked about their GPA, number of hours
they study at night, number of nights they go out, and their gender. Summary output of the
regression model is shown below. Note that male is coded as 1.
Estimate  Std. Error  t value 
Pr(>t) 

(Intercept)  3.45  0.35  9.85 
0.00 
studyweek  0.00  0.00  0.27 
0.79 
sleepnight  0.01  0.05  0.11 
0.91 
outnight  0.05  0.05  1.01 
0.32 
gender  0.08  0.12  0.68 
0.50 
(a) Calculate a 95% con dence interval for the coefficient of gender in the model, and interpret it in the context of the data.
(b) Would you expect a 95% con dence interval for the slope of the remaining variables to include 0? Explain
^{18}W. N. Venables and B. D. Ripley. Modern Applied Statistics with S. Fourth Edition. Data can also be found in the R MASS package. New York: Springer, 2002.
8.6 Cherry trees. Timber yield is approximately equal to the volume of a tree, however, this value is difficult to measure without rst cutting the tree down. Instead, other variables, such as height and diameter, may be used to predict a tree's volume and yield. Researchers wanting to understand the relationship between these variables for black cherry trees collected data from 31 such trees in the Allegheny National Forest, Pennsylvania. Height is measured in feet, diameter in inches (at 54 inches above ground), and volume in cubic feet.^{19}
Estimate  Std. Error  t value 
Pr(>t) 

(Intercept)  57.99  8.64  6.71 
0.00 
height  0.34  0.13  2.61 
0.01 
diameter  4.71  0.26  17.82 
0.00 
(a) Calculate a 95% con dence interval for the coefficient of height, and interpret it in the context of the data.
(b) One tree in this sample is 79 feet tall, has a diameter of 11.3 inches, and is 24.2 cubic feet in volume. Determine if the model overestimates or underestimates the volume of this tree, and by how much.
Model selection
8.7 Baby weights, Part IV. Exercise 8.3 considers a model that predicts a newborn's weight using several predictors. Use the regression table below, which summarizes the model, to answer the following questions. If necessary, refer back to Exercise 8.3 for a reminder about the meaning of each variable.
Estimate  Std. Error  t value 
Pr(>t) 

(Intercept)  80.41  14.35  5.60 
0.0000 
gestation  0.44  0.03  15.26 
0.0000 
parity  3.33  1.13  2.95 
0.0033 
age  0.01  0.09  0.10 
0.9170 
height  1.15  0.21  5.63 
0.0000 
weight  0.05  0.03  1.99 
0.0471 
smoke  8.40  0.95  8.81 
0.0000 
(a) Determine which variables, if any, do not have a signi cant linear relationship with the outcome and should be candidates for removal from the model. If there is more than one such variable, indicate which one should be removed first.
(b) The summary table below shows the results of the model with the age variable removed. Determine if any other variable(s) should be removed from the model.
Estimate  Std. Error  t value 
Pr(>t) 

(Intercept)  80.64  14.04  5.74 
0.0000 
gestation  0.44  0.03  15.28 
0.0000 
parity  3.29  1.06  3.10 
0.0020 
height  1.15  0.20  5.64 
0.0000 
weight  0.05  0.03  2.00 
0.0459 
smoke  8.38  0.95  8.82 
0.0000 
^{19}D.J. Hand. A handbook of small data sets. Chapman & Hall/CRC, 1994.
8.8 Absenteeism, Part II. Exercise 8.4 considers a model that predicts the number of days absent using three predictors: ethnic background (eth), gender (sex), and learner status (lrn). Use the regression table below to answer the following questions. If necessary, refer back to Exercise 8.4 for additional details about each variable.
Estimate  Std. Error  t value 
Pr(>t) 

(Intercept)  18.93  2.57  7.37 
0.0000 
eth  9.11  2.60  3.51 
0.0000 
sex  3.10  2.64  1.18 
0.2411 
lrn  2.15  2.65  0.81 
0.4177 
(a) Determine which variables, if any, do not have a signi cant linear relationship with the outcome and should be candidates for removal from the model. If there is more than one such variable, indicate which one should be removed first.
(b) The summary table below shows the results of the regression we re t after removing learner status from the model. Determine if any other variable(s) should be removed from the model.
Estimate  Std. Error  t value 
Pr(>t) 

(Intercept)  19.98  2.22  9.01 
0.0000 
eth  9.06  2.60  3.49 
0.0006 
sex  2.78  2.60  1.07 
0.2878 
8.9 Baby weights, Part V. Exercise 8.3 provides regression output for the full model (including all explanatory variables available in the data set) for predicting birth weight of babies. In this exercise we consider a forwardselection algorithm and add variables to the model oneatatime. The table below shows the pvalue and adjusted \(R^2\) of each model where we include only the corresponding predictor. Based on this table, which variable should be added to the model first?
variable  gestation  parity  age  height  weight 
smoke 
pvalue  \(2.2\times 10^{16}\)  0.1052  0.2375  \(2.97 \times 10^{12}\)  \(8.2 \times 10^{8}\) 
\(2.2 \times 10^{16}\) 
\(R^2_{adj}\) 
0.1657  0.0013  0.0003  0.0386  0.0229 
0.0569 
8.10 Absenteeism, Part III. Exercise 8.4 provides regression output for the full model, including all explanatory variables available in the data set, for predicting the number of days absent from school. In this exercise we consider a forwardselection algorithm and add variables to the model oneatatime. The table below shows the pvalue and adjusted R^{2} of each model where we include only the corresponding predictor. Based on this table, which variable should be added to the model first?
variable  ethnicity  sex 
learner status 
pvalue  0.0007  0.3142 
0.5870 
\(R^2_{adj}\) 
0.0714  0.0001 
0 
Checking model assumptions using graphs
8.11 Baby weights, Part V. Exercise 8.7 presents a regression model for predicting the average birth weight of babies based on length of gestation, parity, height, weight, and smoking status of the mother. Determine if the model assumptions are met using the plots below. If not, describe how to proceed with the analysis.
8.12 GPA and IQ. A regression model for predicting GPA from gender and IQ was fit, and both predictors were found to be statistically signi cant. Using the plots given below, determine if this regression model is appropriate for these data.
Logistic regression
8.13 Possum classi cation, Part I. The common brushtail possum of the Australia region is a bit cuter than its distant cousin, the American opossum (see Figure 7.5 on page 318). We consider 104 brushtail possums from two regions in Australia, where the possums may be considered a random sample from the population. The rst region is Victoria, which is in the eastern half of Australia and traverses the southern coast. The second region consists of New South Wales and Queensland, which make up eastern and northeastern Australia.
We use logistic regression to differentiate between possums in these two regions. The outcome variable, called population, takes value 1 when a possum is from Victoria and 0 when it is from New South Wales or Queensland. We consider ve predictors: sex male (an indicator for a possum being male), head length, skull width, total length, and tail length. Each variable is summarized in a histogram. The full logistic regression model and a reduced model after variable selection are summarized in the table.
Full Model
Estimate  SE  Z 
Pr(>Z) 

(Intercept)  39.2349  11.5368  3.40  0.0007 
sex male  1.2376  0.6662  1.86  0.0632 
head length  0.1601  0.1386  1.16 
0.2480 
skull width  0.2012  0.1327  1.52  0.1294 
total length  0.6488  0.1531  4.24  0.0000 
tail length  1.8708  0.3741  5.00  0.0000 
Reduced Model
Estimate  SE  Z 
Pr(>Z) 

(Intercept)  33.5095  9.9053  3.38 
0.0007 
sex male  1.4207  0.6457  2.20 
0.0278 
head length  
skull width  0.2787  0.1226  2.27 
0.0231 
total length  0.5687  0.1322  4.30 
0.0000 
tail length  1.8057  0.3599  5.02  0.0000 
(a) Examine each of the predictors. Are there any outliers that are likely to have a very large inuence on the logistic regression model?
(b) The summary table for the full model indicates that at least one variable should be eliminated when using the pvalue approach for variable selection: head length. The second component of the table summarizes the reduced model following variable selection. Explain why the remaining estimates change between the two models.
8.14 Challenger disaster, Part I. On January 28, 1986, a routine launch was anticipated for the Challenger space shuttle. Seventythree seconds into the ight, disaster happened: the shuttle broke apart, killing all seven crew members on board. An investigation into the cause of the disaster focused on a critical seal called an Oring, and it is believed that damage to these Orings during a shuttle launch may be related to the ambient temperature during the launch. The table below summarizes observational data on Orings for 23 shuttle missions, where the mission order is based on the temperature at the time of the launch. Temp gives the temperature in Fahrenheit, Damaged represents the number of damaged Orings, and Undamaged represents the number of Orings that were not damaged.
Shuttle Mission  1  2  3  4  5  6  7  8  9  10  11  12 
Temperature  53  57  58  63  66  67  67  67  68  69  70 
70 
Damaged  5  1  1  1  0  0  0  0  0  0  1  0 
Undamaged  1  5  5  5  6  6  6  6  6  6  5  6 
Shuttle Mission  13  14  15  16  17  18  19  20  21  22  23 
Temperature  70  70  72  73  75  75  76  76  78  79  81 
Damaged  1  0  0  0  0  1  0  0  0  0  0 
Undamaged  5  6  6  6  6  5  6  6  6  6  6 
(a) Each column of the table above represents a different shuttle mission. Examine these data and describe what you observe with respect to the relationship between temperatures and damaged Orings.
(b) Failures have been coded as 1 for a damaged Oring and 0 for an undamaged Oring, and a logistic regression model was t to these data. A summary of this model is given below. Describe the key components of this summary table in words.
Estimate  Std. Error  z value 
Pr(>z) 

(Intercept)  11.6630  3.2963  3.54 
0.0004 
Temperature  0.2162  0.0532  4.07 
0.0000 
(c) Write out the logistic model using the point estimates of the model parameters.
(d) Based on the model, do you think concerns regarding Orings are justi ed? Explain.
8.15 Possum classi cation, Part II. A logistic regression model was proposed for classifying common brushtail possums into their two regions in Exercise 8.13. Use the results of the summary table for the reduced model presented in Exercise 8.13 for the questions below. The outcome variable took value 1 if the possum was from Victoria and 0 otherwise.
(a) Write out the form of the model. Also identify which of the following variables are positively associated (when controlling for other variables) with a possum being from Victoria: skull width, total length, and tail length.
(b) Suppose we see a brushtail possum at a zoo in the US, and a sign says the possum had been captured in the wild in Australia, but it doesn't say which part of Australia. However, the sign does indicate that the possum is male, its skull is about 63 mm wide, its tail is 37 cm long, and its total length is 83 cm. What is the reduced model's computed probability that this possum is from Victoria? How confident are you in the model's accuracy of this probability calculation?
8.16 Challenger disaster, Part II. Exercise 8.14 introduced us to Orings that were identified as a plausible explanation for the breakup of the Challenger space shuttle 73 seconds into takeo in 1986. The investigation found that the ambient temperature at the time of the shuttle launch was closely related to the damage of Orings, which are a critical component of the shuttle. See this earlier exercise if you would like to browse the original data.
(a) The data provided in the previous exercise are shown in the plot. The logistic model fit to these data may be written as
\[ log (\frac {\hat {p}}{1  \hat {p}} = 11.6630  0.2162 \times \text {Temperature}\]
where \(\hat {p}\) is the modelestimated probability that an Oring will become damaged. Use the model to calculate the probability that an Oring will become damaged at each of the following ambient temperatures: 51, 53, and 55 degrees Fahrenheit. The modelestimated probabilities for several additional ambient temperatures are provided below, where subscripts indicate the temperature:
\[\hat {p}_{57} = 0.341 \hat {p}_{59} = 0.251 \hat {p}_{61} = 0.179 \hat {p}_{63} = 0.124\]
\[\hat {p}_{65} = 0.084 \hat {p}_{67} = 0.056 \hat {p}_{69} = 0.037 \hat {p}_{71} = 0.024\]
(b) Add the modelestimated probabilities from part (a) on the plot, then connect these dots using a smooth curve to represent the modelestimated probabilities.
(c) Describe any concerns you may have regarding applying logistic regression in this application, and note any assumptions that are required to accept the model's validity.
Contributors
David M Diez (Google/YouTube), Christopher D Barr (Harvard School of Public Health), Mine ÇetinkayaRundel (Duke University)