Statistics: The Art and Science of Learning from Data (4th Edition)
Statistics: The Art and Science of Learning from Data (4th Edition)
4th Edition
ISBN: 9780321997838
Author: Alan Agresti, Christine A. Franklin, Bernhard Klingenberg
Publisher: PEARSON
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Chapter 13, Problem 64CP
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Explain the difference in the slope of poverty, which is 28.33 for the two-predictor model and 14.95 after adding the third predictor to the model.

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(c) Because logistic regression predicts probabilities of outcomes, observations used to build a logistic regression model need not be independent. A. false: all observations must be independent B. true C. false: only observations with the same outcome need to be independent I ANSWERED: A. false: all observations must be independent.  (This was marked wrong but I have no idea why. Isn't this a basic assumption of logistic regression)
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Spam filters are built on principles similar to those used in logistic regression. We fit a probability that each message is spam or not spam. We have several variables for each email. Here are a few: to_multiple=1 if there are multiple recipients, winner=1 if the word 'winner' appears in the subject line, format=1 if the email is poorly formatted, re_subj=1 if "re" appears in the subject line. A logistic model was fit to a dataset with the following output:   Estimate SE Z Pr(>|Z|) (Intercept) -0.8161 0.086 -9.4895 0 to_multiple -2.5651 0.3052 -8.4047 0 winner 1.5801 0.3156 5.0067 0 format -0.1528 0.1136 -1.3451 0.1786 re_subj -2.8401 0.363 -7.824 0 (a) Write down the model using the coefficients from the model fit.log_odds(spam) = -0.8161 + -2.5651 + to_multiple  + 1.5801 winner + -0.1528 format + -2.8401 re_subj(b) Suppose we have an observation where to_multiple=0, winner=1, format=0, and re_subj=0. What is the predicted probability that this message is spam?…

Chapter 13 Solutions

Statistics: The Art and Science of Learning from Data (4th Edition)

Ch. 13.1 - Used cars The following data (also available from...Ch. 13.2 - Predicting sports attendance Keeneland Racetrack...Ch. 13.2 - Predicting weight Lets use multiple regression to...Ch. 13.2 - Prob. 14PBCh. 13.2 - Price of used cars For the 19 used cars listed in...Ch. 13.2 - Prob. 16PBCh. 13.2 - Softball data For the Softball data set on the...Ch. 13.2 - Slopes, correlations, and units In Example 2 on y...Ch. 13.2 - Predicting college GPA Using software with the...Ch. 13.3 - Predicting GPA For the 59 observations in the...Ch. 13.3 - Study time help GPA? Refer to the previous...Ch. 13.3 - Variability in college GPA Refer to the previous...Ch. 13.3 - Does leg press help predict body strength? Chapter...Ch. 13.3 - Prob. 24PBCh. 13.3 - Interpret strength variability Refer to the...Ch. 13.3 - Any predictive power? Refer to the previous three...Ch. 13.3 - Predicting pizza revenue Aunt Ermas Pizza...Ch. 13.3 - Prob. 28PBCh. 13.3 - Mental health again Refer to the previous...Ch. 13.3 - Prob. 30PBCh. 13.3 - House prices Use software to do further analyses...Ch. 13.4 - Body weight residuals Examples 47 used multiple...Ch. 13.4 - Strength residuals In Chapter 12, we analyzed...Ch. 13.4 - Prob. 34PBCh. 13.4 - Nonlinear effects of age Suppose you fit a...Ch. 13.4 - Prob. 36PBCh. 13.4 - Why inspect residuals? When we use multiple...Ch. 13.4 - College athletes The College Athletes data set on...Ch. 13.4 - House prices Use software with the House Selling...Ch. 13.4 - Prob. 40PBCh. 13.5 - U.S. and foreign used cars Refer to the used car...Ch. 13.5 - Prob. 42PBCh. 13.5 - Predict using house size and condition For the...Ch. 13.5 - Quality and productivity The table shows data from...Ch. 13.5 - Predicting hamburger sales A chain restaurant that...Ch. 13.5 - Prob. 46PBCh. 13.5 - House size and garage interact? Refer to the...Ch. 13.5 - Prob. 48PBCh. 13.5 - Comparing sales You own a gift shop that has a...Ch. 13.6 - Prob. 50PBCh. 13.6 - Prob. 51PBCh. 13.6 - Prob. 52PBCh. 13.6 - Prob. 53PBCh. 13.6 - Prob. 54PBCh. 13.6 - Prob. 55PBCh. 13.6 - Prob. 56PBCh. 13.6 - Prob. 57PBCh. 13.6 - Prob. 58PBCh. 13.6 - Prob. 59PBCh. 13 - House prices This chapter has considered many...Ch. 13 - Prob. 61CPCh. 13 - Prob. 62CPCh. 13 - Prob. 63CPCh. 13 - Prob. 64CPCh. 13 - Prob. 65CPCh. 13 - Prob. 66CPCh. 13 - Prob. 67CPCh. 13 - Prob. 68CPCh. 13 - Prob. 69CPCh. 13 - AIDS and AZT In a study (reported in the New York...Ch. 13 - Factors affecting first home purchase The table...Ch. 13 - Unemployment and GDP Refer to Exercise 13.67. When...Ch. 13 - Prob. 75CPCh. 13 - Prob. 76CPCh. 13 - Prob. 77CPCh. 13 - Prob. 78CPCh. 13 - Prob. 79CPCh. 13 - True or false: Slopes For data on y = college GPA,...Ch. 13 - Prob. 81CPCh. 13 - Lurking variable Give an example of three...Ch. 13 - Prob. 83CPCh. 13 - Prob. 84CPCh. 13 - Prob. 85CPCh. 13 - Logistic versus linear For binary response...Ch. 13 - Prob. 87CPCh. 13 - Prob. 88CPCh. 13 - Prob. 89CPCh. 13 - Prob. 90CPCh. 13 - Prob. 91CPCh. 13 - Prob. 92CPCh. 13 - Prob. 93CP
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