Intro STATS, Books a la Carte Plus New Mystatlab with Pearson Etext -- Access Card Package
Intro STATS, Books a la Carte Plus New Mystatlab with Pearson Etext -- Access Card Package
4th Edition
ISBN: 9780321869852
Author: Richard D. De Veaux
Publisher: PEARSON
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Chapter 22, Problem 7E

Human births, last time For the data in Exercise 1,

  1. a) Compute the standardized residual for each season.
  2. b) Are any of these particularly large? (Compared to what?)
  3. c) Why should you have anticipated the answer to part b?
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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 22 Solutions

Intro STATS, Books a la Carte Plus New Mystatlab with Pearson Etext -- Access Card Package

Ch. 22 - Bank cards At a major credit card bank, the...Ch. 22 - Prob. 3ECh. 22 - Prob. 4ECh. 22 - Customer ages An analyst at a local bank wonders...Ch. 22 - Bank cards, once more A market researcher working...Ch. 22 - Human births, last time For the data in Exercise...Ch. 22 - Prob. 8ECh. 22 - Prob. 9ECh. 22 - Prob. 10ECh. 22 - Prob. 11ECh. 22 - Prob. 12ECh. 22 - Dice After getting trounced by your little brother...Ch. 22 - MMs As noted in an earlier chapter, Mars Inc. says...Ch. 22 - Nuts A company says its premium mixture of nuts...Ch. 22 - Prob. 16ECh. 22 - NYPD and race Census data for New York City...Ch. 22 - Violence against women In its study When Men...Ch. 22 - Fruit flies Offspring of certain fruit flies may...Ch. 22 - Prob. 20ECh. 22 - Prob. 21ECh. 22 - Lottery numbers The fairness of the South African...Ch. 22 - Prob. 23ECh. 22 - Prob. 24ECh. 22 - Childbirth, part 2 In Exercise 23, the table shows...Ch. 22 - Prob. 26ECh. 22 - Prob. 27ECh. 22 - Prob. 28ECh. 22 - Prob. 29ECh. 22 - Does your doctor know? (part 4) In Exercises 24,...Ch. 22 - Prob. 31ECh. 22 - Prob. 32ECh. 22 - 33. Titanic Here is a table we first saw in...Ch. 22 - NYPD The table below shows the rank attained by...Ch. 22 - Prob. 35ECh. 22 - NYPD again Examine and comment on this table of...Ch. 22 - Cranberry juice Its common folk wisdom that...Ch. 22 - Prob. 38ECh. 22 - Montana A poll conducted by the University of...Ch. 22 - 40. Fish diet Medical researchers followed 6272...Ch. 22 - Prob. 41ECh. 22 - Working parents In April 2009, Gallup published...Ch. 22 - Grades Two different professors teach an...Ch. 22 - Full moon Some people believe that a full moon...Ch. 22 - Grades, again In some situations where the...Ch. 22 - Full moon, next phase In Exercise 44, you found...Ch. 22 - Racial steering A subtle form of racial...Ch. 22 - 48. Titanic, redux Newspaper headlines at the...Ch. 22 - Prob. 49ECh. 22 - Prob. 50ECh. 22 - Prob. 51ECh. 22 - Education by age Use the survey results in the...
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