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 25, Problem 20E

GPA and SATs A large section of Stat 101 was asked to fill out a survey on grade point average and SAT scores. A regression was run to find out how well Math and Verbal SAT scores could predict academic performance as measured by GPA. The regression was run on a computer package with the following output:

Response: GPA

Chapter 25, Problem 20E, GPA and SATs A large section of Stat 101 was asked to fill out a survey on grade point average and

  1. a) What is the regression equation?
  2. b) From this model, what is the predicted GPA of a student with an SAT Verbal score of 500 and an SAT Math score of 550?
  3. c) What else would you want to know about this regression before writing a report about the relationship between SAT scores and grade point averages? Why would these be important to know?
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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?…
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