Elementary Statistics
Elementary Statistics
12th Edition
ISBN: 9780321836960
Author: Mario F. Triola
Publisher: PEARSON
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Chapter 12, Problem 10CRE

Lottery: Goodness-of-Fit The bars in the histogram included with Exercise 9 depict these frequencies: 21, 19, 15, 18, 24, 18,16,24,30, and 15. Test the claim that the digits are selected from a population in which the digits arc all equally likely. Is there a problem with the lottery?

9. Lottery: Interpreting a Graph Shown below is a histogram of digits selected in California’s Win 4 lottery. Each drawing involves the random selection (with replacement) of four digits between 0 and 9 inclusive.

  1. a. If the lottery works correctly, what should be the shape of the histogram in the long run? Does the histogram shown here depict the expected distribution?
  2. b. Does the display depict a normal distribution? Why or why not?

Chapter 12, Problem 10CRE, Lottery: Goodness-of-Fit The bars in the histogram included with Exercise 9 depict these

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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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