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 18, Problem 27E

Speed of light In 1882, Michelson measured the speed of light (usually denoted c as in Einstein’s famous equation E = mc2). His values are in km/sec and have 299,000 subtracted from them. He reported the results of 23 trials with a mean of 756.22 and a standard deviation of 107.12.

  1. a) Find a 95% confidence interval for the true speed of light from these statistics.
  2. b) State in words what this interval means. Keep in mind that the speed of light is a physical constant that, as far as we know, has a value that is true throughout the universe.
  3. c) What assumptions must you make in order to use your method?
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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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Intro STATS, Books a la Carte Plus New Mystatlab with Pearson Etext -- Access Card Package

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