Stats: Data And Models Plus Mylab Statistics With Pearson Etext -- Access Card Package (5th Edition)
Stats: Data And Models Plus Mylab Statistics With Pearson Etext -- Access Card Package (5th Edition)
5th Edition
ISBN: 9780135256213
Author: Richard D. De Veaux, Paul F. Velleman, David E. Bock
Publisher: PEARSON
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Chapter 25, Problem 16E

a.

To determine

State the null and alternative hypotheses.

b.

To determine

Obtain the conclusion from the ANOVA table.

c.

To determine

Identify whether the assumptions for the test are reasonable.

d.

To determine

Test the difference using a two-sample pooled t-test.

Find the P-value.

Show that the square of the t-statistic is equal to the F-ratio.

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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?…
Consider an event X comprised of three outcomes whose probabilities are 9/18​, 1/18​,and 6/18. Compute the probability of the complement of the event. Question content area bottom Part 1 A.1/2 B.2/18 C.16/18  D.16/3
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