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The survey on student attitude toward e-books described in Exercise 3 .5 was conducted in 2011. A similar survey was also conducted in 2012 (The Chronicle of Higher Education, August 23, 2013). Data from 1588 students who participated in the 2012 survey are summarized in the accompanying table.
- a. Use these data and the data from Exercise 3 .5 to construct a comparative bar chart that shows the distribution of responses for the two years. (Hint: See Example 3 .1.)
- b. Based on your graph from part (a) do you think there was much of a change in attitude toward e-books from 2011 to 2012?
3.5 College student attitudes about e-books were investigated in a survey of 1625 students. Students were asked to indicate their level of agreement with the following statement:
“I would like to be able to get all my textbooks in digital form.”
The responses are summarized in the accompanying table. (The Chronicle of Higher Education, August 23, 2013)
- a. Construct an appropriate graphical display to summarize the information given in the table.
- b. Write a headline that would be appropriate for a newspaper article that summarized the results of this survey.
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Chapter 3 Solutions
Introduction To Statistics And Data Analysis
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- Name: Lab Time: Quiz 7 & 8 (Take Home) - due Wednesday, Feb. 26 Contingency Analysis (Ch. 9) In lab 5, part 3, you will create a mosaic plot and conducted a chi-square contingency test to evaluate whether elderly patients who did not stop walking to talk (vs. those who did stop) were more likely to suffer a fall in the next six months. I have tabulated the data below. Answer the questions below. Please show your calculations on this or a separate sheet. Did not stop walking to talk Stopped walking to talk Totals Suffered a fall Did not suffer a fall Totals 12 11 23 2 35 37 14 14 46 60 Quiz 7: 1. (2 pts) Compute the odds of falling for each group. Compute the odds ratio for those who did not stop walking vs. those who did stop walking. Interpret your result verbally.arrow_forwardSolve please and thank you!arrow_forward7. In a 2011 article, M. Radelet and G. Pierce reported a logistic prediction equation for the death penalty verdicts in North Carolina. Let Y denote whether a subject convicted of murder received the death penalty (1=yes), for the defendant's race h (h1, black; h = 2, white), victim's race i (i = 1, black; i = 2, white), and number of additional factors j (j = 0, 1, 2). For the model logit[P(Y = 1)] = a + ß₁₂ + By + B²², they reported = -5.26, D â BD = 0, BD = 0.17, BY = 0, BY = 0.91, B = 0, B = 2.02, B = 3.98. (a) Estimate the probability of receiving the death penalty for the group most likely to receive it. [4 pts] (b) If, instead, parameters used constraints 3D = BY = 35 = 0, report the esti- mates. [3 pts] h (c) If, instead, parameters used constraints Σ₁ = Σ₁ BY = Σ; B = 0, report the estimates. [3 pts] Hint the probabilities, odds and odds ratios do not change with constraints.arrow_forward
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