Statistics: The Art and Science of Learning from Data (4th Edition)
4th Edition
ISBN: 9780321997838
Author: Alan Agresti, Christine A. Franklin, Bernhard Klingenberg
Publisher: PEARSON
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Question
Chapter 14, Problem 71CP
a.
To determine
Find the number of groups result from different combination of the three factors.
b.
To determine
Find an indicator variable for regression model.
c.
To determine
Find the possible estimates of the model parameter in such a way that estimated corn yield is high when nitrogen and phosphate are at high level and potash is at the low level.
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Statistics: The Art and Science of Learning from Data (4th Edition)
Ch. 14.1 - Hotel satisfaction The CEO of a company that owns...Ch. 14.1 - Prob. 2PBCh. 14.1 - Whats the best way to learn French? The following...Ch. 14.1 - Prob. 4PBCh. 14.1 - Prob. 5PBCh. 14.1 - ANOVA and box plots For two studies, each...Ch. 14.1 - Prob. 7PBCh. 14.1 - Smoking and personality A study about smoking and...Ch. 14.1 - Prob. 9PBCh. 14.1 - Prob. 10PB
Ch. 14.1 - Prob. 11PBCh. 14.2 - House prices and age For the House Selling Prices...Ch. 14.2 - Time on Facebook Do freshmen spent significantly...Ch. 14.2 - Prob. 14PBCh. 14.2 - Tukey holding time comparisons Refer to the...Ch. 14.2 - Prob. 16PBCh. 14.2 - REM regression Refer to the previous exercise. a....Ch. 14.2 - Prob. 18PBCh. 14.2 - Regression for outsourcing Refer to the previous...Ch. 14.2 - Advertising effect oil sales Each of 100...Ch. 14.3 - Reducing cholesterol An experiment randomly...Ch. 14.3 - Prob. 24PBCh. 14.3 - Political ideology in 2014 The GSS measures...Ch. 14.3 - Prob. 26PBCh. 14.3 - Corn and manure In Example 10, the coefficient of...Ch. 14.3 - Prob. 28PBCh. 14.3 - Regression for telephone holding times Refer to...Ch. 14.3 - Prob. 30PBCh. 14.3 - Income by gender and degree In 2012, the...Ch. 14.3 - Prob. 32PBCh. 14.3 - Attractiveness and getting dates The results in...Ch. 14.3 - Prob. 34PBCh. 14.3 - Regression of weight gain on diet Refer to the...Ch. 14 - Good friends and marital status Is the number of...Ch. 14 - Prob. 37CPCh. 14 - Singles watch more TV The 2014 General Social...Ch. 14 - Prob. 39CPCh. 14 - Prob. 40CPCh. 14 - Prob. 41CPCh. 14 - Prob. 42CPCh. 14 - Prob. 43CPCh. 14 - Comparing therapies for anorexia The Anorexia data...Ch. 14 - Prob. 45CPCh. 14 - Prob. 46CPCh. 14 - Prob. 47CPCh. 14 - Prob. 48CPCh. 14 - Prob. 49CPCh. 14 - Prob. 50CPCh. 14 - Prob. 51CPCh. 14 - TV watching by gender and race When we use the...Ch. 14 - Prob. 53CPCh. 14 - Prob. 54CPCh. 14 - Prob. 55CPCh. 14 - Prob. 56CPCh. 14 - Prob. 57CPCh. 14 - Prob. 59CPCh. 14 - Prob. 60CPCh. 14 - Prob. 61CPCh. 14 - Prob. 62CPCh. 14 - Prob. 63CPCh. 14 - Prob. 64CPCh. 14 - Prob. 65CPCh. 14 - Prob. 66CPCh. 14 - Prob. 67CPCh. 14 - Prob. 68CPCh. 14 - Prob. 69CPCh. 14 - Prob. 70CPCh. 14 - Prob. 71CP
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forwardUrban Travel Times Population of cities and driving times are related, as shown in the accompanying table, which shows the 1960 population N, in thousands, for several cities, together with the average time T, in minutes, sent by residents driving to work. City Population N Driving time T Los Angeles 6489 16.8 Pittsburgh 1804 12.6 Washington 1808 14.3 Hutchinson 38 6.1 Nashville 347 10.8 Tallahassee 48 7.3 An analysis of these data, along with data from 17 other cities in the United States and Canada, led to a power model of average driving time as a function of population. a Construct a power model of driving time in minutes as a function of population measured in thousands b Is average driving time in Pittsburgh more or less than would be expected from its population? c If you wish to move to a smaller city to reduce your average driving time to work by 25, how much smaller should the city be?arrow_forward
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