Concept explainers
Ii. Problem 13 in Chapter 15 examined the relationship between weight and income for a sample of
a. Find the regression equation for predicting income from weight. (Identify the weight scores as X values and the income scores as Y values.)
b. What percentage of the variance in the income is accounted for by the regression equation? (Compute the
c. Does the regression equation account for a significant portion of the variance in income? Use
The researchers cited in the previous problem also examined the weight/salary relationship for men and found a positive relationship, suggesting that we have very different standards for men than for women (Judge & Cable, 2010). The following are data similar to those obtained for a sample of male professionals. Again, weight relative to height is coded in five categories from 1 = thinnest to 5 = heaviest. Income is recorded as thousands earned annually.
a. Calculate the Person correlation for these data.
b. Is the correlation statistically significant? Use a two-tailed test with
X |
Want to see the full answer?
Check out a sample textbook solutionChapter 16 Solutions
Statistics for The Behavioral Sciences (MindTap Course List)
- Olympic 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_forwardThe following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.arrow_forwardQuestion 2 Personal wealth tends to increase with age as older individuals have had more opportunities to earn and invest than younger individuals. The following data were obtained from a random sample of eight individuals and records their total wealth (Y) and their current age (X). Person Total wealth (‘000s of dollars) Age (Years) Y X A 280 36 B 450 72 C 250 48 D 320 51 E 470 80 F 250 40 G 330 55 H 430 72 A part of the output of a regression analysis of Y against X using Excel is given below: SUMMARY OUTPUT Regression Statistics Multiple R 0.954704 R Square 0.91146 Adjusted R Square 0.896703 Standard Error 28.98954…arrow_forward
- Question 16 Regression analysis was applied between sales (in $1000s) and advertising (in $100s), and the following regression function was obtained. = 500 + 4x Based on the above estimated regression line, if advertising is $10,000, then the point estimate for sales (in dollars) is _____. $505,000 $900 $40,500 $900,000arrow_forwardProblem 1: The regression below relates earnings to years of experience for a sample of 30 working adults. wage = 15.6 + 1.4*exp Predictor Coef SE Coef T Constant 15.6 2.38 6.55 Exp 1.40 .60 2.33 where wage equals hourly wage rate ($/hour) and exp equals years of work experience a)According to the above regression results, by how much does a year of experience increase the hourly wage rate? b)For a = .05, test the hypothesis that increases in experience are associated with increases in earnings. Clearly state the null and alternative hypotheses, show all relevant statistics for performing the test, and report the conclusion of your test. c)For a = .05 test the hypothesis that a 1year increase in experience increases the wage rate by more than $1/hour. Clearly state the null and alternative hypotheses, show all relevant statistics for performing the test, and report the conclusion of your test. d)…arrow_forwardThe operations manager of a musical instrument distributor feels that the demand for Bass Drums may be related to the number of television appearances by the popular rick group Green Shades during the previous month. The manager has collected the data shown in the following table. Demand for Bass Drums 3 6 7 5 10 8 Green Shades TV appearances 3 4 7 6 8 5 Develop the linear regression equation to forecast. Forecast demand for Bass Drums when Green Shades’ TV appearances are 10. Compute MSE and standard deviation for Problem 8.arrow_forward
- question 26 What is the relationship between the number of minutes per day a woman spends talking on the phone and the woman's weight? The time on the phone and weight for 8 women are shown in the table below. Time 54 88 82 61 39 40 84 83 Pounds 149 198 184 166 142 140 170 163 The equation of the linear regression line is: ˆyy^ = ?+ x (Please show your answers to 3 decimal places) Use the model to predict the weight of a woman who spends 50 minutes on the phone.Weight = ? (Please round your answer to the nearest whole number.) Interpret the slope of the regression line in the context of the question: For every additional minute women spend on the phone, they tend to weigh on averge 0.87 additional pounds. As x goes up, y goes up. The slope has no practical meaning since you cannot predict a women's weight. Interpret the y-intercept in the context of the question: The y-intercept has no practical meaning for this study. The average woman's weight is…arrow_forwardQUESTION 16 A set of n = 20 pairs of X and Y scores has SS X = 10, SS Y = 40, and SP = 30. What is the slope for the regression equation for predicting Y from X? a. b = 0.25 b. b = 3.33 c. b = 4.00 d. b = 0.33arrow_forward
- College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningFunctions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning