Monthly Income Monthly Rent 24 7.0 16 4.5 19 6.5 31 11.2 12 4.5 27 8.5 The null hypothesis is that the slope of the population regression line of monthly rent on monthly income is zero and the alternative hypothesis is that the slope of this population regression line is greater than zero. The significance level is 1%. What is the value of the test statistic, t, rounded to three decimal places?

Monthly Income Monthly Rent 24 7.0 16 4.5 19 6.5 31 11.2 12 4.5 27 8.5 The null hypothesis is that the slope of the population regression line of monthly rent on monthly income is zero and the alternative hypothesis is that the slope of this population regression line is greater than zero. The significance level is 1%. What is the value of the test statistic, t, rounded to three decimal places?

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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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?
A normal quantile plot of the residuals from this regression model should look like 1. Randomly scattered noise (watermelon seeds) 2. A normal distribution 3. A parabola 4. A uniform distribution 5. A line 6. Symmetric
The numbers of insured commercial banks y (in thousands) in the united states for the years 1987 to 1996 are shown in the table beloL (Source: Federal Deposit Insurance Corporation) Year 1987 1988 1989 1990 1991 1992 1994 1995 1996 Number 13.70 (1000's) 13.12 12.71 12.34 11.92 12.27 12.51 12.68 12.99 28 a) What regression model would fit this data (1linear, quadratic, cubic)? Explain. b) Use technology to determine the regression equation for this data. Round your values to the nearest thousandth. c) What is the independent variable in this problem? What is the dependent variable? Explain. d) Use your regression equation to predict the number of insured commercial banks in the US in the year 2015. e) Was the process you used in question (d) above considered Interpolation or extrapolation? Explain. f) Use your regression equation to predict the calendar year in which you would expect there to be 50,000 insured commercial banks.
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8) The following results are from a regression where the dependent variable is GRADUATION RATE and the independent variables are % OF CLASSES UNDER 20, % OF CLASSES OF 50 OR MORE, STUDENT/FACULTY RATIO, ACCEPTANCE RATE, 1ST YEAR STUDENTS IN TOP 10% OF HS CLASS. *see below* Below is the regression of % OF CLASSES UNDER 20 on the other independent variables. *see below* a) Calculate the VIF for % OF CLASSES UNDER 20. b) Based on the VIF, do you think that multicollinearity is a problem? Explain.
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The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) to (c) below. ..... (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for females. Find the least-squares regression line for males. y=x+O Data for licensed drivers by age and gender. (Round the slope to three decimal places and round the constant to the nearest integer as needed.) Find the least-squares regression line for females. x + Number of Number of Number of Male Fatal Number of Female Fatal (Round the slope to three decimal places and round the constant to the nearest integer as needed.) Licensed Drivers Crashes Licensed Drivers Crashes (b) Interpret the slope of the least-squares regression line for each gender, if appropriate. How might an insurance…