Concept explainers
(a)
To calculate; the
(b)
To calculate; the least-squares regression line for predicting average credit score (y) from average number of late payments (x)
(c)
To construct; a residual plot for the given data
(d)
To calculate; the least-squares regression line using only those points with
(e)
To calculate; the least-squares regression line using only those points with
(f)
To calculate; the coefficient of determination using only those points with
(g)
To calculate; the coefficient of determination using only those points with
(h)
To calculate; the situation in late payments and credit score are more strongly related i.e.
Want to see the full answer?
Check out a sample textbook solutionChapter 4 Solutions
Elementary Statistics (Text Only)
- 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_forwardSales of a particular product for the years 2017 through 2020 have been 45,332, 61,076, 59,547 and 77,795 respectively. If you are going to find the sales forecast by using simple regression (least squares) method, what would be the value of (a) for the regression equation?arrow_forwardwo thousand (2,000) adults ages 50 to 80 years were recruited into a 10-year prospective cohort study which started in 1971. The purpose of the study was to examine the effect of gender on death at the end of the study (died during the study period vs survived), controlling for age, years if smoking, and occupation. What is the most appropriate statistical analysis you would conduct to answer the research question? A. Multiple linear regression B. Survival analysisC. Multiple logistic regression.D. All of the abovearrow_forward
- OLS Regression is intended to determine which variables cause the variance of a dependent variable, but regression models actually cannot differentiate between cause and effect. A. True B. Falsearrow_forward1. Considering Earnings/Event as the dependent variable (outcome), perform the multiple linear regression using Excel with normal probability plot showing the linear trendlines. Explain the R2, F-stat (Anova) and the regression coefficients. 2. Write down the linear equation for Earnings/Event.arrow_forwardWhat is the most suitable statistical test in order to determine whether weight, height, and age explain the variance in cholesterol levels of a patient? a. ANOVA b. Log-linear analysis c. Logistic Regression d. Multiple Linear Regressionarrow_forward
- The graph shows a bivariate data set and its least squares regression line. Draw the residual plot for the same data set?arrow_forwardThe consumption function captures one of the key relationships in economics. It expresses consumption as a function of disposal income, where disposable income is income after taxes. The attached file “Regression–Dataset1” shows data of average US annual consumption (in $) and disposable income (in $) for the years 2000 to 2016. a)what is the Pearson correlation value between income and consumption? Select one: a. 0.791 b. 0.109 c. None of the above d. 0.978arrow_forwardThe 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.arrow_forward
- College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage LearningBig Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin HarcourtLinear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage Learning
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillElementary Linear Algebra (MindTap Course List)AlgebraISBN:9781305658004Author:Ron LarsonPublisher:Cengage Learning