For demonstration, suppose we had the following data regarding the number of pass completions X and the number of passing yards Y for six Ottawa Redblacks players in a recent CFL season: Player # 1 3 4 5 6 Completions 11 32 5 147 55 101 Passing Yards 178 444 15 1612 531 1832 After finding the least-squares regression line regressing pass yards on pass completions, what is the residual for player #1? 2.
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- Suppose a doctor measures the height, x, and head circumference, y, of 11 children and obtains the data below. The correlation coefficient is 0.904 and the least squares regression line is y=0.208x+11.736. Complete parts (a) and (b) below.A scatterplot of student height, in inches, versus corresponding arm span length, in inches, is shown below. One of the points in the graph is labeled A. If the point labeled A is removed, which of the following statements would be true? The slope of the least squares regression line is unchanged and the correlation coefficient increases. The slope of the least squares regression line is unchanged and the correlation coefficient decreases. The slope of the least squares regression line increases and the correlation coefficient increases. The slope of the least squares regression line increases and the correlation coefficient decreases. The slope of the least squares regression line decreases and the correlation coefficient increases.Is a baseball players' slugging percentage correlated to their strikeout percentage? A random sample of n=6n=6professional baseball players gave the following data (Source: baseball-reference.com) Slugging 0.396 0.42 0.323 0.078 0.473 0.467 Strikeouts 27 14.3 30.8 47.1 17.8 36.7 Find the least squares line if we consider slugging percengtage as the explanatory variable and strikeout percentage as the response variable. (Round the y-intercept and slope to 2 decimal places.)y^ = For a unit increase in slugging percentage, how much of a decrease Correct in strikeout percentage is predicted? (Round your answer to 2 decimal places.) What percentage of the variation in strikeout percentage (yy) can be explained by slugging percentage (xx) and the least squares line? (Round to the nearest percent.) p-value (Round to four decimal places)
- The following table presents the percentage of students who tested proficient in reading and the percentage who tested proficient in math for each of the ten most populous states in the United States. State Percent Proficient in Reading Percent Proficient in Mathematics California 60 59 Texas 73 78 New York 75 70 Florida 66 68 Illinois 75 70 Pennsylvania 79 77 Ohio 79 76 Michigan 73 66 Georgia 67 64 North Carolina 71 73 Compute the least-squares regression line for predicting math proficiency from reading proficiency. Predict the math proficiency for another state with reading proficiency of 63 percent.An article included a summary of findings regarding the use of SAT I scores, SAT II scores, and high school grade point average (GPA) to predict first-year college GPA. The article states that "among these, SAT II scores are the best predictor, explaining 17 percent of the variance in first-year college grades. GPA was second at 15.2 percent, and SAT I was last at 13.1 percent." If the data from this study were used to fit a least squares line with y = first-year college GPA and x = high school GPA, what would the value of r2 have been? R2 ____________________%A regional distributor of NIKE shoes is in the process of analyzing the factors that influence thedemand for the NIKE brand. The distributor hired an economist to conduct a study on the demand for this product. The economist collected quarterly time series data from 1986Q1 to 1991Q4 on the following variables:SALES Sales of NIKE shoesRPDI Real personal disposal incomeCONF Consumer confidence indexD2 Dummy variable for quarter 2D3 Dummy variable for quarter 3D4 Dummy variable for quarter 4Ordinary Least Squares was applied using sales as the dependent variable and real personal disposal income, consumer confidence index, dummy variable for quarter 2, dummy variable for quarter 3, and dummy variable for quarter 4 as independent variables. The table below shows the OLS output.Model 1: OLS, using observations 1986:1-1991:4 (T = 24)Dependent variable: SALESCoefficient Std. Error t-ratio p-valueconst −139.452 61.8421 −2.255 0.0368 **RPDI 1.56286 0.438492 3.564 0.0022 ***CONF 0.256247…
- A regional distributor of NIKE shoes is in the process of analyzing the factors that influence thedemand for the NIKE brand. The distributor hired an economist to conduct a study on the demand for this product. The economist collected quarterly time series data from 1986Q1 to 1991Q4 on the following variables:SALES Sales of NIKE shoesRPDI Real personal disposal incomeCONF Consumer confidence indexD2 Dummy variable for quarter 2D3 Dummy variable for quarter 3D4 Dummy variable for quarter 4Ordinary Least Squares was applied using sales as the dependent variable and real personal disposal income, consumer confidence index, dummy variable for quarter 2, dummy variable for quarter 3, and dummy variable for quarter 4 as independent variables. The table below shows the OLS output.Model 1: OLS, using observations 1986:1-1991:4 (T = 24)Dependent variable: SALESCoefficient Std. Error t-ratio p-valueconst −139.452 61.8421 −2.255 0.0368 **RPDI 1.56286 0.438492 3.564 0.0022 ***CONF 0.256247…A suburban hotel derives its revenue from its hotel and restaurant operations. Theowners are interested in the relationship between the number of rooms occupied on anightly basis and the revenue per day in the restaurant. Below is a sample of 25 days(Monday through Thursday) from last year showing the restaurant income and numberof rooms occupied.Compute the least-squares regression line for the given data set.
- 1. An article included a summary of findings regarding the use of SAT I scores, SAT II scores, and high school grade point average (GPA) to predict first-year college GPA. The article states that "among these, SAT II scores are the best predictor, explaining 17 percent of the variance in first-year college grades. GPA was second at 15.3 percent, and SAT I was last at 13.6 percent." If the data from this study were used to fit a least squares line with y = first-year college GPA and x = high school GPA, what would the value of r2 have been? r2 =_______ 2. A study was carried out to investigate the relationship between the hardness of molded plastic (y, in Brinell units) and the amount of time elapsed since the plastic was molded (x, in hours). Summary quantities include n = 15, SSResid = 1,237.628, and SSTo = 24,619.737. Calculate and interpret the coefficient of determination. (Round the coefficient of determination to four decimal places when written as a decimal and 2 decimal…Consider a regression analysis with n = 47 and three potential independent variables. Suppose that one of the independent variables has a correlation of 0.95 with the dependent variable. Does this imply that this independent variable will have a very large Student’s t statistic in the regression analysis with all three predictor variables?Consider the fitted values from a simple linear regression model with intercept: yˆ = 7 + 4x. Assume that the total number of observations is n = 20. In addition, the explained sum of squares is SSE = 10 and the residual sum of squares is SSR = 30. Under the classical Gauss-Markov assumptions, a) What is the value of the R2 ?