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TOPIC: Linear Regression
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- 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)From the below table, Use excels to find the answer for the below questions: State Percentage Proficient in Reading Percentage 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 Activities: Compute the least-squares regression line for predicting math proficiency from reading proficiency. Interpretation of the least-squares regression line in part a.The table below shows the numbers of bushels of barley cultivated per acre for 12 one-acre plots of land for two different strains of barley, PHT-34 and CBX-21. PHT-34 CBX-21 43 55 49 46 47 43 38 44 47 45 45 49 50 47 46 59 46 52 46 49 45 48 43 51 Determine the minimum data value, the quartiles, and the maximum data value for the PHT-34 and CBX-21 data sets. PHT-34 CBX-21 min Q1 Q2 Q3 max
- The following table gives retail values of a 2017 Corvette for various odometer readings. Odometer Reading Retail Value ($) 13,000 52,525 18,000 51,675 20,000 51,400 25,000 50,475 29,000 49,825 32,000 49,275 (a) Find the equation of the least-squares line for the data. (Where odometer reading is the independent variable, x, and retail value is the dependent variable. Round your numerical values to two decimal places.) ŷ = (b) Use the equation from part (a) to predict the retail price of a 2017 Corvette with an odometer reading of 30,000. Round to the nearest $100. $ (c) Find the linear correlation coefficient for these data. (Round your answer to four decimal places.) r =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…Computer output from a least-squares regression analysis based on a sample of size 17 is shown in the table. Term COEFCOEF SE CoefSE Coef TT Constant 7.43 0.59 12.59 xx 5.65 1.14 6.45 Assuming all conditions for inference are met, which of the following defines a 95 percent confidence interval for the slope of the least-squares regression line?
- Like father, like son: In 1906 , the statistician Karl Pearson measured the heights of 1078 pairs of fathers and sons. The following table presents a sample of 7 pairs, with height measured in inches, simulated from the distribution specified by Pearson. Father'sheight Son'sheight 65.4 66.0 73.6 74.9 68.3 68.3 66.7 68.8 69.1 71.8 70.7 71.0 69.3 71.4 Compute the least-squares regression line for predicting son's height ( y) from father's height (x). Round the slope and y-intercept values to at least four decimal places.The following table presents the ages of 8 U.S. presidents and their wives on the first day of their presidencies. Name Her Age His Age Barack and Michelle Obama 45 47 George W. and Laura Bush 54 54 Bill and Hillary Clinton 45 46 Ronald and Nancy Reagan 55 64 Gerald and Betty Ford 59 69 Lyndon and Lady Bird Johnson 56 61 John and Jacqueline Kennedy 31 43 Dwight and Mamie Eisenhower 50 55 Part 1 of 4 (a) Compute the least-squares regression line for predicting the president's age from the first lady's age. Round the slope and y -intercept values to at least four decimal places. Regression line equation: =y .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 sociologist is interested in the relation between x = number of job changes and y = annual salary (in thousands of dollars) for people living in the Nashville area. A random sample of 10 people employed in Nashville provided the following information. x (number of job changes) 5 3 6 6 1 5 9 10 10 3 y (Salary in $1000) 36 37 34 32 32 38 43 37 40 33 (d) If someone had x = 2 job changes, what does the least-squares line predict for y, the annual salary? (Round your answer to two decimal places.)thousand dollars thousand dollarsCompute the least-squares regression line for the given data set.