30f8 -/6 i: 5 Part 3 ((2) Does the intercept make sense in this context? 0 Yes O No 4 of 8 - 14 E . . . Use the computer output to estimate the intercept So and the slope B1 . The regression equation is Y = 803 - 4.86X. Predictor Coef SE Coef T P Constant 802.566 87.24 9.20 0.000 X -4.859 1.593 -3.05 0.006 Intercept Po : Slope B1 :4 of 8 - 14 E . . . Use the computer output to estimate the intercept So and the slope B1 . The regression equation is Y = 803 - 4.86X. Predictor Coef SE Coef T P Constant 802.566 87.24 9.20 0.000 X -4.859 1.593 -3.05 0.006 Intercept Po : Slope B1 :With which of the foll does the correlation, r 120 500 100 BO 300 200 100 20 5 15 20 5 19 15 (a) (b) O Scatterplot (a) O Scatterplot (b) O Scatterplot (c) O Scatterplot (d)\f6of8 L Ch "- II to. NBA Players: Correlation Matrix The dataset NBAPIayersZOlS contains information on many variables for players in the NBA (National Basketball Association) during the 201415 season. The dataset includes information for all players who averaged more than 24 minutes per game (n = 182) and 25 variables, includingAge, Points (number of points for the season per game), FTPct (free throw shooting percentage), Rebounds (number of rebounds for the season), and Steals (number of steals for the season). A correlation matrix for these five variables is shown. A correlation matrix allows us to see lots of correlations at once, between many pairs of variables. For any pair of variables (indicated by the row and the column), we are given two values: the correlation as the top number and the pvalue for a twotail test of the correlation right beneath it. Correlations: Age, Points, FTPct, Rebounds, Gaels Age Points FTPct Rel: Part 2 (b) Interpret the slope in context. 0 Given a one hour increase in study time, expected change in Grade is 3.8. 0 Given a one point increase in Grade, expected change in Study time is 3.8 hours. 0 Given a one hour increase in study time, expected grade will be 3.8 . 5 of 8 > - 14 E . . . Ine regression equation Is Y = 84.9 - 0.0132X. edictor Coef SE Coef T P onstant 84.91 12.18 6.97 0.000 -0.01317 0.01098 -1.20 0.245 Sample slope: p-value: HI Does X appear to be an effective predictor of the response variable Y? Yes C No\f50f8 -/4 i: 5 Computer output for tting a simple linear model is given below. State the value of the sample slope for this model and give the null and alternative hypotheses for testing if the slope in the population is different from zero. Identify the pvalue and use it (and a 5% signicance level) to make a clear conclusion about the effectiveness of the model. The regression equation is Y = 84.9 - 0.0132X. Predictor Coef SE Coef T P Constant 84.91 12.18 6.97 0.0( X -0.01317 0.01098 -1.20 0.24 Sample slope: pvalue: (5 V I II .0 30f8