Computer output from a least-squares regression analysis based on a sample of size 17 is shown in the table. Term COEF SE Coef T Constant 7.43 0.59 12.59 x 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?

Suppose a doctor measures the height, x, and head circumference, y, of 11 children and obtains the data below. The correlation coefficient is 0.883 and the least squares regression line is y = 0.134x + 13.693. Complete parts (a) and (b) below. Height, x Head Circumference, y 17.4 27.75 25.25 26 25.25 27.25 26.75 26 27.25 27 27.25 26.75 17.1 17.3 17.0 17.4 17.2 17.2 17.3 17.3 17.4 17.3 ..... (a) Compute the coefficient of determination, R?. R? % (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. Approximately % of the variation in is explained by the least-squares gression model. (Round to one decimal place as needed.)

Two measures of a baseball player's effectiveness as a hitter are the number of hits he makes in a season and thenumber of times he "bats in" a run (knows as "Runs Batted In" or RBIs). Can we predict a batter's RBIs from hisMajor League Baseball batters in 2017.hits? Below is numerical and graphical output from a computer regression of RBIs on Hits for 12 randomly selected Major League Baseball batters in 2017.  Assume that the conditions for inference have been satisfied.(a) Do these data provide convincing evidence that there is a linear relationship between RBIs and Flits for MajorLeague Baseball batters in 2017? (b) Construct a 95% confidence interval for the slope of the population regression line for predicting RBIs from Hits.

A local real estate agent argues that house prices in Pittsburgh are related tothe number of rooms in a house. You collect a random sample of 190 houses and run asimple regression using House Price (measured in dollars) as the dependent variableand Rooms (measured as the number of rooms in a house) as the independentvariable. Given below is the Excel output of the regression results. (a) Construct the 99.7% confidence interval for the slope of the regressionline.(b) Consider a house with 5 room. What is the probability that the housewill sell for more than $270,245?(c) A savvy real estate investor, Noah, owns a house in Pittsburgh thathe is planning to sell. Based on the regression put, Noah decides to destroy somewalls to reduce the number of rooms in the house, hoping to increase the houseselling price. Based on the regression output, do you think this is a good decision?Justify your comment.

Data on 12 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. ski 85 2.5. treadmill = Coefficients Estimate (Intercept) Treadmill 85 - 2.5 Std. Error What is the test statistic? -2.5 0.45 1 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? Round your answers to three decimal places. Using your answer from the previous question, find the p-value. Part 2 o

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In this question, we investigate the relationship between the top (maximum) speed (mph) and maximum height for a random sample of roller coasters in the United States. Here is the scatterplot and a summary of the simple linear regression model.   Construct a 95% confidence interval for the true slope of the regression line and interpret your confidence interval in context.

Use the following information from a multiple regression analysis. n=15 b1=4 b2=5 Sb1=1.3 Sb2=0.7 a. Which variable has the largest​ slope, in units of a t​ statistic? b. Construct a 95​% confidence interval estimate of the population​ slope, β1. c. At the 0.05 level of​ significance, determine whether each independent variable makes a significant contribution to the regression model. On the basis of these​ results, indicate the independent variables to include in this model.

Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.858 and the least squares regression line is y = 0.228x +11.187. Complete parts (a) and (b) below. Height, x 27.5 25.75 26.5 25.5 27.25 26.25 25.75 27.25 27 27.25 27 Head Circumference, y 17.4 17.2 17.2 16.9 17.6 17.1 17.1 17.4 17.4 17.3 17.3 (a) Compute the coefficient of determination, R². R² =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination and comment on the adequacy of the linear model. Approximately % of the variation in (Round to one decimal place as needed.) is explained by the least-squares regression model. According to the residual plot, the linear model appears to be

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