The following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts ( through (1) OA. The y-intercept of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit OB. The y-intercept of this least-squares regression line shows the speed that the ball is hit at when the distance that the ball travels is zero. OC. The y-intercept of this least-squares regression line shows the distance that the ball would travel when the speed that the ball is hit is zero OD. Interpreting the y-intercept is not appropriate. A (c) Predict the mean distance of all home runs hit at 105 mph. feet. The mean distance of all home runs hit at 105 mph is (Round to one decimal place as needed) (d) If a ball was hit with a speed of 105 miles per hour, predict how far it will travel If a ball is hit with a speed of 105 mph, the distance that it is most likely travel is feet. (Round to one decimal place as needed) (e) Christian Yelich hit a home run 398 feet. The speed at which the ball was hit was 106.2 mph. Did this ball travel farther than you would have predicted? Explain feet that would have been predicted given the speed with which the ball was hit The ball farther than the (Round to one decimal place as needed.) (f) Would you feel comfortable using the least-squares regression model on home runs where the speed of the ball was 122 mph? Explain. OA. Yes, because the least squares regression model is the most accurate way to predict the distance of all home runs hit OB. No, because the least squares regression model cannot predict the distance of a home run when the speed of the ball is outside of the scope of the model OC. Yes, because the least squares regression model can accurately predict the distance of home runs with a higher speed than was observed, but not lower OD. No, because the least squares regression model can accurately predict the distance of home runs with a lower speed than was observed, but not higher. Type here to search O Bi li 64°F Cloudy

Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 95 mm Hg. Use a significance level of 0.05 Right Arm 103 102 94 79 80 D Left Arm 176 170 144 146 145 : Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y= +x (Round to one decimal place as needed.) ater/ Enter your ahswer in the edit fields and then click Check Answer

find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. In each case, there is sujficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Altitude and Temperature Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded by the author during Delta Flight 1053 from New Orleans to Atlanta. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet).

19. You might think that increasing the resources available would elevate the number of plant spe- cies that an area could support, but the evidence suggests otherwise. The data in the accompany- ing table are from the Park Grass Experiment at Rothamsted Experimental Station in the U.K., where grassland field plots have been fertilized annually for the past 150 years (collated by Harpole and Tilman 2007). The number of plant species recorded in 10 plots is given in response to the number of different nutrient types added Plot 1 2 3 4 5 6 7 8 9 10 Number of nutrients added 0 0 0 3144 E2 3 Number of plant species 36 36 32 34 33 30 20 23 21 16

In a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.7 -0.4 -0.79 0.4

For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the American League teams in the year 2002. The variable x denotes the player payroll (in millions of dollars) for the year 2002, and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the scatter plot below, as is the least-squares regression line. The equation for this line is y = 11.43 + 0.23x. Player payroll, x (in Mean attendance, y (in $1,000,000s) thousands) Anaheim 62.8 28.52 Baltimore 56.5 33.09 40- Boston 110.2 32.72 35 Chicago White Sox 54.5 20.74 30- Cleveland 74.9 32.35 25- Detroit 54.4 18.52 Kansas City 49.4 16.30 15- Minnesota 41.3 23.70 10+ New York Yankees 133.4 42.84 Oakland 41.9 26.79 20 40 60 80 100 120 140 Seattle 86.1 43.70 Player payroll, Тarmpa Bay 34.7 13.21 X (in $1,000,000s) Техas 106.9 29.01 Toronto 66.8 20.25 Send data to calculator Send data to Excel Based on the sample data and…

Suppose the athletic director at a university would like to develop a regression model to predict the point differential for games played by the men's basketball team. A point differential is the difference between the final points scored by two competing teams. A positive differential is a win, and a negative differential is a loss. For a random sample of games, the point differential was calculated, along with the number of assists, rebounds, turnovers, and personal fouls. Use the data in the accompanying table attached below to complete parts a through e below. Assume a = 0.05.  a) Using technology, construct a regression model using all three independent variables.  y = __ + (_)x1 + (_)x2 + (_)x3 + (_)x4  b) Test the significance of each independent variable using a= 0.10.  c) interpret the p-value for each independent variable.  d) Construxt a 90% confidence interval for the regression coefficients for each independent variable and interpret the meaning.  e) Using the results from…

A recent study showed that the hours a person exercised in a week affected the individual'sresting heart rate. It was computed that r = -.68 and the least squares regression line was?̂ = 83-1.4x, where x is the hours exercised and y is the resting heart rate.   d. What percentage of variability in resting heart rate can be explained by variability inhours exercised?

Listed below are systolic blood pressure measurements​ (in mm​ Hg) obtained from the same woman. Find the regression​ equation, letting the right arm blood pressure be the predictor​ (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05.   Right Arm 103 102 96 76 76   Left Arm 174 167 149 148 148

when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?