Chapter 13, Problem 13.51CRP

Using your graphing calculator, make a scatter plot of the data from the table. Then graph your model from Question 2 along with the data. How well does your model fit the data? What could you do to try to improve your model?

If you travel 100 miles in two hours, then your average speed for the trip is Average speed=_________=________

Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300

What is the slope of the line on the geoboard?

Spring is a peak time for selling houses. The file SpringHouses contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018 (realtor.com website) Click on the datafile logo to reference the data. DATA file a. The Excel output for the estimated regression equation that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house: SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression statistics Regression Residual Total Multiple R R Square ANOVA Adjusted R Square Standard Error Observations 0.7429 0.5519 0.4907 61948.6931 Regression statistics Regression Residual Total df Intercept Sq Ft Beds Lower 95% 0.9353 -145129.5298 Intercept Baths 0.9528 -49383.5243 0.0180 Sq Ft Beds 0.0326 Does the estimated regression equation provide a good fit to the data? Explain. Hint: If R is greater than 45%, the…

Professional basketball has truly become a sport that generates interest among fans around the world. More and more players come from outside the United States to play in the National Basketball Association (NBA). You want to develop a regression model to predict the number of wins achieved by each NBA team, based on field goal (shots made) percentage and three-point field goal percentage. The data are stored in NBA.xlsx .   TEAM Wins Field Goal % Three-Point Field Goal % Points Per Game Rebound Freedraw Turnover Houston Rockets 65 46 36.2 112.4 43.5 19.6 13.8 Toronto Raptors 59 47.2 35.8 111.7 44 17.3 13.4 Golden State Warriors 58 50.3 39.1 113.5 43.5 16.6 15.4 Boston Celtics 55 45 37.7 104 44.5 16 14 Philadelphia 76ers 52 47.2 36.9 109.8 47.4 17.1 16.5 Cleveland Cavaliers 50 47.6 37.2 110.9 42.1 18.1 13.7 Portland Trail Blazers 49 45.2 36.6 105.6 45.5 16.7 13.5 Indiana Pacers 48 47.2 36.9 105.6 42.3 14.9 13.3 New Orleans Pelicans 48 48.3 36.2 111.7 44.3 16.1 14.9…

Professional basketball has truly become a sport that generates interest among fans around the world. More and more players come from outside the United States to play in the National Basketball Association (NBA). You want to develop a regression model to predict the number of wins achieved by each NBA team, based on field goal (shots made) percentage and three-point field goal percentage. The data are stored in NBA.xlsx . TEAM Wins Field Goal % Three-Point Field Goal % Points Per Game Rebound Freedraw Turnover Houston Rockets 65 46 36.2 112.4 43.5 19.6 13.8 Toronto Raptors 59 47.2 35.8 111.7 44 17.3 13.4 Golden State Warriors 58 50.3 39.1 113.5 43.5 16.6 15.4 Boston Celtics 55 45 37.7 104 44.5 16 14 Philadelphia 76ers 52 47.2 36.9 109.8 47.4 17.1 16.5 Cleveland Cavaliers 50 47.6 37.2 110.9 42.1 18.1 13.7 Portland Trail Blazers 49 45.2 36.6 105.6 45.5 16.7 13.5 Indiana Pacers 48 47.2 36.9 105.6 42.3 14.9 13.3 New Orleans Pelicans 48 48.3 36.2 111.7 44.3 16.1 14.9…

A marketing consultant created a linear regression model to predict the number of units sold by a client based on the amount of money spent on marketing by the client. Which of the following is the best graphic to use to evaluate the appropriateness of the model?

A researcher is analyzing the relationship between various variables in housing data for 32 cities: median list prices of single family homes, condominium or co-ops, all homes, median household income, unemployment rate, and population. What is the regression equation for the model that predicts the list price of all homes using population as an explanatory variable?What is the interpretation of the slope? What is the interpretation of the intercept?