The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi'gal). The predictor (x) variables are WT (weight in pounds). DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why? E Click the icon to view the table of regression equations. The best variable is because it has the best combination of V P-value,. and adjusted R?. (Type integers or decimals. Do not round.)

The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi'gal). The predictor (x) variables are WT (weight in pounds). DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why? E Click the icon to view the table of regression equations. The best variable is because it has the best combination of V P-value,. and adjusted R?. (Type integers or decimals. Do not round.)

Name: #20 R2 Adjusted R?Predictor (x) Variables P-ValueWT/DISP/HWYWT/DISPWT/
Uploaded: 2024-03-05T11:52:21.000Z
Channel: Bartleby
Description: #20 R2 Adjusted R?Predictor (x) Variables P-ValueWT/DISP/HWYWT/DISPWT/HWYDISP/HWYRegression EquationCITY = 6.82 - 0.00126WT - 0.259DISP +0.657HWYCITY = 37.6-0.00157WT - 1.33DISPCITY = 6.72 -0.00157WT +0.674HWY0.0000.9430.9330.0000.7470.9430.9350.7120

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?
ANSWER THE FOLLOWING QUESTION.
The average midterm score in a large statistics class was 60 with an SD of 5. The average final score in the same class was 80 with an SD of 15. The correlation coefficient between midterm and final scores was r=0.6. Using the regression line, we predict the final score of a student with a midterm score of 70 to be but this prediction is likely to be off by about Fill in the blanks, rounding each answer to one decimal point.
The table below gives the number of hours five randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. hours studying 0 1 3 4 5 grades 63 65 80 81 87 Find the value of the coefficient of determination. Round your answer to three decimal places.
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x,) and newspaper advertising (x,). The estimated regression equation was ŷ = 82.1 + 2.23x, + 1.70x2. The computer solution, based on a sample of eight weeks, provided SST = 25.1 and SSR = 23.345. (a) Compute and interpret R2 and R_2. (Round your answers to three decimal places.) . Adjusting for the number of independent variables in the model, the The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is
A random sample of some of the heaviest carnivores on Earth was reviewed to determine if there is an association between the length (in meters) and weight (in kilograms) of these carnivores. Here are the scatter plot, the residuals plot, a histogram of the residuals, and the regression analysis of the data. Use this information to analyze the association between the length and weight of these carnivores. Hgram the ete Rasiduala Vera the Fited Vl Sertf htLeu The regression equation is Weight = - 668 + 485 Length Coef SE Coef 232.8 90.63 Predictor P - 668.3 -2.87 0.024 5.35 0.001 Constant Length 485.21 S = 194.794 R-Sq = 80.4% R-Sq (adj) = 77.6% Perform a significance test and state your conclusion in context. O The population is the random sample of carnivores - the parameter is the relationship between height and weights for the sample of carnivores The population is carni carnivores parameter is the relationship between height and weight for Ho: ? Ha: ? Choose the conditions which…
You have gathered data from a random sample of fast-food sandwiches in order to better understand how the amount of fat in these sandwiches relates to the amount of carbohydrates in the sandwiches. Your ultimate goal is to construct a regression equation to predict amount of carbohydrates based on amount of fat. If this is your goal, which variable should you put on the vertical axis (or y-axis) of a scatterplot of this data? O When conducting a regression analysis, it makes no difference which variable is on which axis. O Amount of fat, because it is the explanatory variable. O Amount of carbohydrates, because it is the explanatory variable. Amount of carbohydrates, because it is the response variable. O Amount of fat, because it is the response variable.
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 2 2.5 3 3.5 4 4.5 5 Midterm Grades 72 74 80 82 87 88 93 Find the estimated slope. Round your answer to three decimal places.
A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using two independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. With this regression model, the predicted price of a 10-year old home with 2,500 square feet of living area is __________. $205.00 $200,000.00 $205,000.00 $255,000.00
The table below gives the number of hours seven randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Studying 2 2.5 3 3.5 4 4.5 5 Midterm Grades 72 74 80 82 87 88 93 Determine if the statement "Not all points predicted by the linear model fall on the same line" is true or false.
The correlation between two variables x and y is –0.6. If we used a regression line to predict y using x, what percent of the variation in y would be explained?