### Regression Analysis SummaryThis table presents the results of multiple regression analyses using different predictor variables to model the target variable "CITY."| Predictor (x) Variables | P-Value | R² | Adjusted R² | Regression Equation ||-------------------------|---------|------|-------------|------------------------------------------------------------|| WT/DISP/HWY | 0.000 | 0.942| 0.932 | CITY = 6.88 - 0.00129WT - 0.253DISP + 0.651HWY || WT/DISP | 0.000 | 0.747| 0.719 | CITY = 37.7 - 0.00161WT - 1.27DISP || WT/HWY | 0.000 | 0.942| 0.936 | CITY = 6.67 - 0.00161WT + 0.669HWY || DISP/HWY | 0.000 | 0.935| 0.928 | CITY = 1.81 - 0.627DISP + 0.705HWY || WT | 0.000 | 0.712| 0.697 | CITY = 42.2 - 0.00670WT || DISP | 0.000 | 0.658| 0.640 | CITY = 29.4 - 2.98DISP || HWY | 0.000 | 0.924| 0.920 | CITY = - 3.15 + 0.818HWY |### Explanation of Metrics:- **P-Value**: Indicates the statistical significance of the model. A P-value of 0.000 suggests high significance.- **R² (R-Squared)**: Represents the proportion of the variance for the target variable that's explained by the predictors. Higher values indicate a better fit.- **Adjusted R²**: Adjusts the R² value based on the number of predictors, providing a more accurate measure in models with multiple variables.- **Regression Equation**: Shows the mathematical relationship between the predictors and the target variable, CITY. The coefficients of the predictors indicate their respective impact.### Observations:1. **WT/DISP/HWY 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). The equation CITY = −3.15 + 0.818(HWY) was previously determined to be the best for predicting city fuel consumption. A car weighs 2790 lb, it has an engine displacement of 1.4 L, and its highway fuel consumption is 37 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate?[Table icon] Click the icon to view the table of regression equations.The best predicted value of the city fuel consumption is [Text Box] [Dropdown].(Type an integer or a decimal. Do not round.)The predicted value [Dropdown] likely to be a good estimate and [Dropdown] likely to be very accurate because [Text Box].

GivenFor WT= 2790 lbsDISP =1.4 L HWY=37ml/galSo in order to calculate the predicted value of the fuel consumption we will consider the following regression equation is:CITY = -3.15 +0.818HWYCITY=-3.15 + 0.818 × 37CITY=27.116 Therefore the best predicted value of the fuel consumption is 27.0137 ml/gal The predicted value is likely to be a good estimate and less likely to be very accurate because the adjusted R square is maximum in equation 3 that is CITY=667-0.00161WT+0.669HWY which shows that on adding a variable the fitness of model increases

Math

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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). The equation CITY = - 3.15 + 0.818HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2790 lb, it has an engine displacement of 1.4 L, and its highway fuel consumption is 37 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? is that predicted value likely to be very accurate? E Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) The predicted value V likely to be a good estimate and V likely to be very accurate because

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). The equation CITY = - 3.15 + 0.818HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2790 lb, it has an engine displacement of 1.4 L, and its highway fuel consumption is 37 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? is that predicted value likely to be very accurate? E Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) The predicted value V likely to be a good estimate and V likely to be very accurate because

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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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). The equation CITY = -3.14 +0.816HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2740 lb, it has an engine displacement of 1.5 L, and its highway fuel consumption is 40 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. Regression Table The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) Predictor (x) Variables WT/DISP/HWY WT/DISP WT/HWY DISP/HWY WT DISP HWY P-Value R² 0.000 0.943 0.000 0.748 0.000 0.942…
A regression was run to determine if there is a relationship between hours of tv watched per day (x) and number of sit-ups a person can do (y)
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 85 mm Hg. Use a significance level of 0.05. Right Arm 100 99 91 76 76 5 Left Arm 175 170 146 147 146 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.) Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
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The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 4141 inches. Is the result close to the actual weight of 273273 pounds? Use a significance level of 0.05. Chest size (inches) 4040 5353 3838 4343 4444 5858 Weight (pounds) 227227 360360 153153 206206 234234 414414 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. Question content area bottom Part 1 What is the regression equation? ModifyingAbove y with caretyequals=enter your response hereplus+enter your response herex (Round to one decimal place as needed.) Part 2 What is the best predicted weight of a bear with a chest size of 4141 inches? The best predicted weight for a bear with a chest size of 4141 inches is enter your response here pounds. (Round to one…
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). The equation CITY = -3.14 +0.818HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2760 lb, it has an engine displacement of 2.2 L, and its highway fuel consumption is 34 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.) Regression Table Predictor (x) Variables P-Value R² Adjusted R² WT/DISP/HWY WT/DISP WT/HWY DISP/HWY 0.000 0.943 0.000 0.747 0.000 0.941…
Given that the systolic blood pressure in the right arm is 90 mm Hg, the best systolic blood pressure in the left arm is how many mm Hg?
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The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 48 inches. Is the result close to the actual weight of 538 pounds? Use a significance level of 0.05.
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