O Regression TablePredictor (x) Variables P-ValueWT/DISP/HWYR2Adjusted R2Regression Equation0.0000.9430.933CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWYWT/DISP0.0000.7490.721CITY = 38.4 - 0.00161WT - 1.28DISPWT/HWY0.0000.9420.936CITY = 6.65 - 0.00161WT + 0.675HWYDISP/HWY0.0000.9350.928CITY = 1.84 - 0.626DISP +0.707HWYWT0.0000.7120.697CITY = 41.8 - 0.00602WTDISP0.0000.6590.641CITY = 28.8 - 3.03DISPHWY0.0000.9250.921CITY = - 3.14 + 0.823HWYPrintDone 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 (enginedisplacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why?E Click the icon to view the table of regression equations.Choose the correct answer below.O A. The equation CITY = 6.65 - 0.00161WT + 0.675HWY is best because it has a low P-value and the highest adjusted value of R2.O B. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it has a low P-value and the highest value of R?.OC. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it uses all of the available predictor variables.O D. The equation CITY = - 3.14 + 0.823HWY is best because it has a low P-value and its R2 and adjusted R? values are comparable to the R2 and adjusted R? values of equations with more predictor variables.

Name: O Regression TablePredictor (x) Variables P-ValueWT/DISP/HWYR2Adjuste
Uploaded: 2024-03-20T06:38:44.000Z
Channel: Bartleby
Description: O Regression TablePredictor (x) Variables P-ValueWT/DISP/HWYR2Adjusted R2Regression Equation0.0000.9430.933CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWYWT/DISP0.0000.7490.721CITY = 38.4 - 0.00161WT - 1.28DISPWT/HWY0.0000.9420.936CITY = 6.65 - 0.001

The equation City = -3.14 + 0.823HWY is best because its p-value is significant, also both R2 and…

Math

Statistics

R2 Adjusted R² Regression Equation CITY = 6.83 – 0.00132WT – 0.253DISP + 0.654HWY CITY = 38.4 – 0.00161WT – 1.28DISP Predictor (x) Variables P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 WT/DISP/HWY 0.943 0.933 WT/DISP 0.749 0.721 WT/HWY DISP/HWY 0.942 0.936 CITY = 6.65 - 0.00161WT + 0.675HWY CITY = 1.84 – 0.626DISP + 0.707HWY CITY = 41.8 - 0.00602WT 0.935 0.928 WT 0.712 0.697 0.641 0.921 DISP 0.659 CITY = 28.8 - 3.03DISP HWY 0.925 CITY = - 3.14 + 0.823HWY Print Done

R2 Adjusted R² Regression Equation CITY = 6.83 – 0.00132WT – 0.253DISP + 0.654HWY CITY = 38.4 – 0.00161WT – 1.28DISP Predictor (x) Variables P-Value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 WT/DISP/HWY 0.943 0.933 WT/DISP 0.749 0.721 WT/HWY DISP/HWY 0.942 0.936 CITY = 6.65 - 0.00161WT + 0.675HWY CITY = 1.84 – 0.626DISP + 0.707HWY CITY = 41.8 - 0.00602WT 0.935 0.928 WT 0.712 0.697 0.641 0.921 DISP 0.659 CITY = 28.8 - 3.03DISP HWY 0.925 CITY = - 3.14 + 0.823HWY Print Done

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Two variables are defined, a regression equation is given, and one data point is given. Hgt = height in inches Age = age in years of a child Hgt = 24.5+2.78(Age) The data point is a child 12 years old who is 60 inches tall. X Your answer is incorrect. (a) Find the predicted value for the data point and compute the residual. Enter the exact answers. Predicted value = i Residual = inches inches
A regression coefficient is given Match the correct equation with answers given below Y =0.00114x + 2.53 Y=1.662x + 83.34 Y = -1.04x + 50.3 Y= -0.667x + 52.6
Let x be the size of a house (in square feet) and y be the amount of natural gas used (therms) during a specified period. Suppose that for a particular community, x and y are related according to the simple linear regression model with the following values. ? = slope of population regression line = 0.016 ? = y intercept of population regression line = −7 Question: Graph the population regression line by first finding the point on the line corresponding to x = 1,000 and then the point corresponding to x = 2,000, and drawing a line through these points.
Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1
From the results shown above, write the regression equation.
B1
Find the equation of the regression line for the given data
The estimated regression equation for a model involving two independent variables and 10 observations follows. ý = 22.1370 + 0.5303Xq + 0.4920X2 (a) Interpret b₁ in this estimated regression equation. O b₁ = 0.5303 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₁ = 0.5303 is an estimate of the change in y corresponding to a 1 unit change in x₁ when X₂ is held constant. O b₁ = 22.1370 is an estimate of the change in y corresponding to a 1 unit change in x₁ when x₂ is held constant. O b₁ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₂ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₁ when x₂ is held constant. Interpret b₂ in this estimated regression equation. O b₂ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₂ = 22.1370 is an estimate of the change in y corresponding to a…
Question 10. What is the expected value of the elasticity of education on wages for a person ith 15 years of education at its expected wage? (a) 1.5 (b) 150 160 15.15 (c) 150 (d) 1 (e) 160 150
Q10. price based on two features. Suppose we are going to apply multivariate Linear Regression to predict the house x1 = age of the house (0 – 40) x2 = quality of neighbouring schools (0 – 3) h(8) = 0, + 8,x1 + 02x2 Suggest one way to scale the features. Describe what will happen if feature scaling is not performed before the training process is conducted.