Consider the data.14Yi76.11The estimated regression equation for these data is ŷ= 0.80 + 2.40x.(a) Compute SSE, SST, and SSR using equations SSE = E(y; - ŷ;)², sST = E(y; - y)², and SSR = E(ŷ; - 7)².SSE =SST =SSR =(b) Compute the coefficient of determination r.p2 =Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.)The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line.The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line.The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line.The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line.(c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)13O O O

We have used the excel data analysis tool to run the regression analysis.

The estimated regression equatión for these data is ý = 0.80 + 2.40x. (a) Compute SSE, SST, and SSR using equations SSE = E(y, - ŷ)?, sST = E(y, - y)?, and SSR = E(ŷ, - )?. SSE = SST = SSR = (b) Compute the coefficient of determination r2. Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares lir O The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squa O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squ O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares lin (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)

The estimated regression equatión for these data is ý = 0.80 + 2.40x. (a) Compute SSE, SST, and SSR using equations SSE = E(y, - ŷ)?, sST = E(y, - y)?, and SSR = E(ŷ, - )?. SSE = SST = SSR = (b) Compute the coefficient of determination r2. Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares lir O The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squa O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squ O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares lin (c) Compute the sample correlation coefficient. (Round your answer to three decimal places.)

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

Use the following table to find the equation of the regression line, between x and y 2 3 4 1 2 3 y 1 2 3 3 2 O ý = 2.97x - 0.455 O ý = -0.455x + 2.97 O ý = 2.122x + 0.098 O ý = 0.098x + 2.122
Compute the coefficients b₁ and b₂ for the regression model y₁ = 60+ b₁x₁₁ +b2×21 given the summary statistics shown below. a. Tx₁y b. rx₁y c. Tx₁y d. = 0.80, x2y=0.60, =200, Sx2 = 0.40, Sx₁ 0,5x1x2 =-0.80, гx2y=0.60, rx₁x2 = -0.40, Sx₁ = 200, Sx2 = 100, sy = 500 = 100, 0, Sy = 500 100, Sy = 500 =0.80, x2y = -0.20, 0, rx₁x2 = -0.10, Sx₁ =200, Sx2 = 100, Sy = 500 =0.50, rx2y=0.75, x₁x2 = 0.70, Sx₁ =200, Sx2 =
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 ý = 83.3 + 2.24x, + 1.30x2. The computer solution, based on a sample of eight weeks, provided SST 25.2 and SSR = 23.455. %D (a) Compute and interpret R² and R,. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R = 0.653 and R, = 0.595. Do you prefer the multiple regression results? Explain. %3D 2 Multiple regression analysi v ---Select--- ipreferred since both R2 and R, show ---Select--- O…
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). Which regression equation is best for predicting city fuel consumption? Why? Click the icon to view the table of regression equations. Choose the correct answer below. A. The equation CITY=6.86 -0.00131WT -0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R². B. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R². C. The equation CITY= -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R² values of equations with more predictor variables. O D. The equation CITY=6.86 -0.00131WT-0.258DISP + 0.659HWY is best because it…
Q12) If the slope of the regression equation y=b0+b1×x is equal to negative, then; 1. as x increases y decreases 2.. as x changes, y does not change 3.. Either a or b is correct 4.. as x decreases y increases
The flow rate in a device used for air quality measurement depends on the pressure drop x (inches of water) across the device's filter. Suppose that for x values between 5 and 20, these two variables are related according to the simple linear regression model with true regression line y = -0.11 + 0.097x. (a.1) What is the true average flow rate for a pressure drop of 10 in.?(a.2) A drop of 15 in.?(b) What is the true average change in flow rate associated with a 1 inch increase in pressure drop?(c) What is the average change in flow rate when pressure drop decreases by 5 in.?
A trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x₁ = distance traveled (miles) and x₂ = the number of deliveries made. Suppose that the model equation is Y = -0.800+ 0.060x₁ +0.900x₂ + e (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? hr (b) How would you interpret ₁ = 0.060, the coefficient of the predictor x₁? O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a one-mile (i.e.…
Find the multiple regression equation with weight as the response variable and the dummy variable of sex and the variable of age as the explanatory variables.
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 ý = 82.3 + 2.29x, + 1.90x2. The computer solution, based on a sample of eight weeks, provided SST = 25.1 and SSR = 23.415. (a) Compute and interpret R? and R 2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is 653 x . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R2 = 0.653 and R,2 = 0.595. Do you prefer the multiple regression results? Explain. Multiple regression analysis (is preferred since both R2 and R.2 show an increased v v…
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 - 3.17 +0.823HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2700 lb, it has an engine displacement of 1.6 L, and its highway fuel consumption is 35 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 I R² Adjusted R2 WT/DISP WT/HWY Predictor (x) Variables P-Value WT/DISP/HWY 0.000 0.942 0.000 0.748 0.000 0.942 0.000…
The table shows the number of goals allowed and the total points earned (2 polnts for a win, and 1 point for an overtme or shootout loss) by 14 lce hockey teams over the course of a season The equation of the regression line is (a) Find the coefficient of determination,, and interpret the result (b) Find the standard error of the estimate, and interpret the resut Goals Allowed, x Points, y -0.560x 218.067 Use he data to answer the folowing questions 217 213 221 229 260 262 277 205 216 20s 217 208 257 246 66 70 105 105 0 83 47106 103 97 9182 91 88 Inco (a) r-O (Round to three decimal places as noeded.)
B b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? The scatter diagram indicates a positive linear relationship between a = average number of passing yar and y = the percentage of games won by the team. c. Develop the estimated regression equation that could be used to predict the percentage of games won given the avera passing yards per attempt. Enter negative value as negative number. WinPct =| |)(Yds/Att) (to 4 decimals) d. Provide an interpretation for the slope of the estimated regression equation (to 1 decimal). The slope of the estimated regression line is approximately So, for every increase : of one yar number of passes per attempt, the percentage of games won by the team increases by %. e. For the 2011 season, the average number of passing yards per attempt for the Kansas City Chiefs was was 5.5. Use th regression equation developed in part (c) to predict the percentage of games won by the Kansas City Chiefs.…