The following estimated regression equation is based on 30 observations.The values of SST (Total Sum of Squares) and SSR (Regression Sum of Squares) are 1,805 and 1,755, respectively.a. Compute \( R^2 \) (to 3 decimals). [Input field for answer]b. Compute \( R^2_{\text{adj}} \) (to 3 decimals). [Input field for answer]c. Comment on the goodness of fit.The estimated regression equation: \( \hat{y} = 17.8 + 4x_1 - 2.4x_2 + 7.4x_3 + 2.8x_4 \)[Dropdown menu to select an answer]

As per given regression equation, there are 4 independent variables. The sample size is 30. Total…

Answered: The following estimated regression…

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 regression equation is: ŷ = 67.16 + 8.417x where ŷ is the miles traveled, and x is the MPG. The sample size used was all 110 MPG records. The correlation coefficient r = 0.620. Use the information to obtain an estimate of my mileage if my MPG is 22. Is it option: a.) cannot estimate ŷ rcrit = 0.195; the correlation IS NOT significant b.) ŷ = 252.33 rcrit = 0.195; the correlation IS significant c.) ŷ = 252.33 rcrit = 0.187; the correlation IS significant d.) cannot estimate ŷ rcrit = 0.187; the correlation IS NOT significant
consider the following points (1.3,2.4) (2.5,2.4) (3.3,2) (4.2,1.8) (5.1,1.6) find the equation of the regression line (round the values 2 decimal placed.
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. 170 480 Calories, x Sodium, y 560 Find the regression equation. ŷ=x+ (Round to three decimal places as needed.) Choose the correct graph below. OA. 0ff 0 200 150 430 Calories 130 320 a 130 380 O B. 560 0 80 250 200 G Calories (a) Predict the value of y for x = 160. Choose the correct answer below. OA. 390.863 OB. 440.983 OC. 591.343 O D. not meaningful (b) Predict the value of y for x = 90. Choose the correct answer below. 190 510 (a) x (c) x 160 calories 140 calories O C. A 560+ 0-T 0 200 Calories (b) x = 90 calories (d) x = 220 calories OD. 560- 0 200 Calories Q
8
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Predictor Coef SE Coef t-ratio Constant Arm span -7.611 2.567 2.965 0.046 0.186 0.035 5.377 0.000 S = 1.61 R-Sq = 63.0% R-Sq (Adj) = 64.9% Which of the following is the best interpretation of the standard deviation of the residuals? The typical arm span is 161 centimeters. The typical foot length is 16.1 centimeters. The typical distance between the observed and predicted arm spans is 1.61 centimeters. The typical distance between the observed and predicted foot lengths is 1.61 centimeters. Op
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. Calories, x Sodium, y 130 380 80 270 (a) x= 150 calories (c) x-120 calories 190 160 415 180 465 130 350 (b) X3D90 calories (d) x= 60 calories 540 (a) Predict the value of y for x= 150. Choose the correct answer below. O A. 212.451 B. 347.151 C. 414.501 O D. not meaningful (b) Predict the value of y for x= 90. Choose the correct answer below. O A. 212.451 O B. 347.151 es OC. 279.801
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
The following estimated regression equation is based on 30 observations. ŷ= 17.5 +3.91 -2.12 +7.523 +2.824 The values of SST and SSR are 1,808 and 1,761, respectively. a. Compute R2 (to 3 decimals). b. Compute R (to 3 decimals). c. Comment on the goodness of fit. The estimated regression equation Select your answer -
For the given data below, Solve, a. Find the regression equation, y = b0 + b1x.b. If x = 170 orders, how many shipments are expected?
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 58 inches. Is the result close to the actual weight of 662 pounds? Use a significance level of 0.05. Chest size (Inches) 46 57 53 41 40 40 Weight (Pounds) 384 580 542 358 306 320
Bluereef real estate agent wants to form a relationship between the prices of houses, how many bedrooms, House size in sq ft and Lot Size in sg ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract: The regression equation is Price = B + ÞBedroom + yHouse Size + ALot Size Predictor Coef SE Coef T P Constant 37718 14177 2.66 ** Bedrooms 2306 6994 0.33 0.742 House Size 74.3 52.98 0.164 Lot Size -4.36 17.02 -0.26 0.798 s= 25023 R-Sq=56.0% R-Sq(adj)=54.6% Source DF MS F P Regression Residual 3 76501718347 25500572782 **** Error 96 60109046053 626135896 Total 99 1. Fill in the missing values *, **, and **** 2. Use the p-value approach to determine if o is significant at the 5% significance level

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