**Exercise 15.13 Algo (Multiple Coefficient of Determination)**The following estimated regression equation is based on 30 observations:\[ \hat{y} = 18.5 + 4x_1 - 2.5x_2 + 7.6x_3 + 2.7x_4 \]The values of SST and SSR are 1,804 and 1,761, respectively.a. **Compute \( R^2 \)** (to 3 decimals).b. **Compute \( R^2_a \)** (to 3 decimals).c. **Comment on the goodness of fit.**The estimated regression equation provided an excellent fit.**Details on Graphs/Diagrams:**There are no graphs or diagrams provided in this exercise.

Given n=30, k=number of explanatory variables=4

Answered: The following estimated regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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The following estimated regression equation is based on 30 observations. ŷ = 17.7+3.8x1 2.4x2 + 7.5a3 +2.8x4 |- The values of SsST and SSR are 1,803 and 1,763, respectively. a. Compute R (to 3 decimals). b. Compute R (to 3 decimals). c. Comment on the goodness of fit. The estimated regression equation Select your answer - Hint 0-Icon Key
Given are 3 observations for two variables, x and y. 1 3 6. 9. 10 What is the estimated regression equation? Select one: a. ÿ = -2 + 6.5x O b. ý 10.89 – 0.87x с. у -0.87+ 10.89x d. ŷ 6.5 – 2x %3D
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 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 75 75 O Left Arm 174 167 181 149 147 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 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
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 sq ft. The data pertaining to 100 houses were processed using MINITAB and the following is an extract of the output obtained: The regression equation is Price = B + ¢Bedroom + yHouse Size + ALot Size Predictor Сoef SE Coef т 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 3 76501718347 25500572782 Residual Error 96 60109046053 626135896 Total 99 • Is y significantly different from -0.5? Perform the F test at the 1% level, making sure to state the null and alternative hypotheses. Give an interpretation to the term “R-sq" and comment on its value.
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 80 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 77 77 Q Left Arm 174 168 148 148 146 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is ŷ=+x. (Round to one decimal place as needed.) mm Hg. Given that the systolic blood pressure in the right arm is 80 mm Hg, the best predicted systolic blood pressure in the left arm is (Round to one decimal place as needed.) Data table Critical Values of the Pearson Correlation Coefficient r α = 0.05 α = 0.01 0.950 0.990 0.959 0.878 0.811 0.917 0.754 0.875 0.707 0.834 0.666 0.798 0.632 0.765 0.602 0.735 0.576 0.708 0.553 0.684 0.532 0.661 0.514 0.641 0.497 0.623 0.482…
The following estimated regression equation is based on 30 observations. The values of SST and SSR are 1,803 and 1,761, respectively. a. Compute R² (to 3 decimals). b. Compute R² (to 3 decimals). c. Comment on the goodness of fit. The estimated regression equatio ✔ - Select your answer - provided an excellent fit provided a moderately good fit did not provide a good fit ŷ = 17.5 + 4x1 - 2.5x2 +7.7x3 +2.5x4
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 -
In a regression analysis involving 25 observations, the following estimated regression equation was developed.ŷ = 10 – 18x1 + 3x2 + 14x3Also, the following standard errors and the sum of squares were obtained.Sb1 = 3 Sb1 = 6 Sb1 = 7SST = 4,800 SSE = 1,296The multiple coefficient of determination is _____. a. .27 b. .73 c. .33 d. .50
Compute the least-squares regression equation for the given data set. Round the slope and yFintercept to at least four decimal places. 4 y 7. 3. Send data to Excel Regression line equation: y =|
The following results were obtained from a simple linear regression analysis. Total sum of square = 5.7640. Unexplained sum of square = 0,2225. The coefficient of determination is: OA 0.0386 O B, 0.0402 OC0.9614 O D.0.9805

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