Given are five observations for two variables, x and y.The estimated regression equation for these data is ŷ = 66.18 - 2.38x.a. Compute SSE, SST, and SSR.2 9 14 18 20XiYi 50 60 41 18 12SSE(to 2 decimals)SST(to 2 decimals)SSR(to 2 decimals)b. Compute the coefficient of determination r². Comment on the goodness of fit.(to 3 decimals)fit;The least squares line provided an goodc. Compute the sample correlation coefficient. Enter negative value as negative number.(to 3 decimals)*% of the variability in y has been explained by the estimated regression equation (to 1 decimal).

Given We'll use Excel to compute the calculation. First we will add given data in to excel

Answered: ted regression equation for these data…

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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Use the given data set to answer parts (a) and (b). a. Find the regression equation for the data points. b. Graph the regression equation and the data points. a. Find the regression equation for the data points. G (Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) X 5 y - 1 4 1 1 -1 3 2 2 4 Q
How many independent variables are involved in a multiple regression equation? a. One b. Zero c. Two or more d. At least three
r = -0.579 Calculate the coefficient of determination. (Round to three decimal places as needed.) What does this tell you about the explained variation of the data about the regression line? % of the variation can be explained by the regression line. (Round to one decimal place as needed.) About the unexplained variation? % of the variation is unexplained and is due to other factors or to sampling error. (Round to one decimal place as needed.)
Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 0.25
The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. | VIF ₂2 = VIF₁ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? OA. No. The VIF for each independent variable is less than 5. B. Yes. The VIF for each independent variable is greater than 5. OC. No. The VIF for each independent variable is greater than 5. OD. Yes. The VIF for each independent variable is less than 5. wwwwwww Table of Data Standby Total Staff Hours Present 245 330 274 358 195 197 117 153 115 275 200 236 336 339 321 303 286 329 352 323 Remote Hours 417 655 524 385 349 350 388 153 278 479 Print Done
The table below shows how far a person driving in their car was able to go over different intervals of time. Perform a regression to model the data, using the form y = ka + h. What is your value for k? (Round your answer to the nearest tenth.) Time (hours) Distance (miles) 1.2 26 2.5 57 3.1 63 4.8 114
Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 1.5.
Use the given data set to answer parts (a) and (b). a. Find the regression equation for the data points. X 5 b. Graph the regression equation and the data points. y -1 10 -10 y B. 10 -10- C. a. Find the regression equation for the data points. ŷ-0 (Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) b. Graph the regression equation and the data points. O A. 10 T -10- 1 2 -1 3 327 D. -10 4 y 4 28 0
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. The table shows the shoe size and heights (in.) for 6 men. Shoe size, x Height, y Find the regression equation. ŷ=x+ (Round to three decimal places as needed.) Choose the correct graph below. O A. Height (in.) 75+ 65- 8 Shoe size N O B. Height (in.) 75+ 65+ 8.5 65.0 Shoe size 9.5 66.0 o 11.0 67.0 O C. Height (in.) 75 65 8 Shoe size 12.0 73.0 Q 13.0 71.0 D. Height (in.) 75- 65 13.5 73.0 Shoe size Q o
Can you please help with sub-parts C and D
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=a+bx b=-0.993 a=29.135 r2=0.463761 r=-0.681 Use this to predict the number of situps a person who watches 6 hours of TV can do. Round to one decimal place.

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ted regression equation for these data is ŷ= 66.18-2.38x, e SSE, SST, and SSR. (to 2 decimals) (to 2 decimals) (to 2 decimals) e the coefficient of determination ². Comment on the goodness of fit. (to 3 decimals)