For the following data:Y38.86.341447Find the regression equation for predicting Y from X.ΣΧSSxΣΥΣΧΥSPUse the regression equation to find a predicted Y for each X.X Y38826.246.4473.

From the given information we find the solution.

Answered: the following data: Y 8 8. 2 2 4 6 1 4…

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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given: X(i): 3, 12, 6, 20, 14 Y(i): 55, 40, 55, 10, 5 a. construct a scatter diagram b. develop the estimated regression equation computing the values of b(0) and b(1) d. use the estimated regression equation to predict the value of y when x = 10
4 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 pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x 0 2 2 4 4 5 Test score, y Find the regression equation. y=x+ X+ C Test score 80- (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. O A. O B. 0- 0 8 Hours studying 40 43 50 49 62 67 OA. 97.5 OB. 55.2 O C. 60.1 P Test score D. not meaningful 80- 0- 0 8 Hours studying O C. Test score (a) x = 3 hours (c) x = 12 hours 80- (a) Predict the value of y for x = 3. Choose the correct answer below. 0- 0 8 Hours studying O D. OA. 55.2 OB. 52.7 O C. 60.1 O D. not meaningful (b) Predict the value of y…
Listed below is the multiple regression equation for predicting Y by X₁ through Xs. Y is the number of sales per month. X₁ is the number of sales calls made X2 is the number of hours on the showroom floor X3 is the amount spent on radio ads X is the number of text messages sent Xs is the number of email messages sent Regression Statistics R Square 0.634 Standard Error 7.6796 Observations 45 Intercept X₁ X₂ X3 XA Xs Coefficients Standard Error 6.8163 0.1005 -0.3207 0.2084 0.1221 0.0943 77.1211 1.9439 -11.123 0.1144 11.34 1.23 What is the computed value of the F statistic you would use to test the significance of the entire model? Round your answer to two decimal places.
Height, x 772 628 518 508 496 483 stories, y 51 48 44 43 37 35 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 pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. (a) x = 498 feet (b) x = 640 feet (c) x = 345 feet (d) 735 feet Find the regression equation.Now construct a scatter plot of the data and draw the regression line. (a) Now use the regression equation to predict the value of y for each of the given x-values, if meaningful. Because the correlation between x and y is significant, the equation of the regression line can be used to predict y-values. However, prediction values are meaningful only for x-values in the range of the data. Begin with x = 498. Since x = 498…
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The following table shows unit sales of iPhones for the given year. t = years since 2010 0 1 2 3 I – units sold in millions 8 16 37 47 Find the equation of the regression line. ________________________ How many units sold does the regression line predict for 2015? ________________________ Explain in practical terms the meaning of the slope.
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Use the computer output to estimate the intercept β0 and the slope β1.The regression equation is Y⁢=825-3.12X. Predictor Coef SE Coef T P Constant 825.405 89.72 9.20 0.000 X -3.119 1.023 -3.05 0.006 Intercept β0: Slope β1: Click if you would like to Show Work for this question: Open Show Work
EQuestion Help 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 pair of variables have a significant correlation) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x IA Test score,y 3 (a) x=2 hours (c) x= 12 hours (b) x = 25 hours (d) x= 4.5 hours 38 44 50 47 61 68 Find the regression equation. y = 6.269 x+ (32.48) (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. OD. OB. OC. O A. 80- 80- 80 80-4 0-+ Hours studying Hours studying Hours studying Hours studying 10 18 PM (E) More 10/3/2020 Click to select your answer(s)
uestion Completion Status: P QUESTION 8 Refer to the data in the table below. −3 -2 -1.5 0 0.5 2 2.5 -1 -1.5 0.5 0 0.5 2.5 y 2 3 Palja: 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 = 7. For the toolbar, press ALT+F10 (PC) or ALT+FN+F 10 (Mac). BIUS Paragraph Arial X Et P Click Save and Submit to save and submit. Click Save All Answers to save all answers. a 10pt DELL 3 4 3 !
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 pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x 1 2 2 Test score, y 39 45 50 48 3 5 6 62 72 (a) x = 2 hours (c) x = 15 hours (b) x = 3.5 hours (d) x = 4.5 hours Find the regression equation. y = x+ y= (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)

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the following data: Y 8 8. 2 2 4 6 1 4 4 7 1 the regression equation for predicting Y from X. ΣΧ SSx ΣΥ ΣΧΥ SP the regression equation to find a predicted Y for each X. Y 8 8 6. 6