b) for this answer, the number of square lines must be mentioned in order to get the solution
Given are five observations for two variables, x and y. xi Yi 1 4 2 7...
Given are five observations for two variables, and y. 1 2 3 5 Yi 3 7 5 11 14 The estimated regression equation for these data is ý = 0.2 +2.6z. a. Compute SSE, SST, and SSR using the following equations (to 1 decimal). SSE = Sy.- SST = = (y - SSR = = (y - SSE SST SSR b. Compute the coefficient of determination (to 3 decimals). Does this least squares line provide a good fit? Yes, the...
Given are five observations for two variables, x and y. xi 4 8 14 16 18 yi 58 52 45 24 11 The estimated regression equation for these data is y= 75.06 - 3.09x. A. Compute SSE, SST, and SSR using the following equations (to 2 decimal). B.Compute the coefficient of determination r2 (to 3 decimals). The least squares line provided an (good/bad) fit; ---------% of the variability in y has been explained by the estimated regression equation (to 1...
Check My Work (2 remaining) eBook Video Given are five observations for two variables, and y 4 7813 15 The estimated regression equation for these data is-1+2.8z. a. Compute SSE, SST, and SSR using the following equations (to 1 decimal). (y,-y? SST SSE SST SSR b. Compute the coefficient of determination 2 (to 3 decimals) Does this least squares line provide a good fit? Select your answer C. Compute the sample correlation coefficient (to 4 decimals) Check My Work (2...
Given are five observations for two variables, z and y. 12 3 4 5 37 7 13 14 The estimated regression equation for these data is 0.4 +2.8. a. Compute SSE, SST, and SSR using the following equations (to 1 decimal). SST E(,- SSE SST SSR b. Compute the coefficient of determination 2 (to 3 decimals). Does this least squares line provide a good fit? No, the least squares line does not produce much of a fit c. Compute the...
Given are five observations for two variables, x and y. 4 8 12 16 18 yi 58 51 48 14 15 The estimated regression equation for these data is y= 76.77 - 3.41x a. Compute SSE, SST, and SSR (to 2 SSE decimals) (to 2 SST decimals) (to 2 SSR decimals) b. Compute the coefficient of determination r. Comment on the goodness of fit (to 3 decimals) % of the variability in y has been explained by the estimated regression...
Makeup for an Active Learning Activity with an excused absence E I MINDTAP Video eBook Given are five observations for two variables, z and y. 1 2 3 4 5 v 4 7 6 12 14 The estimated regression equation for these data is y 1.1+2.5x. a. Compute SSE, SST, and SSR using the following equations (to 1 decimal). SSE SST SSR b. Compute the coefficient of determination r2 (to 3 decimals). Does this least squares line provide a good...
The data from exercise 1 follow.The estimated regression equation for these data is yˆ = .20 + 2.60x.Compute SSE, SST, and SSR using the following equations (14.8), (14.9), and (14.10) (to 1 decimal if necessary).SSESSTSSRCompute the coefficient of determination r2 (to 3 decimals).Does this least squares line provide a good fit?Compute the sample correlation coefficient (to 4 decimals).
15. {Exercise 12.23 (Algorithmic)} Given are five observations for two variables, x and y. xi 1 2 3 4 5 yi 3 8 6 12 14 The estimated regression equation is ŷ = 0.8 + 2.6x. Compute the mean square error using the following equation (to 3 decimals). Compute the standard error of the estimate using the following equation (to 3 decimals). Compute the estimated standard deviation b1 using the following equation (to 3 decimals). Use the t test to...
A sales manager collected the following data on x = years of experience and y = annual sales ($1,000s). The estimated regression equation for these data is ý = 81 + 4x. Salesperson Years of Experience Annual Sales ($1,000s) 1 107 103 101 119 8 9 10 10 11 13 123 127 136 (a) Compute SST, SSR, and SSE. SST = SSR = SSE = (b) Compute the coefficient of determination 2. (Round your answer to three decimal places.) 12...
Given are five observations for two variables, x and y. xi 1 2 3 4 5 yi 3 8 5 10 14 (d) Develop the estimated regression equation by computing the values of b0 and b1 using b1 = Σ(xi − x)(yi − y) Σ(xi − x)2 and b0 = y − b1x. ŷ = (e) Use the estimated regression equation to predict the value of y when x = 2.