Given are five observations for two variables, and y.The estimated regression equation for these data is ŷ = 0.6 +2.8x.a. Compute SSE, SST, and SSR using the following equations (to 1 decimal).SSE = Σ (Yi – ŷ;) ²SST =Σ(Yi - y)²Σ (ŷ₁ - y)²SSR =SSESSTSSR6.4XXb. Compute the coefficient of determination 2 (to 3 decimals).XDoes this least squares line provide a good fit?Yes, the least squares line provides a very good fitc. Compute the sample correlation coefficient (to 4 decimals).0.96 XXiYi142 3 4 57 6 13 15

The estimated regression equation for given data is , y^ = 0.6+2.8x

Answered: Given are five observations for two…

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 accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a)x= 140 cm (b)x= 172 cm (c) x = 164 cm (d) x = 158 cm Click the icon to view the table of lengths and girths. The equation of the regression line is y=x+7 (Round to two decimal places as needed.). 4
Use multiple linear regression fit of the form y = a + bx₁ + cx₂ for the following data: 1.5 3 3 -1 X₁0 01 2 1 X₂ 0 1 0 1 2 1 2 3 -1 -2 -1.5 -12 -15 17 y 1644
Interpret the intercept and the coefficients of D1 and D2 in the regression above.
A student used multiple regression analysis to study how family spending (y) is influenced by income (X1), family size (x2), and additions to savings (x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained.
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
You are given the following data, where X1 (final percentage in math class) and X2 (number of absences) are used to predict Y (standardized math test score in third grade): Y X1 X2 345 70 390 80 1 370 75 4 375 92 400 82 350 70 3 310 61 5 420 80 375 88 3 410 72 1 485 99 300 65 7 Determine the following multiple regression values. Report intercept and slopes for regression equation accurate to 3 decimal places: Intercept: a = Partial slope X1: bị = Partial slope X2: bz = Report sum of squares accurate to 3 decimal places: SSreg Test the significance of the overall regression model (report F-ratio accurate to 3 decimal places and P-value accurate to 4 decimal places): F-ratio = P-value = Report the variance of the residuals accurate to 3 decimal places: Sres = Report the results for the hypothesis test for the significance of the partial slope for final percentage in math class (report the test statistic for the regression coefficients accurate to 3 decimal places and P-value accurate to…
Data from 147 colleges from 1995 to 2005 (Lee,2008) were tested to predict the endowments (in billions) to a college from the average SAT score of students attending the college. The resulting regression equation was Y = -20.46 + 4.06 (X). This regression indicates that: a. for every one-point increase in SAT scores, a college can expect 4.06 billion more in endowments. b. most colleges have very high endowments. c. for every one-point increase in SAT scores, a college can expect 20.46 billion fewer in endowments. d. for every one-dollar increase in endowments, the college can expect a half-point increase in SAT scores.
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 Test score, y (a) x = 2 hours (c) x = 14 hours (b) x = 4.5 hours (d) x = 1.5 hours 2 3 4 4 6 38 45 51 48 64 67 Find the regression equation. ŷ =x+ (O (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. A. ОВ. OD. 80- 80- Hours studying Hours studying Hours studying Hours studying (a) Predict the value of y for x = 2. Choose the correct answer below. O A. 44.5 О В. 58.8 OC. 41.7 O D. not meaningful (b) Predict the value of y for x = 4.5. Choose the correct answer below. O A. 58.8 О В. 41.7 OC.…
The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x=140 cm (b) x=172 cm (c) x=164 cm (d) x=158 cm Length, x Girth, y138 106168 130153 116144 105159 126158 119124 103137 103154 121148 111147 106145 110 The equation of the regression line is y=____x + ___
Define both x and y in all problems. x is the cause, and y is the effect. This is the most important step when doing linear regression, otherwise, all the remanding parts will be wrong.
Given the following data 5 x = 3,y = 8, Ex² = 55 , n=5, b = 2.6 i = 1 where (b) is slope of the linear regression equation V = a + bx Then, the value of 2 xy is i = 1 Select one: а. 146 b. 166 C. 450 d. 1260

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