Suppose that n= 195 I.I.d. observations for (Y, X;) yleld the following regression results:v= 32.68 + 67.55Xx, SER= 18.63, R = 0.84(15.7) (12.5)Another researcher is interested in the same regression, but he makes an error when he enters the data into the regression: He enters each observation twice, so he has 390 observations (with observation 1 entered twice, observation 2 enteredtwice, and so forth).Which of the following eatimated parameters changeresult? (Check all that apply)O A. The estimated intercept and slope.O B.The R of the regression.Yc. The standard error of the regrussion (SER).YD. The standard errors of the estimated coefficients.Using the 390 observations, what results will be produced by his regression program?Ý= 32.68 + 67.55X, SER =.R = 0.84(Round your responses to two decimal places)

The regression line used to predict dependent or response variable based on the effect of…

Answered: Suppose that n= 195 I.L.d. observations…

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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In the following case find the best predicted value of y given the following: x = 2.00, r = -0.123 (p-value = 0.62), y (bar)= 8.00, n = 30 and the equation of the regression line is: y (hat) = 7.00 - 2.00x
The following estimated regression equation based on 10 observations was presented. = 29.1240 + 0.5906x, + 0.4580x2 The values of SST and SSR are 6,721.125 and 6,215.375, respectively. (a) Find SSE. SSE = (b) Compute R2. (Round your answer to three decimal places.) R2 = (c) Compute R. (Round your answer to three decimal places.) R. %3D (d) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) The estimated regression equation provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation did not provide a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation provided a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. The estimated regression equation did not provide a good fit as…
Using the same set of data as questions 4 and 5, what would be the "a" coefficient be if we force the regression through the origin (0,0)? Use four significant digits. Y 4.99 22.19 1.96 9.89 2.98 11 9. 40.46 4.04 18.93 6.06 25 0.88 0.19 8.02 34.02 6.97 28.03
The following regression model was fitted to sample data with 12 observations: = 30 +4.50x. What is the residual for an observation (x = 2, y = 40)? 0.50 -1 1 -0.50
Complete the missing information for this regression model. Note: N = 26. |X3 Ý = 31.85 + 2.071X1 3.183X2 + 2.325 0.874 2.387 1.868 Standard Errors 13.699 2.37 t-ratios 0.034 P-values (Except for P-values, report all values occurote to 3 decimal places. For P-values, report accurate to 4 decimal places.)
The following estimated regression equation based on 10 observations was presented. ŷ= 29.1230+ 0.5806x + 0.4380x2 The values of SST and SSR are 6,725.125 and 6,223.375, respectively. (a) Find SSE. SSE= (b) Compute R2. (Round your answer to three decimal places.) R² = (c) Compute R. (Round your answer to three decimal places.) R2= (d) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The estimated regression equation did not provide a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation did not provide a good fit as a small proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation provided a good fit as a large proportion of the variability in y has been explained by the estimated regression equation. O The estimated regression equation provided a good fit…
A set of n = 15 pairs of X and Y scores has SSX = 10, SSY = 40, and SP = 30. What is the slope for the regression equation for predicting Y from X? a. 10/30 b. 10/40 c. 40/10 d. 30/10
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.
Consider the following hypothetical regression: FRIES = 22.5 + 0.08*TRAFFIC + 9.1*COUPON? + -1.1*TEMP where FRIES is the number of pounds of fries a restaurant sells in a week, TRAFFIC is the number of people who walked by the restaurant that week (foot traffic), COUPON? is a dummy variable of if the restaurant offered a coupon or not that week (1=coupon, 0=no coupon); and TEMP is the average high that week, measured in Fahrenheit. All variables are statistically significant. If the average high is expected to be 4 degrees warmer next week, how should FRIES change? 1 Increase by 18.1 pounds 2 Decrease by 4.4 pounds 3 It is impossible to tell without knowing the values of TRAFFIC and COUPON?. 4 Increase by 4.4 pounds 5 Increase by 26.9 pounds

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