Consider the linear model y=B,+B,x,+B,x,+B,x+P+u, You estimate the model y=B,+B,x,+B,x,observations and obtain the OLS residuals . You then estimate the auxiliary regression+u based on 123313 3144iThe LM statistic3i+14/you obtain to test the null hypothesis that H:B,=B,=0 is 20.91. What is theR2of the auxiliary regression?It is not possible to say0.17O 0.175714O 0.177203

The linear model is given which is as follows:The LM statistic to test that is 20.91 and n 123.The…

Answered: Consider the linear model y =B₁+₁₁+₂21…

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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29)
Please do not explain, just say the answer like A,B,C,D,
Find the least square regression line.
Suppose we have a multiple regression model with 2 predictors and an intercept. (Without any interaction or higher order terms, we have only the 2 predictors in the model and the intercept.) We have only n= 6 observations (so it would be rather silly to fit this model to this data, but let's pretend it is reasonable). We find the values of the first 5 residuals are: 2.6, 2.3, 2.5, -1.5, -1.4 What is the value of MSRes for this multiple regression model?
Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1
5. Write the equation of the least-squares regression line defining any variables used. Round coefficients to 4 decimal places .
Suppose that you perform a hypothesis test for the slope of the population regression line with the null hypothesis H0: β1 = 0 and the alternative hypothesis Ha: β1 ≠ 0. If you reject the null hypothesis, what can you say about the utility of the regression equation for making predictions?
A company studying the productivity of its employees on a new information system was interested in determingg if the age (X) of data entry opeertors influenced the number of completed entries made per hour (Y). The regression equation is y = 14.374 - 0.145x Suppose the acyual completed entries per hour for an operator who is 35 years old was 8. The residual is:
The table below shows (lifetime) peptic ulcer rates (per 100 population), UU, for various family incomes, xx, as reported by the 1989 National Health Interview Survey. Income 4000 6000 8000 12000 16000 20000 30000 45000 60000 Ulcer rate 14.1 13.1 13.8 12.6 11.8 11.7 11.5 9.3 7.6 (a) Find the equation of the regression line. Ulcer rate, U(x)= . (b) Estimate the peptic ulcer rate for an income level of x0= 25000 according to the linear model in part (a). Ulcer rate, U(x0)= .
In this problem you will perform calculations by hand, do not use the R functions lm() or anova() to find the results. Suppose our sample data consists of the six (x,y) pairs {(2,5), (4,6), (5,5), (7,10), (8,9), (10,13)}. Find Determine the fitted regression equation for this data, and use it to predict y for x=2, 4, 5, 7, 8, and Find the error associated with each of these predictions. Find Use the results from part (a) and (c) to calculate the coefficient of determination. What percentage of the variation in y is explained by the regression?
The estimated regression equation for a model involving two independent variables and 10 observations follows. ý = 22.1370 + 0.5303Xq + 0.4920X2 (a) Interpret b₁ in this estimated regression equation. O b₁ = 0.5303 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₁ = 0.5303 is an estimate of the change in y corresponding to a 1 unit change in x₁ when X₂ is held constant. O b₁ = 22.1370 is an estimate of the change in y corresponding to a 1 unit change in x₁ when x₂ is held constant. O b₁ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₂ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₁ when x₂ is held constant. Interpret b₂ in this estimated regression equation. O b₂ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₂ = 22.1370 is an estimate of the change in y corresponding to a…
Suppose we carry out a linear regression analysis with the following model: Y = Bo + B1X1 + .. + BrXr + € where k = 8. n = 195. We find SSReg = 231.4, and SSTotal = 972.5. Before collecting the data, we knew that we'd like to test the null hypothesis that Bi = B2 = 63 = 0. If we run a regression model leaving the predictors associated with these slopes out of the model, we find SSReg = 74.1 and SSTotal = 972.5. What is the value of the appropriate Fstatistic to test this null hypothesis (that these 3 slopes are 0)?

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