Don't copy and paste the answer, dont give the answer in incomplete steps and don't give the wrong answer, will give the downvote for these mistakes. ㅋI Carrelation and regressionitot (x,y) have the faire polif alien is given by tof (ury) = 118\4 | <2, 0 nell01 otherwiseyou have to show the regression of d'on & ar you aist Weal but aftellit Hot2 The wsables & and Y' with sexo mens and Standard deviationOr and Oor obe netrally cantrelated with coppelation efficientp. You have to show that I and u definies as UxX HYU=ndaypacent national varit vesand Vs Xмои Матвеше74wwwalleOL2 (HP) respetway.

As per bartleby guidelines we can solve only first question and rest can be reposted

Answered: (x, y) have the fame poef weer is given…

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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Enter T for true or F for false: 1. The Durbin-Watson test focuses on 1st order autocorrelation. 2. The Durbin-Watson test is predicated on the condition that regression residuals be independent of each other. 3. Autocorrelation will tend to cause the calculated standard error of the estimate to be overestimated. 4. Autocorrelation will tend to cause the calculated standard errors of the partial regression coefficients to be underestimated.
Suppose that Yt is the monthly value of the number of new home construction projects started in the United States. Because of the weather, Yt has a pronounced seasonal pattern; for example, housing starts are low in January and high in June. Let μJan denote the average value of housing starts in January and let μFeb, μMar,. . . . , μDec denote the average values in the other months. Show that the values of μJan, μFeb, . . . , μDec can be estimated from the OLS regression Yt = β0 + β1Febt + β2Mart + . . . .+ β11Dect + ut, where Febt is a binary variable equal to 1 if t February, Mart is a binary variable equal to 1 if t is March, and so forth.
a.Conduct an Augmented Dickey fuller test of unit roots for each variable. b. Estimate the following model using OLS and present the OLS estimates, making sure that you use stationary variables in your OLS regression. InYt = ßo + ß1lnl2 + ß2lnCt + ut... (1a) Where Yt, lt and Ct rare as defined above, ßo, ß1, ß2 are the parameters to be estimated and ut is the random error term. c.Test for autocorrelation.
Consider the following simple linear regression model: y = β0 + β1x + u. Using a sample of n observations on x and y, you estimate the model by OLS and obtain the estimates βˆ 0, βˆ 1, and the R-squared of the regression, R2 . Then you scale this sample by a factor of 100, obtain a new sample {xi/100; yi/100} for i = 1, . . . , n, re-estimate the model by OLS, and denote the new coefficient estimates by β˜ 0, β˜ 1, and the new R-squared of the regression by R˜2 . a) Give the expression of β˜ 1 in terms of βˆ 1, and justify your answer.
Consider the simple linear regression model where Y1, . . . , Yn are independent random variables with Yi ∼ N(µi , σ 2 ), µi = β0 + β1xi , for i = 1, . . . , n. Given that we observe y1, . . . , yn, find the MLEs for β0, β1 and σ 2 .
The following table contains data for the number of bacteria y (in millions) growing in a particular medium after x hours. Using a calculator or statistical software, find the linear regression line for the data in the table. Enter your answer in the form y=mx+b, with m and b both rounded to two decimal places. x y 0 3.67 1 5.24 2 7.04 3 10.31 4 9.83 5 11.04 6 14.68 7 15.02
Enter T for true or F for false. In multiple regression as in simple linear regression we use ANOVA to test individual partial regression coefficients for their significance in explaining the data. _____ Multicollinearity is a condition where two or more of the independent variables in the data are correlated. One of the ways this can show up in data analysis is in stepwise regression where addition of another explanatory variable causes partial regression coefficients of variables already in the regression to change substantially. _____ The prediction interval in multiple regression is a confidence interval around a specific value of y. _____ The confidence interval in multiple regression is centered on the regression-predicted value of y. _____ If a histogram of residual values is symmetric and bell shaped, this is evidence that the regression is reasonable. _____
The estimated regression equation for a model involving two independentvariables and 65 observations isyˆ = 55.17 + 1.2X1Other statistics produced for the analysis include: SE(b1) = 0.33. Perform a t-test usingthe critical value approach for the significance of β1
Suppose that the sales of a company (Y) is regressed on advertising expenditure (x) and labor cost (z), and the estimated regression equation is Y = 5 + 0.5x + 0.7z + u (where u is the error term). Here, sales, advertising expenditure and labor cost are measured in million Tk. Standard error for the coefficient of x is 0.04, standard error for the coefficient of z is 0.01, and the sample size is 20. Can we conclude that advertising expenditure is a statistically significant variable?
Let X1, X2 denote two independent variables, each with a x^2(2) distribution. Find the joint pdf of Y1=X1 and Y2 = X2+X1. Note that the support of Y1, Y2 is 0<y1<y2<infinity. Also, find the marginal pdf of wach Y1 and Y2. Are Y1 and Y2 independent?
For these (x,y) pairs of data points: 1,5 3,7 4,6 5,8 7,9 Compute b1. Compute b0. What is the equation of the regression line?
In running a regression of the retunrs of stock XYZ against the returns on the market, the Std for the returns of stock XYZ is 20% and that of the market returns is 15%. If the estimated beta is found to be 0.75 : What is the maximum possible value of beta given that the standar deivation of the returns of stock XYZ is 20% and those of the market is 15% ?

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(x, y) have the fame poef weer is given by t [ f (nry) = -128 \4 | <2, 02 01 otherwise you have to show the regression of a any as you a I't Wheel, ilgut pafhestion The sales and 8/² nicht ا ان