If x =2y + 3 and y = kx + 6 are the regression lines of x on y and y on x%3Drespectively.i) show that 0

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Answered: If x 2y + 3 and y = kx + 6 are the…

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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Some non-linear regressions can also be estimated using a linear regression model (using 'linearization'). Assume that the data below show the selling prices y (in dollars) of a certain equipment against its age x (in years). We'd like to fit a non-linear regression in the form y = cd* to estimate parameters c and d from the data by linearizing the model through In y In c+ (In d)x = b, + b, x. y y 6381 3 5394 5673 4980 2 5740 4896 (Click the button to copy or download the data.) Using Excel ot other software, the non-linear regression model y = cd can be estimated as: y = D*. (Round c and d to four decimal places, inlcuding any zeros.)
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?
The table shows the numbers of new-vehicle sales (in thousands) in the United States for Company A and Company B for 10 years. The equation of the regression line is ModifyingAbove y with caret equals 0.993 x plus 1 comma 195.82y=0.993x+1,195.82. Complete parts (a) and (b) below. New-vehicle sales left parenthesis Company Upper A right parenthesis comma x(Company A), x 4 comma 1674,167 3 comma 8823,882 3 comma 5693,569 3 comma 4433,443 3 comma 2993,299 3 comma 1173,117 2 comma 8372,837 2 comma 4982,498 1 comma 9401,940 2 comma 0842,084 New-vehicle sales left parenthesis Company Upper B right parenthesis comma y(Company B), y 4 comma 9204,920 4 comma 8444,844 4 comma 8374,837 4 comma 7104,710 4 comma 6754,675 4 comma 4284,428 4 comma 6604,660 3 comma 8323,832 2 comma 9272,927 2 comma 7532,753 Question content area bottom Part 1 (a) Find the coefficient of determination and interpret the result.…
Can a causal relationship be established between a variable y and a variable x by running the following regressions: i) y = f(x) and ii) x = f(y). Explain in less than 75 words.
6. Suppose we estimate a linear regression equation Y; = Bo + B1X; + u; by OLS. (a) Show thatE ûi 0, where the û;'s are the regression residuals. i3D1 (b) Suppose we regress X; on û; by OLS, including a constant term in the regression. Show that the estimated coefficient on û; is equal to zero. (c) Suppose we regress Y; on the predicted value Y; by OLS, including a constant term in the regression. Show that the estimated coefficient on Y; is equal to one. (Hint: the regression residual is defined by û; = Y; – Y; where the predicted value Y; = Bo + B,X;. Here, Bo and B1 are the OLS estimators.)
We wish to predict the salary for baseball players (y) using the variables RBI (x1) and HR (x2), then we use a regression equation of the form ˆy=b0+b1x1+b2x2y^=b0+b1x1+b2x2. HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars. The following is a chart of baseball players' salaries and statistics from 2016. Player Name RBI's HR's Salary (in millions) Adrian Beltre 104 32 18.000 Justin Smoak 34 14 3.900 Jean Segura 64 20 2.600 Justin Upton 87 31 22.125 Brandon Crawford 84 12 6.000 Curtis Granderson 59 30 16.000 Aaron Hill 38 10 12.000 Miquel Cabrera 108 38 28.050 Adrian Gonzalez 90 18 21.857 Jacoby Ellsbury 56 9 21.143 Mark Teixeira 44 15 23.125 Albert Pujols 119 31 25.000 Matt Wieters 66 17 15.800 Logan…
Suppose you have a survey where one of the variables is "Sex", and all of the 300 people surveyed answered one of the following: 1) Male or 2) Female. Suppose further that you create 2 dummy variables: D1 = 1 if male, zero otherwise D2 = 1 if female, zero otherwise What would happen if you include both dummy variables in your regression in Excel? O Excel will not be able to run a regression with both variables in the regression. Nothing. This is the correct way to do it. O Your regression will exhibit serial correlation. O Your regression will exhibit some multicollinearity, but can be remedied with "robust standard errors."
A statistics student is asked to estimate Y = Bo + B1X + E. She calculates the following values: Ex = 280, Σ(x₁ - x)² = 350, Σy, = 600, Σ(y, − y) = 1000 Σ(x,x)(y₁ - y) = -630, n = 20 Which of the following is the sample regression equation? OY--55.2-1.8X + e OY 55.2 +1.8X + e OY-55.2+1.8X + e OY 55.2 1.8X + e
Find the least-squares regression line ŷ = bo + bịx through the points (-3, 1), (0, 6), (4, 15), (8, 19), (10, 23). For what value of x is ŷ = 0?

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If x 2y + 3 and y = kx + 6 are the regression lines of x on y and y on x %3D respectively. i) show that 0