Suppose all of the regression assumptions in Key Concept 4.30 are satisfied except thatthe first assumption is replaced with E (u₁|X;) = 2. Which parts of Key Concept 4.40continue to hold? Which change? Why? (Is, normally distributed in large samples withmean and variance given in Key Concept 4.40? What about Bo?) The Least Squares AssumptionsY=Bo+B₁X₁+₁,i = 1,..., n, where1. The error term u, has conditional mean zero given X; E(u, X) = 0;2. (X, Y),i=1,..., n, are independent and identically distributed (i.i.d.) drawsfrom their joint distribution; and3. Large outliers are unlikely: X; and Y, have nonzero finite fourth moments.Large-Sample Distributions of Bo and ₁If the least squares assumptions in Key Concept 4.3 hold, then in large samples Boand , have a jointly normal sampling distribution. The large-sample normal dis-tribution of B₁ is N(B₁, o), where the variance of this distribution, o, isvar[(X₁-x)][var(X)]²The large-sample normal distribution of Bo is N(B), where1 var(Hu)[E(H)2 where H₁=1-Hx(4.21)(4.22)KEY CONCEPT4.3KEY CONCEPT4.4

All three of the least square assumptions held except the first assumption that instead of 0It is…

Answered: Suppose all of the regression…

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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1) Given are five observations collected in a regression study on two variables. 2 4 7 8 2 3 2 4 а) b) c) Develop a scatter diagram for these data. Develop the estimated regression equation for these data. Use the estimated regression equation to predict the value ofy when x = 6
Which of the following four statements is correct?Note: Only one answer is correct per question.1) Let f (x) = 2 + 5x be a linear regression line of a linear regression from Y to X.Then the following applies to the scatter plot: 1) The closer the points in the scatter diagram to the regression line drawnthe stronger the correlation between X and Y. 2) The further the points in the scatter plot are from the regression line drawn away, the stronger the correlation between X and Y. 3) Only if all points lie exactly on the regression line drawn isthe correlation between X and Y is strong. 4) The position of the points in the scatter plot has nothing to do with the strength of the correlation. 2) For the events A = "number of points when rolling the dice is two" and B = "number of points when rolling the diceRolling the dice is straight ":* P (A \ B) is smaller than P (A).* P (A \ B) is the same size as P (A).* P (A \ B) is greater than P (A).* No general statement can be made about…
The Update to the Task Force Report on Blood Pressure Control in Children [12] reported the observed 90th per-centile of SBP in single years of age from age 1 to 17 based on prior studies. The data for boys of average height are given in Table 11.18. Suppose we seek a more efficient way to display the data and choose linear regression to accomplish this task. age sbp 1 99 2 102 3 105 4 107 5 108 6 110 7 111 8 112 9 114 10 115 11 117 12 120 13 122 14 125 15 127 16 130 17 132 Do you think the linear regression provides a good fit to the data? Why or why not? Use residual analysis to justify your answer. Am I supposed to run a residual plot and QQ-plot for this question?
2. (35 Points) Consider the following dataset obtained from a sample. 80 3 97 4 92 4 102 103 8 111 10 119 10 123 11 117 13 136 (a) Compute the estimated linear regression coefficients to predict y using x values. (b) Using the estimated regression line found in the previous part, predict the value of y if æ equals to 7. (c) Compute SSE, SST, and SSR. Then, also compute the resulting coefficient of determination and comment on the goodness of fit. (d) Conduct t-test to test the significance of the regression relationship with a = 0.05. (e) Conduct F-test to test the significance of the regression relationship with a = 0.05.
A recent Gallup survey of a random sample of Americans (18 and older) found that the average number of alcoholic drinks consumed per week (drinks) by males was 4.2 and by females was 1.4.[1] Suppose we use the underlying survey data to estimate a least-squares regression of the average number of drinks a person reports consuming per week (Drinks;) on a dummy variable equal to 1 if i is female and O otherwise (Female;). (Assume all respondents identify as either male or female.) The estimated regression line equation can be written as: Drinks = a +bFemale Alcohol Consumption by Gender Because Female is a dummy variable, the problem provides us with enough information to figure out the exact regression line equation. What is the numerical value of a?
I need solutions for d) and e). Please.
Solve the second question in regression analysis
Bxi + €i, where €; are independently and Consider a simple linear regression model Y; = identically distributed with mean 0 and variance o?, and i = 1,..., n. Note: this model does not have a intercept term. Derive the Best Linear Unbiased Estimator (BLUE) for B. Denote this by BBLUE. Make sure to state why this is the BLUE.
Suppose x1 ans x2 are predictor variables for a response variable y. a. The distribution of all possible values of the response variable corresponding to particular values of the two predictor variables is called a distribution of the response variable. b. State the four assumptions for multiple linear regression inferences.
7. Find slop of a linear regression model for the following data: x = [1, 2, 3, 4, 5, 6, 7] z = [ 1.40, 3.78, 4.41, 4.60, 8.40, 8.64, 12.81]. -1.7 -0.5 0.5 O 1.7 CS Scanned with CamScannel
Consider the following linear regression model: Yi = B1 + B2x2i + B3x3i + ei of = o²r What is the weight to be used for generalized (or weighted) least-squares estimation? Select one: 1 1 Ob. 1 Oc. 1 od.
Show calculations or explanation for each question. a) Which of the following techniques is used to predict the value of one variable on thebasis of other variables?a. Correlation analysisb. Coefficient of correlationc. Covarianced. Regression analysis b) In the least squares regression line, y^=3-2x the predicted value of y equals:a. 1.0 when x = −1.0b. 2.0 when x = 1.0c. 2.0 when x = −1.0d. 1.0 when x = 1.0 c) In the simple linear regression model, the y-intercept represents the:a. change in y per unit change in x.b. change in x per unit change in y.c. value of y when x = 0.d. value of x when y = 0.

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