2. Consider the multiple regression model with three independent variables, under theclassical linear model assumptions MLR.1 through MLR.6:y= Bo+ B1x1 + B2x2 + B3x3 + u.You would like to test the null hypothesis Ho: B1 - 3ẞ2 = 1.a. Let B1 and B2 denote the OLS estimators of B1 and B2. Find Var(B₁ - 3B2) interms of the variances of B₁ and B2 and the covariance between them. What is thestandard error of B₁ - 3B2 ?b. Write the t statistic for testing Ho: ẞ1-3ẞ2 = 1.c. Define B1-3ẞ2 and ₁ = B₁ - 3ẞ2. Write a regression equation involving Bo,1, B2 and B3 that allows you to directly obtain ₁ and its standard error.

Given:Consider the multiple linear regression models with three independent variables as given…

Answered: 2. Consider the multiple regression…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter1: Functions

Section1.EA: Extended Application Using Extrapolation To Predict Life Expectancy

Problem 5EA

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
a) explain on he strenght and variation of the model (multiple regression) b) At a-value =0.01. test whether there is a significiant relationship between the dependent variable (y) and the independant variables x1, x2 and x3
Use the general equation for the least square regression line to show that this line always passes through the point (x,y) * bars above the x and y.That is, set x=x(with a bar above the x) and show that the line predicts that y=y (with a bar above the y).
We have data on Lung Capacity of persons and we wish to build a multiple linear regression model that predicts Lung Capacity based on the predictors Age and Smoking Status. Age is a numeric variable whereas Smoke is a categorical variable (0 if non-smoker, 1 if smoker). Here is the partial result from STATISTICA. b* Std.Err. of b* Std.Err. N=725 of b Intercept Age Smoke 0.835543 -0.075120 1.085725 0.555396 0.182989 0.014378 0.021631 0.021631 -0.648588 0.186761 Which of the following statements is absolutely false? A. The expected lung capacity of a smoker is expected to be 0.648588 lower than that of a non-smoker. B. The predictor variables Age and Smoker both contribute significantly to the model. C. For every one year that a person gets older, the lung capacity is expected to increase by 0.555396 units, holding smoker status constant. D. For every one unit increase in smoker status, lung capacity is expected to decrease by 0.648588 units, holding age constant.
)A county real estate appraiser wants to develop a statistical model to predict the appraised value of 3) houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model: E(u) = Bo + Bix, where y = appraised value of the house (in thousands of dollars) and x = number of rooms. Using data collected for a sample of n = 73 houses in Fast Meadow, the following results were obtained: y = 73.80 + 19.72x What are the properties of the least squares line, y = 73.80 + 19.72x? A) Average error of prediction is 0, and SSE is minimum. B) It will always be a statistically useful predictor of y. C) It is normal, mean 0, constant variance, and independent. D) All 73 of the sample y-values fall on the line.
Consider the multiple regression model Y₁ = Bo + B₁x1₁j + B₂x2j+B3 x 3j+ €j under the usual assumptions labelled A1, A2, A3, A4, A5, A6. Briefly explain which type of graphs are performed in the analysis of residuals.
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.…
Consider the two variable regression model: Y = Bo+P,Edu+ B2EXP21 + u, where Y denotes the average monthly income, Edu denotes the number of years of education, Exp denotes the number of years of experience, and u, denotes the error term. Suppose the researcher wants to test whether the effect of education on average monthly income and the effect of experience on the average monthly income of an individual are the same or not So, the test the researcher wants to conduct is Ho: B = 6, vs. H, f,+P2 The hypotheses can be tested by modifying the original regression equation to turn the restriction into a restriction on a single regression coefficient. Suppose the regression function is modified in the following way: Y, Po+Y,Edu, + B2W +u, where y, =P,-P2 and W, = Edu,, + Exp2i %D Since y, = B, - B2, the test the researcher wants to conduct will now be Ho: y=0 vs. H y0. Let y, and SE(y,), denote the estimated slope coefficient of y, and the standard error of 7, respectively. is .D IS If…
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.…
A) A linear regression has a =6 and b=5 what is y predicted as when x=9? B) A linear regression has b=3 and a=4.What is the predicted Y for x=7?
The manufacturer of Beanie Baby dolls used quarterly price data for 2012/- 2020/V (t = 1, ..., 36) and the regression equation Pt = a + bt + c₁D1 t + c2 D2t + c3D3 t to forecast doll prices in the year 2021. Pt is the quarterly price of dolls, and D1, D2t, and D3 are dummy variables for quarters I, II, and III, respectively. DEPENDENT VARIABLE: PT R-SQUARE P-VALUE ON F 0.0001 OBSERVATIONS: 36 0.9078 F-RATIO 76.34 STANDARD ERROR PARAMETER ESTIMATE T-RATIOP-VALUE VARIABLE INTERCEPT 24.0 6.20 3.87 0.0005 T 0.8 0.240 3.33 0.0022 D1 -8.0 2.60 -3.08 0.0043 D2 -6.0 1.80 -3.33 0.0022 D3 -4.0 0.60 -6.67 0.0001 In any given year price tends to vary from quarter to quarter as follows: Multiple Choice O O O P₁ > P|| > P||| > PIV PI> PIV> Pill > PII Pll > Pill > PIV> PI Pill > PI>PII > PIV PIV> Pill > PII > PI
Assume that we have the usual Simple Linear Regression model given by: Y₁ = Bo + Bixi +8 Show that in this case: Where Txy R² = rzy is the correlation coefficient between x and y.

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