Problem 2. Consider the linear regression set-up Y = X3 + € where e~ Nn(0, 0² In); the design matrix is n by2 (for simplicity), and the first column of X is a column of 1s. Let Bo, B₁ denote the Least Squares (LS) estimatesof the two parameters Bo, B1.(i) Show that the sum of LS residuals always equals 0.(ii) Using the data, identify a 95% confidence region for the point (Bo, B1)' on the 2-dimensional plane, i.e., finda data-dependent region CC R² such that Prob{(Bo, ß₁)' € C'} = 0.95. [Hint: C will be an ellipse.](iii) Find a sufficient condition on the design matrix X that ensures that Bo, ß₁ are independent.(iv) Assume ² is known and equal to one; also assume that Bo, 3₁ are independent. Find simultaneous (joint)confidence intervals for ßo, ß₁ that have simultaneous exact coverage equal to 95%.

ANSWER: (i) LS estimates Bo and B1 are chosen to minimize the sum of squared residuals e = Y -…

Answered: Problem 2. Consider the linear…

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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If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
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?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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?
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The least squares regression line for a set of data is calculated to be y = 24.8 + 3.41x. (a) One of the points in the data set is (4, 37). Calculate the predicted value. (b) For the point in part (a), calculate the residual.
The estimate of b in the regression equation y=a+bx+error by the method of least square is
The relationship between number of beers consumed (x) and blood alcohol content (y) was studied in 16 male college students by using least squares regression. The following regression equation was obtained from this study: y-hat = -0.0127 + 0.0180x The above equation implies that: each beer consumed increases blood alcohol by .0127 on average it takes 1.8 beers to increase blood alcohol content by .01 After consuming 1 beer, blood alcohol equals .0180. each beer consumed increases blood alcohol by 0.018
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.
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