Please give me answer quickly... 6. Suppose the intercept of simple linear regression model is known as 5.One can consider the modelY₁ = 5+ BX; +uifor i = 1,...,n, E(u) = 0, Var(u) = 1. Find the OLS estimator B,E(B), Var(B).7. Consider the simple linear regression given byY₁ = B1+ B2Xi+uiBut instead of assuming E(u|x) = 0, suppose one assume E(u|x) = 3 ObtainOLS estimator B1, B2.

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Answered: 6. Suppose the intercept of simple…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter7: Analytic Trigonometry

Section7.6: The Inverse Trigonometric Functions

Problem 93E

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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?
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6. Suppose the intercept of simple linear regression model is known as 5. One can consider the model Y₁ = 5+ ẞX₁+ui for i=1,...,n, E(u) = 0, Var(u) = 1. Find the OLS estimator B, E(B), Var(B).