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The 95% confidence interval for B₁ is the interval: A. (B₁ - 1.645SE (1), ₁ + 1.645SE (B1)). B. (ẞ1-1.96SE (B1), ß₁ + 1.96SE (ß₁)) · ○ C. (₁ - 1.96, ³₁ + 1.96). - D. (³₁ − 1.96SE (³₁), ³₁ + 1.96SE (γ₁)). 8 Consider the regression model Y₁ = BX; +u; Where ui and X; satisfy the assumptions specified here. Let ẞ denote an estimator of ẞ that is constructed as ẞ Show that ẞ is a linear function of Y₁, Y2,..., Y. 1 +Yn) | (X1, X2,..., Xn) = Show that ẞ is conditionally unbiased. 1. E (Y;|X1, X2, Xn) = 2. E(B|×₁, X2,..., Xn) = E 1 3 II where Y and X are the sample means of Y; and X;, respectively. ☑' = B

The 95% confidence interval for B₁ is the interval: A. (B₁ - 1.645SE (1), ₁ + 1.645SE (B1)). B. (ẞ1-1.96SE (B1), ß₁ + 1.96SE (ß₁)) · ○ C. (₁ - 1.96, ³₁ + 1.96). - D. (³₁ − 1.96SE (³₁), ³₁ + 1.96SE (γ₁)). 8 Consider the regression model Y₁ = BX; +u; Where ui and X; satisfy the assumptions specified here. Let ẞ denote an estimator of ẞ that is constructed as ẞ Show that ẞ is a linear function of Y₁, Y2,..., Y. 1 +Yn) | (X1, X2,..., Xn) = Show that ẞ is conditionally unbiased. 1. E (Y;|X1, X2, Xn) = 2. E(B|×₁, X2,..., Xn) = E 1 3 II where Y and X are the sample means of Y; and X;, respectively. ☑' = B

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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please give me the right answer for both of thesd problem by fallowing the format too please

The 95% confidence interval for B₁ is the interval: A. (B₁ - 1.645SE (1), ₁ + 1.645SE (B1)). B. (ẞ1-1.96SE (B1), ß₁ + 1.96SE (ß₁)) · ○ C. (₁ - 1.96, ³₁ + 1.96). - D. (³₁ − 1.96SE (³₁), ³₁ + 1.96SE (γ₁)). 8
Transcribed Image Text:The 95% confidence interval for B₁ is the interval: A. (B₁ - 1.645SE (1), ₁ + 1.645SE (B1)). B. (ẞ1-1.96SE (B1), ß₁ + 1.96SE (ß₁)) · ○ C. (₁ - 1.96, ³₁ + 1.96). - D. (³₁ − 1.96SE (³₁), ³₁ + 1.96SE (γ₁)). 8
Consider the regression model Y₁ = BX; +u; Where ui and X; satisfy the assumptions specified here. Let ẞ denote an estimator of ẞ that is constructed as ẞ Show that ẞ is a linear function of Y₁, Y2,..., Y. 1 +Yn) | (X1, X2,..., Xn) = Show that ẞ is conditionally unbiased. 1. E (Y;|X1, X2, Xn) = 2. E(B|×₁, X2,..., Xn) = E 1 3 II where Y and X are the sample means of Y; and X;, respectively. ☑' = B
Transcribed Image Text:Consider the regression model Y₁ = BX; +u; Where ui and X; satisfy the assumptions specified here. Let ẞ denote an estimator of ẞ that is constructed as ẞ Show that ẞ is a linear function of Y₁, Y2,..., Y. 1 +Yn) | (X1, X2,..., Xn) = Show that ẞ is conditionally unbiased. 1. E (Y;|X1, X2, Xn) = 2. E(B|×₁, X2,..., Xn) = E 1 3 II where Y and X are the sample means of Y; and X;, respectively. ☑' = B
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