Question 1Consider the following regression model.Y = Bo + B,X; + B2Z¡ + µ i = 1,.,n.where Y,, X, Z, are i.i.d. &E (Hi/X;Z;) = 0%3DE (uf /xizi) = o?xtb. Using the acronym BLUE, demonstrate and explain how you would derive the L component for the estimator inthis regression model?

The regression model equation is as follows:In the given equation are independent variables.The…

Answered: Question 1 Consider the following…

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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Hello, please help me to solve the question below.Consider this regression model: Yt = β0 + β1 Ut + β2 Vt + β3 Wt + β4 Xt + εt ; where t= 1, ..., 75.We use OLS to estimate the parameters, producing the following model:Ŷt = 1.115 + 0.790 Ut − 0.327 Vt + 0.763 Wt + 0.456 Xt (0.405) (0.178) (0.088) (0.274) (0.017) Given that:R2 = 0.941; Durbin Watson stat DW = 1.907; RSS = 0.0757.(To answer the question, use the 5% level of significance, state clearly H0 and H1 that are tested, the test statistics that are used, and interpret the decisions.) (a) Describe the concepts of unbiasedness and efficiency. State the conditions required of regression (1) in order that the OLS estimators of the model parameters possess these properties. (b) Perform the following tests on the parameters of regression (1): (i) test whether the parameters β1, β2, β3 and β4 are individually statistically significant; (ii) test the overall significance of the regression model; (iii) test whether…
Your regression of 46 observations and 5 regressors shows a -2 R = 0.29. What is the value of R²? Enter your answer with two decimal places.
Example 1: Given the following data 72 = 0.8,ři3 = 0.7,3 = 0.6,0 =10.g. = 8 %3D %3D %3D 0z = 5. Determine the regression equation of X, on X, and X, .
An article presented data on compressive strength x and intrinsic permeability y of various concrete mixes and cures. Summary quantities are n = 14, Σy: = 572, Σy = 23530, Σ x₁ = 43, x² = 157.42, and xy; = 1697.80. Assume that the two variables are related according to the simple linear regression model. Round your answers to 3 decimal places. (a) Test for significance of regression using a = 0.05. (b) Estimate o2. i (c) In this model, what is the standard error of the intercept? i What is the standard error of the slope? i
(b) For a given 10 observations on price (X) and score (Y) the following data were obtained SSE = 1085.903, EX2= 469.48, X= 68, EY= 24704, EY = 464 and E XY = 3276.8 i. Develop an estimated regression equation that can be used to predict score given the price ii. How good is the estimated model's prediction power? ii. (c) A company studied the number of lost-time accidents occurring at its Nairobi Industrial Area, plant. Historical records show that 6% of the employees suffered lost-time accidents last year. Management believes that a special safety program will reduce such accidents to 5% during the current year. In addition, it estimates that 15% of employees who had lost-time accidents last year will experience a lost time accident during the current year. What percentage of the employees will suffer either of the lost-time accident over the two-year period?
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.3 7 3.4 2.4 2.6 2.6 0.9 yy 14 11.1 10.1 6.7 6.1 6.4 6.5 4.9 xx = thousands of automatic weaponsyy = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places):____________________ A) How many murders per 100,000 residents can be expected in a state with 10.8 thousand automatic weapons? Round to 3 decimal places. Answer = B) How many murders per 100,000 residents can be expected in a state with 4.4 thousand automatic weapons? Round to 3 decimal places. Answer =
Consider the data. X;| 1 2 3 4 5 y, 4 8 11 12 The estimated regression equation for these data is ý = 2.30 + 1.90x. (a) Compute SSE, SST, and SSR using equations SSE = E(y; - 9)?, SST = E(y; - y)?, and SSR = E(ŷ; - y)?. SSE = SST = SSR = (b) Compute the coefficient of determination r2. r2 Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line did not provide a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a large proportion of the variability in y has been explained by the least squares line. O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares line. (c) Compute the sample…
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.7 8.1 6.7 3.3 2.7 2.3 2.2 0.3 yy 13.8 10.7 9.9 6.6 6.1 5.9 6.1 4.5 xx = thousands of automatic weaponsyy = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 6.4 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 6.7 thousand automatic weapons? Answer = Round to 3 decimal places.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.9 8.4 6.8 3.8 2.9 2.7 2.6 0.5 14 11.1 9.6 7.3 6.7 6.5 6.3 4.7 x = thousands of automatic weapons y = murders per 100,000 residents %3D Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 2.1 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 10.9 thousand automatic weapons? Answer = Round to 3 decimal places. %3D
Question 1 Using multiple linear regression, estimate the value of a in the given regression model. Use 4 decimal places. MODEL: y=ax"ecx 1 2 3 4 ly 3.6 5.2 6.8 8.8
A residual plot from a simple linear regression analysis is shown to the right. Use the plot to answer the question below. residuals 0 X Which of the following statements regarding the plot is true? OA. The condition that the residuals are normally distributed is not met since not all of the residuals fall on the reference line in the residual plot. OB. The condition that the residuals have constant variation is met since the lines connecting the largest positive residuals and largest negative residuals are parallel. Oc. The condition that the residuals are normally distributed is not met since there is a diamond shape to the residuals in the residual plot. OD. The condition that the residuals have constant variation is not met since the variation increases and then decreases as x gets larger.
Shown in the SPSS output for creating a regression equation to predict students' attitudes toward statistics from their attitudes toward Ariana grande and beer. Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 4.974 0.590 8.432 0.000 AttAriana 0.264 0.155 0.256 1.701 0.097 Attbeer - 0.309 0.122 - 0.381 - 2.536 0.015 Coefficients a a Dependent Variable: Attstatistics Which of these represents the correct regression equation for this information? a. Y = 0.59 +…

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Question 1 Consider the following regression model. where Y₁, X₁, Z are i.i.d.& Y = Bo + BiXi + B2Zitu i = 1,....,n. E(μ₁/x₁z₁) = 0 E(u²/x₁z₁) = 0²xt b. Using the acronym BLUE, demonstrate and explain how you would derive the L component for the estimator in this regression model?