Consider the following regression model: Y₁ =B₁ + B₂X₂ + B3X31+ B4X41 + U1 Using the model above show that the maximum likelihood estimator for the variance, var (uX21, X3i. B4X41), is biased (be sure to comment of the nature of the bias).
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- The least-squares regression line relating two statistical variables is given as = 24 + 5x. Compute the residual if the actual (observed) value for y is 38 when x is 2. 4 38 2In a multiple linear regression model with 3 predictor variables, what is the t-statistic for the hypothesis test of the null hypothesis that the coefficient of the second predictor variable is equal to 0, if the estimated coefficient is 0.5, the standard error of the estimate is 0.1, and the degrees of freedom is 15?Can logistic regression be used in both prospective and retrospective study? Will the odds ratios of the effect of the predictor on the response are different under these two study designs? What formula can we use to solve maximum likelihood estimate of regression coefficient beta in Poisson regression (with log link)? can ridge regression be applied if sample size is smaller than the number of predictors? if any 2 variables in X1, X2 AND Y have a positive correlation, then in the linear regression Y = b0 + b1X1 +b2X2 +e, will the sign of b1 and b2 both be positive? will the residuals that we get from linear regression will always be uncorrelated given X?
- A sample multiple linear regression equation predicts the response variable y to be 87.62 at values of the predictor variables x1 = 5, x2 = 12, and x3 = 27. What is the estimated value of the conditional mean of y at x1 = 5, x2 = 12, and x3 = 27?Given the estimated least square regression line y=2.48+1.63x, and the coefficient of determination of 0.81, What is the value of correlation coefficient?A snack food manufacturer estimates that the variance of the number of grams of carbohydrates in servings of its tortilla chips is 1.34. A dietician is asked to test this claim and finds that a random sample of 16 servings has a variance of 1.22. At α=0.05, is there enough evidence to reject the manufacturer's claim? Assume the population is normally distributed. Complete parts (a) through (e) below.
- State the large-sample distribution of the instrumental variables estimator for the simple linear regression model, and how it can be used for the construction of interval estimates and hypothesis tests.A snack food manufacturer estimates that the variance of the number of grams of carbohydrates in servings of its tortilla chips is 1.25. A dietician is asked to test this claim and finds that a random sample of 22 servings has a variance of 1.35. At ⍶ = 0.05 is there enough evidence to reject the manufacturer’s claim?A linear regression model was fit to a set of data containing 18 observations. The computer output of the regression analysis is shown in the table. Term CoefCoef SE CoefSE Coef TT Constant 12.00 5.43 2.210 xx 0.694 0.241 2.880 Assume the conditions for regression are met. Which of the following defines the margin of error when a 95 percent confidence interval for the slope of the least-squares regression line is calculated? (1.75)(0.241) A (1.75)(0.694) B (1.96)(0.241) C (2.12)(0.241)) D (2.12)(0.694) E
- A snack food manufacturer estimates that the variance of the number of grams of carbohydrates in servings of its tortilla chips is 1.141.14. A dietician is asked to test this claim and finds that a random sample of 2424 servings has a variance of 0.950.95. At alpha equals 0.10α=0.10, is there enough evidence to reject the manufacturer's claim? Assume the population is normally distributed.Consider the simple linear regression model based on normal theory. If we are interested in two different testing H0 : B1 = 0 vs H1: B1 does not = 0 and H0 : p= 0 vs H1: p does not = 0, then show that test statistic value in both the tests are equivalent (values are always same).Consider the multiple regression model shown next between the dependent variable Y and four independent variables X1, X2, X3, and X4, which results in the following function:Ŷ = 33 + 8X1 − 6X2 + 16X3 + 18X4For this model, there were 35 observations; SSR = 1,544 and SSE = 600. Assume a 0.01 significance level.Based on the given information, which of the following conclusions is correct about the statistical significance of the overall model? Multiple Choice Reject the null hypothesis that β3 = 0. Do not reject the null hypothesis that β1 = β2 = β3 = β4 = 0. Reject the null hypothesis that β1 = 0. Reject the null hypothesis that β1 = β2 = β3 = β4 = 0.