Consider the following regression model: Yi = B1 + B2X2i + B3X3i + B4X4i + ui Using the model above show that the maximum likelihood estimator for the variance, var (ui||X2i, X3i, B4X4i), is biased (be sure to comment of the nature of the bias).
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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?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?
- Suppose the simple linear regression model, Yi = β0 + β1 xi + Ei, is used to explain the relationship between x and y. A random sample of n = 12 values for the explanatory variable (x) was selected and the corresponding values of the response variable (y) were observed. A summary of the statistics is presented in the photo attached. Let b1 denote the least squares estimator of the slope coefficient, β1. What is the value of b1?1) Indicate whether the following statements are true or false. Explain why and show your work.a) In a simple regression Yi = B1 + B2 Xi + ui where var(ui) = o^2.X^2, weighting the model by X would solve the problem of non-common (heteroskedastic) variance of the error term.Given the residuals squared derived from the regression: Marks = ƒ(Study hours) Use the information from the following auxiliary regression (table attached) to conduct the Park test to detect for the presence of heteroscedasticity at a = 5%.
- Consider an estimated linear regression model with a response Y and 4 predictors X1, X2, X3, X4. For a random sample of 25 observations on the response and the 4 predictors, the following estimates are obtained using Excel. Regression Statistics Multiple R 0.859825564 Standard Error 127.606 What is the adjusted R-square of the fitted model?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).A multiple regression includes two regressors: Yi = b0 + b1X1i +b2X2i + ui. What is the expected change in Y if X1 increases by 8 unitsand X2 is unchanged? What is the expected change in Y if X2 decreasesby 3 units and X1 is unchanged? What is the expected change in Y if X1increases by 4 units and X2 decreases by 7 units?
- 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?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.At any given combination of values , the assumptions for the multiple regression model require that the population of potential error term values has a. a constant variance. b. a normal distribution. c. a mean equal to zero. d. All three of the assumptions are required.
