Estimate OLS estimators and Their variance Via Matrix notation
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A: Introduction The hypothesis is an assumption which is being tested.
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A: given data, n=40sample mean =64.3 standard deviation =1.44CI=0.99α=1-0.99=0.01
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- Consider the model (1-B)(1-0.2B)X_t = (1-0.5B)W_t find the forecasts for one, two and three steps ahead and calculate the variance of the one, two, and three steps ahead forecast errorsLinear Regression models can be optimised to avoid high variance by means of regularisations such as Lasso and Ridge. Explain when and why they are applied and the difference between both.Create a correlation matrix Table in APA style to report the correlations among these variables, along with their Ms and SDs.
- 8- Conduct residual analysis for the best fitted model . Comment on the normality assumption and constant error variances. Comment on this 4 graph on the the normality assumption and constant error variancesHow can a regression model be used to predict an outcome? Substitute the values of interest for each independent variable into the regression model Substitute the values of interest for each of the coefficients in the model Use Stepwise Regression to incrementally identify the critical independent variables in the best model Use the L.I.N.E. methods to determine which of the residuals has the least variance, and use those parametersConsider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certain