Which of these does not represent the hypothesis testing about Regression Coefficient(s) of Multiple Linear Regression Model? H0: b1=b2=.....bk=0 vs. H1: Not H0 H0: Bi =0 vs. H1: Not H0, for i=1,2,...,k H0: B1=B2=.....Bk=0 vs. H1: Not H0
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- The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 13 In the MLR model, what do we mean by Heteroskedasticity? That the error term depends on the values of the explanatory variables That all the explanatory variables have different variance That the variance of the error term is a function of the explanatory variables That the variance of the error term is constant QUESTION 14 Suppose that in the model Y=b0+b1*X1+u, we add a variable that is correlated with both Y and X1. What will happen…2. What is the use of the regression line? C. A student conducted a regression analysis between the math grades of his classmates and the number of times they were absent in the subject. He found that the regression line that will predict the grade (y) if the number of absences (x) is known y = 97.732 - 2.61x. 1. What is the predicted grade of a student who has 5 absences? 2. What is the predicted grade of a student who has 8 absences? 3. What is the predicted grade of a student who has 2 absences? 4. What is the predicted grade of a student who has 1 absence? 5. What is the predicted grade of a student who has 0 absences?29) Two variables are measured on a random sample of n = 23. The sample data results ina sample correlation of 0.393. If you fit a simple regression model to the data, whatvalue would the coefficient of determination (or R-squared) take on?A) 0.627B) 0.393C) 0.154D) 0.000E) 0.296
- Can you answer the questions in 3b, 3c, and 3d? Thank youThe Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 4 Suppose we have an SLR model, where the dependent variable (Y) represents ‘how satisfied someone is with his/her life, from 0 to 100’ (the higher the value, the higher the satisfaction with life), and the explanatory variable (X1) represents ‘personal annual income in £1,000’. The estimated OLS regression line is: Yhat = 33.2 + 0.74*X1. According to this model, what is the predicted life satisfaction, for someone with…please give me the right answer for both of thesd problem by fallowing the format too please
- 41. Which of the following is the multiple regression model for the data? (a) y = -0.11050 + 2.10797x1 + 0.40717x2 (b) y = -0.11050 + 0.40717x1 + 2.10797x2 (c) y = 0.23563 + 0.00062x1 + 0.23626x2 (d) none (e) y = -0.44187 + 2.42120x1 + 0.36135x2The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 28 Suppose your estimated MLR model is: Y_hat = -30 + 2*X1 + 10*X2 Suppose the standard error for the estimated coefficient associated with X2 is equal to 5. Now, suppose that for some reason we multiply X2 by 5 and we re-estimate the model using the rescaled explanatory variable. What will be the value of the estimated coefficient of X2 and its standard error? The estimated coefficient of X2 will be equal to 50 and its standard error will be…The defending attorney Mr. Justin Case was interested in how a lengthy trial could affect howlong a jury would deliberate on a case (and see if he should just cut to the chase). He observed asample of courtroom trials and noticed the following:Days inTrial (X) 5 2 6 4 5 6 2 4 2 1 HoursDeliberation (Y) 4 4 1 3 1 3 9 2 3 7 1) What is the slope of the regression line?
- The Consumer Reports Restaurant Customer Satisfaction Survey is based upon 148,599 visits to full-service restaurant chains.t Assume the following data are representative of the results reported. The variable type indicates whether the restaurant is an Italian restaurant or a seafood/steakhouse. Price indicates the average amount paid per person for dinner and drinks, minus the tip. Score reflects diners' overall satisfaction, with higher values indicating greater overall satisfaction. A score of 80 can be interpreted as very satisfied. (Let x, represent average meal price, x, represent type of restaurant, and y represent overall customer satisfaction.) Restaurant Туре Price ($) Score Bertucci's Italian 16 77 Black Angus Steakhouse Seafood/Steakhouse 24 79 Bonefish Grill Seafood/Steakhouse 26 85 Bravo! Cucina Italiana Italian 18 84 Buca di Beppo Italian 17 81 Bugaboo Creek Steak House Seafood/Steakhouse 18 77 Carrabba's Italian Grill Italian 23 86 Charlie Brown's Steakhouse…Detail all the steps involved in testing the hypothesis below for the linear regression model y = XB + e, where X = (50 x 6) for two cases. Ho: X3B3 + x4B4 = 0 На: x3B3 + x4B4 # 0The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 16 In a t-test, suppose a researcher sets the significance level at 0.5%. What does this mean? The probability that the null hypothesis is true is 0.5% The researcher would be rejecting the null hypothesis, only if the p-value is less than 0.5% The researcher would be rejecting the null hypothesis, if the t-statistic is higher than 0.5 It does not mean anything, because the significance level can only be set at 5% QUESTION 17 In an MLR…