If the data analyst hypothesizes that grocery sales are higher when paymen cash payment, the null hypothesis would likely state that grocery sales are cards compared to cash payment.
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- Let wage denote hourly wages, educ denote years of education, and define the dummy variable: if educ > 12 D = if educ < 12 i.e. we can think of D as a dummy variable indicating graduation from high school. Suppose we want to test the null hypothesis that the marginal return to education for every year after high school is the same as the marginal return to education for each year before high school (against the alternative that they are different). Which model and test would accomplish this goal? O a. Estimate wage = B1 + B2 educ + ß3 D + e and test Ho : ß3 = B2 against H1 : ß3 # B2. O b. Estimate wage = ß1 + B2educ + ß3 D + e and test Ho : ß3 = 0 against H1 : ß3 # 0. Estimate wage = B1 + B2educ + B3(educ + D) + e and test Ho : ß3 = 62 against H1 : B3 # B2. O d. Estimate wage = B1 + B2educ + B3(D × (educ – 11)) + e and test Ho : ß3 = B2 against H : B3 # B2. c. O e. Estimate wage = Bi + B2educ + ß3(D × (educ – 11)) + e and test Ho : ß3 = 0 against H1 : ß3 + 0.Suppose you have time series data and estimate the following model y = β0 + β1x1 + ε. When you estimate the model, you find that there are correlated observations (serial correlation). You choose to ignore this issue. What are the consequences? A/ The predictions are biased, but the hypothesis tests are valid. B/ The predictions are not biased, but the hypothesis tests are invalid. C/ Both the predictions and the hypothesis tests are valid. D/ Both the predictions and the hypothesis tests are not valid. Choose the correct answer and briefly explain.Three EV (Electric Vehicle) motor systems are being tested for their efficiency. The following are the data. Motor 1 Motor 2 Motor 3 56 60 58 45 55 54 50 52 53 44 59 50 49 50 52 Using alpha = 0.05, are there any differences between the level of efficiency in the motor systems? *Define the null and alternative hypothesis [ Select ] *Calculate the degrees of freedom, dfoetween , dfwithin, and the dfrotal , respectively [ Select ] *State the decision rule, Feritical [ Select ] *Calculate test statistic, F = [Select] *State result and conclusion [ Select ] > >
- Suppose the null hypothesis, Ho, is: Darrell has worked 20 hours of overtime this month. What is the Type I error in this scenario? Select the correct answer below: Darrell thinks he has not worked 20 hours of overtime this month when, in fact, he has. Darrell thinks he has not worked 20 hours of overtime this month when, in fact, he has not. O Darrell thinks he has worked 20 hours of overtime this month when, in fact, he has not. Darrell thinks he has worked 20 hours of overtime this month when, in fact, he has. Content attribution FEEDBACKEstimate the following model: purchase = β0 + β1traditional + β2aggregator + β3substack(Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?
- True or False? WLS is preferred to OLS when an important variable has been omitted from the model.In the linear model ,E (X*u) = a)X*u b) 0 c) u d) none of tha aboveWe have a pooled cross section of data with data from 2009 and 1999 on women’s wages and their years of education. We consider the model: wage = β0 + β1Year2009 + β2educ + β3Year2009 × educ + u where wage is the hourly wage and educ is the years of education. Year2009 is a dummy variable equal to 1 in the year 2009 and 0 otherwise. Which of the following measures the change in the effect of education on the wage between 1999 and 2009? a) β0 b) β1 + β2 + β3 c) none of these answers d) β1 + β3 e) β3
- Can the market model (3,6) be rewritten in the format of (4.1)? Why?Consider the following Stata output for the model of house prices: lhprice=β0+β1bdrms+β2llotsize+β3lsqrft+u estimated on a random sample of 86 houses. You may assume the Gauss Markov Assumptions hold. The variables are defined as: lhprice = the natural log of the house price bedrms = the number of bedrooms llotsize = the natural log of the land or lot size lsqrft = the natural log of the floor space of the house in square feet. Source SS df MS Model Residual 4.65621742 2.09289629 3 82 1.55207247 0.025523126 Total 6.74911372 85 0.079401338 lprice coef. std. err. t P > |t| [95% Conf. Interval] bdrms 0.0581214 -0.0055282 0.121771 llotsize 0.1494716 0.0616548 0.2372884 lsqrft 0.636171 0.421989 0.850353 _cons -0.7083136 -2.221521 0.8048935 Number of obs = 86 F (3, 82) = 60.81 Prob > F = 0.0000 R-squared = 0.6899 Adj R-squared = 0.6786 Root MSE = 0.15976 Does…Suppose the researcher considers the following model : Wage = Bo+B,Female + u, and runs OLS, using wage data on 250 randomly selected male workers and 280 female workers. The researcher has obtained the estimated equation as Wage = 15 (1.00) 3 Female, R = 0.05. (0.5) In the equation, Wage is measured in dollars per hour, Female is a binary variable that is equal to 1 if the person is a female and O if the person is a male. The numbers in the parentheses are the standard errors of the coefficients. Which Statement is NOT correct? The coefficient of Female, -3.00, is statistically significant at 5%. O The p-value for the test that Ho: B = 0, H : B 0 is less than 0.05. This regression may suffer omitted variable bias. R Since insignificant. is too low, the wage difference between men and women is The researcher can increase R- by including more regressors in the model.