3. Consider the following regression results = 36,900 + 200G, +320 E, +23000, (1040) (9.73) (19.69) N=25 R²=0.69 uppose the salary of a lecturer (S) is a function of gender, years of lecturing experience and alifications. (Standard errors in brackets) here Si=Salary of the ith lecturer Gi= A dummy variable equal to 1 if the ith lecturer is male and 0 otherwise Ei = The years of lecturing experience Qi = A dummy variable equal to 1 if the ith lecturer has a PhD and 0 otherwise Find a. Interpret the coefficient estimates of dummy variables Gi and Qi
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- The following table gives the data for the hours students spent on homework and their grades on the first test. The equation of the regression line for this data is yˆ=43.097+1.15x. This equation is appropriate for making predictions at the 0.01 level of significance. If a student spent 32 hours on their homework, make a prediction for their grade on the first test. Round your prediction to the nearest whole number. Hours Spent on Homework and Test Grades Hours Spent on Homework 30 30 31 42 11 27 34 47 5 29 Grade on Test 83 75 75 96 45 76 97 85 53 7510. You estimated a regression with the following output. Source | SS df MS Number of obs = 333 -------------+---------------------------------- F(1, 331) = 4608.21 Model | 32636494.1 1 32636494.1 Prob > F = 0.0000 Residual | 2344225.8 331 7082.25316 R-squared = 0.9330 -------------+---------------------------------- Adj R-squared = 0.9328 Total | 34980719.9 332 105363.614 Root MSE = 84.156 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 30.79902 .4537022 67.88 0.000 29.90652 31.69153 _cons | 20.85313 42.1964 0.49 0.621 -62.1538 103.8601…In a regression analysis if SSE = 64 and SSR = 364, then what is the coefficient of determination? (please keep 2 decimal places)
- ** Based on the regression results, answer the following questions ** A sample of data is collected (from 1999 and 2000) concerning the compensation of the executives (compensation is measured in 1000’s of $’s) of a number of public companies along with other firm-specific data. The dependent variable is total compensation, CEOANN is a dummy variable =1 for an individual who is a CEO and =0 for individuals who are not CEO’s, EMPL is total employees, MKTVAL is the natural logarithm of the market value of the firm, EPSIN is earnings per share, YEAR is a dummy variable = 1 for the year 2000 and =0 for year 1999, and ASSETS is the natural logarithm of the total assets of the company. Based on the regression results, answer the following questions b) What is the estimated regression equation? c) What percentage of the variation in income in explained by the regressors? d) What is the standard error of the error term in the regression equation?The line of best fit through a set of data is y = 13.661 – 3.451x According to this equation, what is the predicted value of the dependent variable when the independent variable has value 80? y = Round to 1 decimal place.The calculation of the coefficient of determination r depends on the number of independent variables. An adjusted value of based on the number of degrees of freedom is calculated using the formula shown below, where n is the number of data pairs and k is the number of independent variables. =1-(1-7)(n-1)] adj n-k-1 Data were found on eight pre-owned sedans of a certain make. Suppose a multiple regression on these data has 6 independent variables. The coefficient of determination is found to be 0.972 based on a sample of 24 paired observations. After calculating rdi, determine the percentage of the variation in y that can be Compare this result with the one obtained using . adj explained by the relationships between variables according to r th %3D 12 adj (Round to three decimal places as needed.) % of the variation in y can be explained by the relationships between variables. About on (Round to one decimal place as needed.) The value of radi the value of 2. is adj uest Que Quest Enter…
- 1.64 and 1.63 is incorrect please help ()()))If you have a b of 0.56 in a regression equation, what does this mean? For every one-unit increase in x, you get an increase of 0.56 in y r = .31 On average, the variability of real scores around the regression line is 0.56 For every 1 standard deviation increase in x, you get an increase of 0.56 standard deviations in y3. Suppose that the following import function for Turkey is estimated for Turkey between 1980-2015. Import, a, + a,GDP; + ażER¸ + ut In order to measure the impact of 2001 crisis the regression is estimated based on the whole and two subsamples and the following RSS are obtained. Time period: 1980-2000 , RSS1= 69 Time period: 2001-2015, RSS2 =35 Time period: 1980-2015 , RSS = 160 Carry out the Chow test whether the regressions for the two periods are different at 5% significance level. (35 P)
- An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.Suppose the following estimated regression equation was determined to predict salary based on years of experience. Estimated Salary = 29,136.63 +2257.51(Years of Experience) What is the estimated salary for an employee with 24 years of experience? Answer Keypad Keyboard Shortcuts TablesП. 2. What is the degrees of freedom in a multiple regression model( with n values in each variable) with 14 independent variables when doing a t-test for the individual regression coefficients determined?