The following sample observations were randomly selected. (Round intermediate calculations and final answers to 2 decimal places.) 5 5 3 6 7 y: 4 6 A) What is the regression equation is y= + X X: 5 7 B) When x is 5 this gives y = 2
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- 6 9 13 Yi 7 18 9. 26 23 a. Which of the following scatter diagrams accurately represents the data? A. 24+ 24+ 20- 20- 16+ 16+ 12+ 12+ 8+ 8+ 4- -4 4 12 16 24 28 x -4 4 8. 12 16 20 24 28 x -4t -4t С. D. 24 -----24+ 20- 20- 16- 16- 20 B. 20 00 00 O.If the sample coefficient of determination (R2) is 0.75, this means that O 75 percent of the variation in the independent variable is explained by the regression. O 75 percent of the variation in the dependent variable is explained by the regression. O 25 percent of the variation in the dependent variable is explained by the regression. O 25 percent of the variation in the independent variable is explained by the regression.Which of the following statement about R^2 is and adjusted-R^2 is correct? O a. Both Statements A and B are correct O b. The value of adjusted-R^2 can increase or decrease when additional variable is included in a regression model O C. An increase in the value of R^2 and adjusted-R^2 does not mean that the additional variable is included in a regression model Statement B is correct but Statement A is incorrect O d.
- Consider the following OLS regression results, In(inc)=1.970+.083educ, R2=.186, where inc represents annual income (in $1000s) and educ represents years of education. The R² can be interpreted as .186% of the variation in annual income is explained by years of education. .186% of the variation in log annual income is explained by years of education. O 18.6% of the variation in annual income is explained by years of education. O 18.6% of the variation in log annual income is explained by years of education.A 10-item statistics quiz was given to 30 students. The table below gives the scores received along with the corresponding frequencies. Score | Frequency 6 5 1 2 8 5 9 7 10 10 5 What was the median score on the quiz? O 7.5 8.5 9 10To determine whether the revenue made trom selling ice creams in a smal shop (in USD) and the temperature (in Celcius degrees) are related, a researcher decides to employ the simple linear regression analysis. Using a random set of days in which the temperature varies between 20 and 35, she comes up with the following regression line: Ice Cream Revenue- 200 90 Temperature Based on this analysis, what can she conclude? O A)At 20 Celcius degrees. the shop owner can xpect to sell 5000 USD O B)AI 30 Celoius degrees, the shop owner can expect to sel 2000 usD O C)As the temperature goes up by one degree Celoius, the revenue increases by approximately 00% OD)On average ce cream revenue is about 200 USD when the temperature is O degrees Celcus O E)As the temperature goes up by one degree Celcius. the reverue inereases by approximately 200 USD.
- Pls help with below homework-) Which are the three methods to handle the panel data model with fixed effects ? Also explain each methodYou estimated the following regression. What value would you predict for Y, if X = 90? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 231 -------------+---------------------------------- F(1, 229) = 67069.23 Model | 2.6971e+09 1 2.6971e+09 Prob > F = 0.0000 Residual | 9209021.29 229 40214.0668 R-squared = 0.9966 -------------+---------------------------------- Adj R-squared = 0.9966 Total | 2.7063e+09 230 11766675.6 Root MSE = 200.53 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 44.88308 .173309 258.98 0.000 44.5416 45.22457 _cons | 21.1834 18.22441 1.16 0.246…5 We are given a sample of n observations which satisfies the following regression model: yi = β0 + β1xi1 + β2xi2 + ui , for all i = 1, . . . , n. This model fulfills the Least-Squares assumptions plus homoskedasticity. (a) Explain how you would obtain the OLS estimator of the coefficients {β0, β1, β2} in this model. (You do not need to show a full proof. Writing down the relevant conditions and explain)
- Given are five observations for two variables, x and y. Excel File: data14-17.xlsx 9 13 20 Yi 7 18 26 23 The estimated regression equation for these data is ŷ = 7.6 + 0.9x . Compute SSE, SST, and SSR (to 1 decimal). SSE SST SSR What percentage of the total sum of squares can be accounted for by the estimated regression equation (to 1 decimal)? % What is the value of the sample correlation coefficient (to 3 decimals)? 2] 1001] kindly tell the difference between log -linear , linear - log ,log -log and linear - linear regression . Out of all these, which is approporaite to carry out GDP regression . 2] also , is it needed to convert all the data to "ln" by typing " =ln" ,if regression is done using excel ?Which of the following statements about the R-squared statistic of a linear estimation (or linear regression) is true? An R-squared statistic of 0.6 means the variation of the independent (explanatory) variable explains about 60% of the variation of the dependent variable. O An R-squared statistic of 0.6 means 60% the variation of the dependent variable cannot be explained by the variation of the independent (explanatory) variable. O An R-squared statistic of 0.6 means the variation of the dependent variable explains about 60% of the variation of the independent (explanatory) variable.