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 0.25, this means that O 25 percent of the variation in the independent variable is explained by the regression. 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. 25 percent of the variation in the dependent 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.