What are similarities of Interrupted Time Series and Regression Discontinuity? What are some differences?
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What are similarities of Interrupted Time Series and Regression Discontinuity? What are some differences?
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- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?Under what conditions might a firm use multiple forecasting methods?Chart and Regression analysis : What does the intercept predict? X: C16 (number of cars on the sales lot) versus Y: C17 (cars sold per day) Equation: y=2.9x + 14.5 Slope:2.9 Intercept:14.5 Does the intercept mean the intercept is 14.5 means that the cars sold per day( Y) predicted number of cars on sale lot(X) to be 14.5, but this intercept has no meaning. So, I will not use to predict cars sold per day?
- I obtained a ridge regression model by k-fold cross-validation using cv_glmnet in R. I need to calculate adjusted R^2 and VIFs for it but I don't know howFind the equation of regression lines and correlation coefficient from the following data. X 28 41 40 38 35 33 46 32 36 33 Y 30 34 31 34 30 26 28 31 26 31Why is an expanded concept of "variation" necessary in statistical improvement?
- Regression analysis. The owner of a small hardware store has noted a sales pattern for window locks that seems to parallel the number of break-ins reported each week in the newspaper. The data are: Sales: 46 18 20 22 27 34 14 37 30Break-ins: 9 3 3 5 4 7 2 6 4a. Plot the data to determine which type of equation, linear or nonlinear, is appropriate.b. Obtain a regression equation for the data.c. Estimate average sales when the number of break-ins is five.calculate the intercept for the regression lineWhy would we graph scatterplots and regression lines?
- what are the Descriptive Statistics, Ordinal Scale and Dichotomous Variable? What are the differences among them?Athlete ages A sports reporter suggests that professional baseball players must, on average, be older than profes-sional football players, since football is a contact sport and players are more susceptible to concussions and seri-ous injuries (www.sports.yahoo.com). One player was selected at random from each team in both professionalbaseball (MLB) and professional football (NFL). Thedata are summarized below.To perform inference on these two samples, what condi-tions must be met? Are they? Explain. MLB NFLn 30 32y 27.5 26.16s 3.94 2.781.) Use the following dummy variables to develop an estimated regression equation to account for seasonal effects only in the data. Qtr1 = 1 if Quarter 1, 0otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise, Qtr3 = 1 if Quarter 3, 0otherwise. Based only on the seasonal effects in the data, compute estimates of quarterly sales for year 6.a. Report the estimate of sales for Year 6 Quarter 1. (Enter a whole value.)b. Report the estimate of sales for Year 6 Quarter 4. (Enter a whole value.)2.) Let Period t = 1 refer to the observation in quarter 1 of year 1; Period t = 2refer to the observation in quarter 2 or year 1; … and Period t = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and Period (t), develop an estimated regression equation to account for seasonal effects and any linear trend in the time series. Based upon the seasonal effects in the data and linear trend, compute the estimates of quarterly sales for year 6.a. Report the estimate…
