Hide Assignment Information Instructions Exercise #5 For this week exercise, we need to try a few logit models (see this link for more information: LOGIT REGRESSION) If you have chosen to work with Excel, please run above three models and complete the following tables. Model 1: Run a regression model and use being a member of network and find out its impact on hospital cost? (Model 1) Model 2: For the 2nd model run a regression model and use being a member of network and find out its impact on hospital cost and hospital revenue? (Model 2) Model 3: For the 3rd model run a regression model and use being a member of network and find out its impact on ratio-Medicare-discharge and ratio-Medicaid-discharge. Based on your finding please recommend 3 policies and discuss the impact of being on a network on hospital cost, hospital revenue and out its impact on ratio-Medicare-discharge and ratio-Medicaid-discharge. Do you recommend keeping membership for a hospital? Why or why not?

Hide Assignment Information Instructions Exercise #5 For this week exercise, we need to try a few logit models (see this link for more information: LOGIT REGRESSION) If you have chosen to work with Excel, please run above three models and complete the following tables. Model 1: Run a regression model and use being a member of network and find out its impact on hospital cost? (Model 1) Model 2: For the 2nd model run a regression model and use being a member of network and find out its impact on hospital cost and hospital revenue? (Model 2) Model 3: For the 3rd model run a regression model and use being a member of network and find out its impact on ratio-Medicare-discharge and ratio-Medicaid-discharge. Based on your finding please recommend 3 policies and discuss the impact of being on a network on hospital cost, hospital revenue and out its impact on ratio-Medicare-discharge and ratio-Medicaid-discharge. Do you recommend keeping membership for a hospital? Why or why not?

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 27P: The file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living...

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Stock market analysts are continually looking for reliable predictors of stock prices. Consider the problem of modeling the price per share of electric utility stocks (Y). Two variables thought to influence this stock price are return on average equity (X1) and annual dividend rate (X2). The stock price, returns on equity, and dividend rates on a randomly selected day for 16 electric utility stocks are provided in the file P13_15.xlsx. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Also, interpret the standard error of estimate and the R-square value for these data.
A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?
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?
The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The management of a technology company is trying to determine the variable that best explains the variation of employee salaries using a sample of 52 full-time employees; see the file P13_08.xlsx. Estimate simple linear regression equations to identify which of the following has the strongest linear relationship with annual salary: the employees gender, age, number of years of relevant work experience prior to employment at the company, number of years of employment at the company, or number of years of post secondary education. Provide support for your conclusion.
The file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living below the poverty level. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Create a chart of the series with the forecasts superimposed from this optimal smoothing constant. Does it make much of an improvement over the model in part b? d. Write a short report to summarize your results. Considering the chart in part c, would you say the forecasts are good?
The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b? Is it guaranteed to produce better forecasts for the future?
Sales over the past 10 years are shown below. Using the method of least squares, calculate the values of a and b to complete this regression equation. Year Sales 1 1,640 2 1,850 3 1,240 4 1,250 5 1,980 6 1,400 7 1,060 8 1,430 9 1,050 10 1,990 (Indicate a negative answer using a "-" sign. Do not round intermediate calculations and round your answer to 3 decimal places.) What is your forecast for next year's sales? (Do not round intermediate calculations and round your answer to 2 decimal places.)

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Instructions
Exercise #5 For this week exercise, we need to try a few logit models (see this link for more information: LOGIT REGRESSION) If you have chosen to work with Excel, please run above three models and complete the following tables. Model 1: Run a regression model and use being a member of network and find out its impact on hospital cost? (Model 1) Model 2: For the 2^nd model run a regression model and use being a member of network and find out its impact on hospital cost and hospital revenue? (Model 2) Model 3: For the 3^rd model run a regression model and use being a member of network and find out its impact on ratio-Medicare-discharge and ratio-Medicaid-discharge. Based on your finding please recommend 3 policies and discuss the impact of being on a network on hospital cost, hospital revenue and out its impact on ratio-Medicare-discharge and ratio-Medicaid-discharge. Do you recommend keeping membership for a hospital? Why or why not?