## Analyze the data for the Boston Red Sox from 2000-2017 for their performance in the American League,

Red Sox

Analyze the data for the Boston Red Sox from 2000-2017 for their performance in the American League,

http://boston.redsox.mlb.com/bos/history/year_by_year_results.jsp

1) Develop fitted forecasts for all possible periods (including 2017) for the percentage of winning games (PCT) using the data from 2000-2017. Develop the forecasts using a) a 3 years moving average; and b) a six years moving average.

2) Compute MAPE, RMSE and Bias for the above forecasts – explain the meaning of these error measures in simple language, using one sentence for each explanation.

3) Which moving avg. forecasting period is more accurate?

4) Which one is more biased?

5) If we had to develop a regression model, which of the two variables, PCT or Attendance, would you pick the independent and dependent variables, respectively? Describe the theory behind your choice.

6) Irrespective of your response to Q # 5 above, develop a regression model with 'PCT' as the independent variable (IV) and 'Attendance' as the independent variable (DV) – use data from years 1990-2017. Use Excel and attach the output as an excel file in the format provided (do not attach any output on errors or residuals)

7) Write the regression model in its equation form: y = a + bx, with figures for a and b from the excel output.

8) Explain what a and b mean, in simple language, using one sentence for each explanation.

9) Examine the model thus developed, using the 4 step procedure described in our forecasting module,

a) How strong is the model fit overall? Provide a measure and a number.

b) Are you sure? Provide a measure and a confidence level

c) How strong is the relationship between the IV and the DV? Provide a measure and a number.

d) Are you sure? Provide a measure and a confidence level.

10) Would you use the model developed to make forecasts? Why, or why not?

11) What other possible IVs and additional information would you consider adding to your regression model to make it even stronger?

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