Part 1: Time Series analysis: discuss the revenue data similar to what I have showed in the Sear example is expected: scatter plot of revenue; linear trend; moving average; structural break. Your company revenue may not exhibit strong seasonality. For practice purpose, you are required to fit a moving average to your revenue data nevertheless.
First explain what the graph tells you as a manager about the trend, seasonality, and structural break in this company data. Then conduct research to find out how management actually made the decisions in the data.
Note: you do not need to provide the graphs that cannot be obtained using Excel (eg. the four figures shown in the second to last slide in the Time Series Analysis ppt file)
Part 2: Linear regression using Macro data:
a. Select one variable from the macro data file that you believe can be highly correlated with the revenue data.
b. Run a simple linear regression using the macro variable as the independent variable and the revenue data as the dependent variable. Report the regression results: show the excel output.
c. Note that the dates for the revenue data and macro variables do not necessarily match each other. You will need to determine the date range that has available data for all variables and drop the extra data. For example, if the date for GDP ranges from 2000 to 2015 and the revenue data ranges from 1995 to 2016. Then you would only keep data from 2000 to 2015.
d. Comment on the significance of the slope coefficient. Briefly discuss the relationship (positive or negative) between the dependent variable and the independent variable. Is the relationship expected?
e. Report R-squared and briefly comment on the fitness of the model based on your opinion.
