Use RStudio to answer the following questions. Provide your written answers, along with any relevant tables and charts, in a single PDF file. Any charts included in your report should be properly labeled and formatted for an audience of company executives. Do not include R code in your PDF report. RMarkdown is not required or suggested for this assignment. You should also submit a single .R script file with your code for the analysis.
Regression Analysis.
1. Because customers value flexibility in their commuting plans, CVE allows customers to cancel a booking without penalty up until the van they booked arrives at their chosen stop. As a result, not all ride bookings result in a ride actually taking place. Estimate a simple linear regression model to understand the relationship between daily bookings and daily completed rides. Report the estimated regression equation and R2 value and interpret them in words.
Professor Kate Ashley
MISM 6202
3. Create a well-formatted and labeled scatter plot to visually inspect the ‘rides’ variable.
Describe any trend and seasonality that appear to be present.
4. Construct a k-period simple moving average for the rides variable, where k is chosen based on your assessment of the seasonality patterns in the data. Explain your choice of k and report MSE, MAD, and MAPE for this forecasting model.
5. Estimate a linear trend model for the ‘rides’ variable. Report the estimated linear trend equation and the R2 of the model, and interpret both the equation and the R2 in words.
6. Estimate a linear trend model with day-of-week dummy variables for the ‘rides’ variable.
Interpret both the estimated regression equation and the R2 in words, and comment on the magnitude of the adjusted R2 relative to the adjusted R2 from the regression you
performed in (5).
