Using bus ridership data, I created a regression model using multiple time series variables and critiqued the model for fit and accuracy.

Leveraged uni-variate time series data that was explored and fit against various models - linear regression, exponential smoothing, and ARIMA function - to find the best model in R. Model types critiqued by actual vs. fit, model summary, accuracy, residuals (including autocorrelation), and forecast.

Explored sales and advertising data in R and fitted it to a linear regression model evaluating for various diagnostics. Added additional lagged time series data to improve the model fit and added the various strategy types of data. From the final model made a sales prediction following a particular strategy type.