Description
For this set of practice problems you should not use any forecasting packages. Instead write the code
yourself as demonstrated in the lessons. However, you may wish to compare your answers to those of your
peers and to those from functions in packages.
1. The built-in dataset USArrests contains statistics about violent crime rates in the US States.
Determine which states are outliers in terms of murders. Outliers, for the sake of this question, are
dened as values that are more than 1.5 standard deviations from the mean.
2. For the same dataset as in (1), is there a correlation between urban population and murder, ,
as one goes up, does the other statistic as well? Comment on the strength of the correlation. Calculate
the Pearson coecient of correlation in R.
3. Based on the data on the growth of mobile phone use in Brazil (you’ll need to copy the data
and create a CSV that you can load into R or use the function from the gsheet package),
forecast phone use for the next time period using a 2-year weighted moving average (with weights of
5 for the most recent year, and 2 for other), exponential smoothing (alpha of 0.4), and linear
regression trendline.
4. Calculate the squared error for each model, , use the model to calculate a forecast for each
given time period and then the squared error. Finally, calculate the average (mean) squared error for
each model. Which model has the smallest mean squared error (MSE)?
5. Calculate a weighted average forecast by averaging out the three forecasts calculated in (3)
with the following weights: 4 for trend line, 2 for exponential smoothing, 1 for weighted moving
average. Remember to divide by the sum of the weights in a weighted average
