Problem 1: The monthly ambulatory visit shown in the Table below occurred in an outpatient clinic.

Months	Visits
July	2,160
August	2,186
September	2,246
October	2,251
November	2,243
December	2,162

1. Predict visits for January, using the nave method.
2. Predict visits for January, using a three-period moving average.
3. Predict visits for January, using a four-period moving average.

Problem 2. Patient days in a hospital were recorded as shown in the Table below.

Month	Patient Days
January	543
February	528
March	531
April	542
May	558
June	545
July	543
August	550
September	546
October	540
November	535
December	529

1. Predict nave forecast of patient days for February and June.
2. Predict the patient days for January, using a four-period moving average.
3. Predict the patient days for January, using the six-period moving average.
4. Plot the actual data and the results of the four period and the six-period moving averages. Which is a better predictor?

Problem 3. Using the visit data in Problem 1, answer the following;

1. Put together a forecast for January visits, using the simple exponential smoothing method with = 0.3.
2. If = 0.5, what is the predicted value for January visits?
3. If = 0.0, what is the predicted value for January visits
4. If = 1.0, what is the predicted value for January visits
5. Which alpha produces the best forecast?

Problem 4. An urgent care center experienced the average patient admissions shown in the Table below during the weeks from the first week of December through the second week of April.

Week	Average Daily Admissions
1-Dec	11
2-Dec	14
3-Dec	17
4-Dec	15
1-Jan	12
2-Jan	11
3-Jan	9
4-Jan	9
1-Feb	12
2-Feb	8
3-Feb	13
4-Feb	11
1-Mar	15
2-Mar	17
3-Mar	14
4-Mar	19
5-Mar	13
1-Apr	17
2-Apr	13

1. Predict forecasts for the admissions from the first week of April through the fourth week of May, using linear regression.
2. Forecast admissions for the periods from the first week of December through the second week of April. Compare the forecast admissions to the actual admissions; What do you conclude?