For the Hawkins Company, the monthly percentages of all shipments received on time over the past 12 months are 80, 82, 84, 83, 83, 84, 85, 84, 82, 83, 84, and 83. Compare a three-month moving average forecast with an exponential smoothing forecast for α = 0.2. Which provides the better forecasts using MSE as the measure of model accuracy? Do not round your interim computations and round your final answers to three decimal places.   Moving average Exponential smoothing MSE       What is the forecast for next month? If required, round your answer to two decimal places.

According to the CDC, cholesterol levels can be reduced by following a healthy diet, getting regular exercise, and reducing the amount of alcohol consumed on a weekly basis. One method of monitoring overall cholesterol levels is to calculate your cholesterol ratio (LDL/HDL), in general a cholesterol ratio between 3.5 and 5.0 is considered to be healthy for most adults. Suppose you are a clinical researcher testing the effects of these lifestyle changes on patient cholesterol levels. In order to determine if following these guidelines helps to reduce overall cholesterol, a total of 18 participants (N = 18) with moderate to high cholesterol ratios (i.e., 3.91 to > 5.0) were recruited for a healthy lifestyle study and their cholesterol ratio was calculated three times over a six week period: Week 0 (baseline), Week 3, and Week 6.  Using the variables "Week 0", "Week 3", and "Week 6" in the data set, conduct a One-Way, Within Subjects (AKA Repeated Measures) ANOVA, at α = 0.05, to see…

The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers):   A.What would be the typical linear regression equation for the number of sales?    B.Make the sales forecast for period 15 of next year.    C. Make the sales forecast for period 17 of next year.      D. What is the standard error for the data?

The number of banks in a country has been dropping steadily since 1984, and the trend in recent years has been roughly linear. The annual data for the years 1999 through 2008 can be summarized as follows, where x represents the years since 1990 and y the number of banks, in thousands. n=10 Ex-235 ²-5605 Ey-77.564 Ey²=603.80424 Exy-1810.155 a. Find an equation for the least squares line. b. Use your result from part a to predict the number of banks in the year 2020. c. If this trend continues linearly, in what year will the number of banks drop below 6200? d. Find and interpret the correlation coefficient.