Cost Revenue Data Analyses Excel Instructions e data on the next sheet represent
ID: 408677 • Letter: C
Question
Cost Revenue Data Analyses Excel
Instructions e data on the next sheet represents weekly sales for a pack of bathroom tiles at a retail store in Atlanta, GA. The stocking policy at the store is to replenish and hold 250 units in stock at the beginning of each week. Your task: 1. Use simple exponential smoothing to forecast the weely demand for weeks 105- 108. You will need to decide which dataset to use, the actual observed sales or the unconstrained demand. 2. In the dataset you choose to use, separate the historical data into an estimation sample (first 3/4 rows of the selected dataset from part 1) and a holdout sample (remaining 1/4 rows of the selected dataset from part 1). 3. Choose a smoothing constant that minimizes the Mean Absolute Error (MAE) over the estimation dataExplanation / Answer
Answer :-
When people first encounter the term Exponential Smoothing they may think
“that sounds like a hell of a lot of smoothing . . . whatever smoothing is". They then start to envision a complicated mathematical calculation that likely requires a degree in mathematics to understand, and hope there is a built-in Excel function available if they ever need to do it. The reality of exponential smoothing is far less dramatic and far less traumatic.
The truth is, exponential smoothing is a very simple calculation that accomplishes a rather simple task. It just has a complicated name because what technically happens as a result of this simple calculation is actually a little complicated.
To understand exponential smoothing, it helps to start with the general concept of “smoothing” and a couple of other common methods used to achieve smoothing.
We consider statistical criteria for partitioning a reference database to obtain separate reference ranges for different subpopulations. Using general formulas relating population variances, sample sizes, and the normal deviate test for the significance of the difference between two subgroup means, we show that partitioning into separate ranges produces little reduction in between-person variability, even when the differences between means are highly significant statistically. However, when there is a clear physiological basis for distinguishing between certain subgroups, simulation studies show that partitioning may be necessary to obtain reference limits that cut off the desired proportions of low and high values in each subgroup. Guidelines based on these results are provided to help decide whether separate ranges should be obtained for a given analyte.
Exponential smoothing technique is one of the most important quantitative techniques in forecasting. The accuracy of forecasting of this technique depends on exponential smoothing constant. Choosing an appropriate value of exponential smoothing constant is very crucial to minimize the error in forecasting. This paper addresses the selection of optimal value of exponential smoothing constant to minimize the mean square error (MSE) and mean absolute deviation (MAD). Trial and error method is used to determine the optimal value of exponential smoothing constant. An example is presented to discuss the method. Abstract-Exponential smoothing technique is one of the most important quantitative techniques in forecasting. The accuracy of forecasting of this technique depends on exponential smoothing constant. Choosing an appropriate value of exponential smoothing constant is very crucial to minimize the error in forecasting. This paper addresses the selection of optimal value of exponential smoothing constant to minimize the mean square error (MSE) and mean absolute deviation (MAD). Trial and error method is used to determine the optimal value of exponential smoothing constant. An example is presented to discuss the method.