# Excel’s Exponential Smoothing option

QNT 275 Week 5 Apply Connect Week 5 Case

You are the manager of a retail store. You want to investigate how metrics can improve the way you manage your business.

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Use the Week 5 Data Set to create and calculate the following in Excel®:

Conduct a goodness of fit analysis which assesses orders of a specific item by size (expected) and items you received by size (observed).

Conduct a hypothesis test with the objective of determining if there is a difference between what you ordered and what you received at the .05 level of significance.

Identify the null and alternative hypotheses.

Generate a scatter plot, the correlation coefficient, and the linear equation that evaluates whether a relationship exists between the number of times a customer visited the store in the past 6 months and the total amount of money the customer spent.

Set up a hypothesis test to evaluate the strength of the relationship between the two variables.

Use a level of significance of .05.

Use the regression line formula to forecast how much a customer might spend on merchandise if that customer visited the store 13 times in a 6 month period.

Consider the average monthly sales of 2014, \$1310, as your base then

Calculate indices for each month for the next two years (based on the 24 months of data).

Graph a time series plot.

In the Data Analysis Toolpak, use Excel’s Exponential Smoothing option.

Apply a damping factor of .5, to your monthly sales data,  then create a new time series graph that compares the original and the revised monthly sales data.

ORDERS VS. SHIPMENTS

Extra Small     30        23

Small   50        54

Medium           85        92

Large   95        91

Extra Large     60        63

2X Large         45        42

CUSTOMERS IN PAST 6 MONTHS

Customer #      # Visits            \$ Purchases

1          8          468

2          6          384

3          8          463

4          2          189

5          10        542

6          4          299

7          6          345

8          2          197

9          4          293

10        1          119

11        3          211

12        9          479

13        7          430

14        7          404

15        6          359

16        10        544

17        9          522

18        5          327

19        6          353

20        7          405

21        4          289

22        7          386

23        7          403

24        1          146

25        7          416

26        9          485

27        3          333

28        7          241

29        2          391

30        6          268

MONTHLY SALES (\$)

Month \$ Sales

Jan       1375

Feb      1319

Mar      1222

Apr      1328

May     1493

Jun       1492

Jul        1489

Aug     1354

Sep      1530

Oct      1483

Nov     1450

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