# QNT 275 Week 5 Apply Connect Week 5 Case

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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.

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

Size
# Ordered

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

Dec
1495

Jan
1545

Feb
1454

Mar
1322

Apr
1492

May
1678

Jun
1645

Jul
1580

Aug
1493

Sep
1719

Oct
1573

Nov
1629

Dec
1680