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

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.

ORDERS VS. SHIPMENTS				CUSTOMERS IN THE PAST 6 MONTHS				MONTHLY SALES ($)
Size	# Ordered	# Received		Customer #	# Visits	$ Purchases		Month	$ Sales
Extra Small	30	23		1	8	468		Jan	1375
Small	50	54		2	6	384		Feb	1319
Medium	85	92		3	8	463		Mar	1222
Large	95	91		4	2	189		Apr	1328
Extra Large	60	63		5	10	542		May	1493
2X Large	45	42		6	4	299		Jun	1492
				7	6	345		Jul	1489
				8	2	197		Aug	1354
				9	4	293		Sep	1530
				10	1	119		Oct	1483
				11	3	211		Nov	1450
				12	9	479		Dec	1495
				13	7	430		Jan	1545
				14	7	404		Feb	1454
				15	6	359		Mar	1322
				16	10	544		Apr	1492
				17	9	522		May	1678
				18	5	327		Jun	1645
				19	6	353		Jul	1580
				20	7	405		Aug	1493
				21	4	289		Sep	1719
				22	7	386		Oct	1573
				23	7	403		Nov	1629
				24	1	146		Dec	1680
				25	7	416
				26	9	485
				27	3	333
				28	7	241
				29	2	391
				30	6	268