This could include graphs of the independent variables against the dependent variable, a correlation matrix to argue the inclusion/exclusion of variables in the model, the R² values, the standard errors, the coefficients of the model (with their units), the significance of the coefficients (p-values), and/or any other type of analysis we have covered in the first five weeks of the course. Weave these items into a story about how you determined that your model is the best one for the data set given.

	Purchase Volume Y	Age X1	Family Income X2	Family Size X3	Gender X4 1= male 0=female	Homeowner X5 1= no 0=yes
	75	42	29000	4	1	1
	129	36	28000	2	0	0
	105	38	32000	2	0	0
	42	54	17000	3	1	1
	17	49	15000	2	1	1
	26	55	19500	3	1	1
	144	25	34000	2	1	0
	100	24	27000	4	0	1
	92	30	25000	3	0	1
	58	35	20250	2	1	1
	111	27	29000	3	0	0
	146	29	38000	2	0	0
	93	38	25000	4	0	1
	68	40	24000	3	1	1
	11	57	22500	2	1	1
	50	41	17750	3	1	1
	75	36	22000	3	0	1
	88	42	19200	4	0	1
	100	44	30000	4	0	0
	86	48	21400	3	0	0
	105	30	35000	2	0	0
	121	27	34000	3	0	0
	14	62	15000	1	1	1
	37	50	18100	2	1	1
	43	39	23500	2	1	1