Month/Yr.	PERIOD	PRICE	AIP	DIFF	ADV	DEMAND
Jan. 2016	1	5.9	6.0	0.1	6.4	13.9
	2	5.9	6.3	0.4	6.1	15.0
	3	6.0	6.4	0.4	7.3	15.7
	4	6.1	6.1	0.0	7.3	15.9
	5	5.9	6.4	0.5	7.2	15.9
	6	5.9	6.3	0.4	6.5	15.6
	7	5.9	6.0	0.1	6.8	15.1
	8	6.8	6.0	-0.8	5.0	14.8
	9	6.8	5.8	-1.0	5.8	15.2
	10	6.4	6.3	-0.1	5.5	14.9
	11	6.5	6.3	-0.2	6.5	14.9
	12	6.3	6.2	-0.1	6.3	13.9
Jan. 2017	13	6.1	6.5	0.4	7.0	15.0
	14	6.1	6.6	0.5	7.7	15.5
	15	6.0	6.3	0.3	6.8	17.0
	16	6.4	6.7	0.3	6.8	13.0
	17	6.2	6.5	0.3	7.1	13.2
	18	6.0	6.8	0.8	7.0	16.2
	19	6.1	6.6	0.5	7.2	16.9
	20	6.4	6.1	-0.3	7.5	17.2
	21	6.0	6.1	0.1	7.8	16.0
	22	6.2	6.2	0.0	8.2	17.9
	23	6.1	6.0	-0.1	8.3	18.1
	24	6.0	6.2	0.2	8.4	15.5
Jan. 2018	25	6.1	6.7	0.6	8.9	18.4
	26	5.9	6.9	1.0	9.1	18.7
	27	6.0	5.8	-0.2	9.3	18.6
	28	6.3	5.8	-0.5	9.4	19.4
	29	6.0	6.0	0.0	9.3	19.6
	30	5.7	6.7	1.0	9.4	18.4
	31	5.6	6.4	0.8	9.5	19.4
	32	6.2	7.0	0.8	9.6	18.6
	33	6.4	7.2	0.8	9.7	17.7
	34	6.5	5.9	-0.6	9.9	19.2
	35	6.2	6.0	-0.2	9.8	19.5
	36	6.7	6.2	-0.5	9.9	17.5
Jan. 2019	37	6.9	6.0	-0.9	10.1	20.0
	38	6.9	6.3	-0.6	10.2	20.0
	39	6.7	6.5	-0.2	10.5	20.8
	40	7.0	6.0	-1.0	10.3	20.8
	41	7.1	6.1	-1.0	9.9	21.0
	42	7.2	6.3	-0.9	10.5	21.9
	43	7.2	6.4	-0.8	10.6	20.6
	44	7.3	6.5	-0.8	10.5	20.5
	45	7.2	6.0	-1.2	11.6	19.4
	46	7.1	6.2	-0.9	10.1	20.3
	47	6.9	5.9	-1.0	10.3	20.3
	48	7.2	6.0	-1.2	10.7	18.2
Jan. 2020	49	7.3	6.4	-0.9	10.9	21.0
	50	7.4	6.5	-0.9	10.8	21.1
	51	7.5	6.5	-1.0	11.1	21.1
	52	7.0	6.2	-0.8	11.2	21.2
	53	6.8	6.8	0.0	11.6	22.1
	54	7.4	6.9	-0.5	11.5	21.6
	55	7.3	6.5	-0.8	11.6	21.7
	56	7.3	6.9	-0.4	11.9	22.3
	57	7.2	7.0	-0.2	11.8	22.4
	58	7.5	6.8	-0.7	11.9	22.5
	59	7.5	6.8	-0.7	11.9	22.8
	60	7.5	6.5	-1.0	11.9	20.2
Jan. 2021	61	7.5	6.2	-1.3	12.0	22.6
	62	7.4	5.9	-1.5	12.1	23.1
	63	7.2	5.6	-1.6	12.6	23.5
	64	7.3	5.3	-2.0	12.3	23.4
	65	7.6	5.0	-2.6	12.1	23.1
	66	7.4	5.2	-2.2	12.5	23.5
	67	8.0	5.5	-2.5	12.5	23.6
	68	8.1	6.0	-2.1	13.2	23.9
	69	8.2	6.5	-1.7	13.5	24.2
	70	8.1	6.5	-1.6	14.5	24.0
	71	8.1	6.0	-2.1	14.2	24.2
	72	8.4	6.5	-1.9	14.9	23.9
Jan. 2022	73	8.5	6.3	-2.2	12.0	23.0
	74	8.6	5.7	-2.9	14.0	24.5
	75	8.3	5.9	-2.4	14.1	24.7
	76	8.5	5.3	-3.2	14.0	24.6
	77	8.6	5.0	-3.6	14.3	24.8
	78	8.5	5.8	-2.7	14.3	24.8
	79	8.7	5.8	-2.9	14.9	24.9
	80	8.5	5.6	-2.9	15.0	24.9
	81	8.7	5.2	-3.5	15.1	25.0
	82	8.8	5.1	-3.7	15.1	25.6
	83	8.7	5.0	-3.7	14.9	25.5
	84	8.9	5.3	-3.6	14.9	25.6
Jan. 2023	85
Feb. 2023	86
Mar. 2023	87

The Fresh Detergent Case

Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 84 sales periods. Each sales period is defined as one month.The variables are as follows:

Demand = Y = demand for a large size bottle of Fresh (in 100,000)

Price = the price of Fresh as offered by Ent. Industries

AIP = the average industry price

ADV = Ent. Industries Advertising Expenditure (in $100,000) to Promote Fresh in the sales period.

DIFF = AIP - Price = the "price difference" in the sales period

1. Make time series scatter plots of all five variables (five graphs). Insert trend line, equation, and R-squared. Observe graphs and provide interpretation of results.

1. Construct scatter plots of Demand vs. DIFF and Demand vs. ADV, Demand vs. AIP, and Demand vs. Price. Insert fitted line, equation, and R-squared. Observe graphs and provide interpretation. Note that Demand is always on the Y axis.

1. Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand. High correlation is r > 0.80. Explain your findings in plain language.