a. Using the 30-week time series data in the table below, create a new Excel data file and do regression analysis on the data consisting of dependent variable Q and independent variables P, Px, A, and I.

Q = Detergent demand in cases; P = Price per case; Px = Competitor price; A = Advertising expenses; I = Household income.

Week	Q	P	Px	A	I
1	1,290	137	94	814	53,123
2	1,177	147	81	896	51,749
3	1,155	149	89	852	49,881
4	1,299	117	92	854	43,589
5	1,166	135	86	810	42,799
6	1,186	143	79	768	55,565
7	1,293	113	91	978	37,959
8	1,322	111	82	821	47,196
9	1,338	109	81	843	50,163
10	1,160	129	82	849	39,080
11	1,293	124	91	797	43,263
12	1,413	117	76	988	51,291
13	1,299	106	90	914	38,343
14	1,238	135	88	913	39,473
15	1,467	117	99	867	51,501
16	1,089	147	76	785	37,809
17	1,203	124	83	817	41,471
18	1,474	103	98	846	46,663
19	1,235	140	78	768	55,839
20	1,367	115	83	856	47,438
21	1,310	119	76	771	54,348
22	1,331	138	100	947	45,066
23	1,293	122	90	831	44,166
24	1,437	105	86	905	55,380
25	1,165	145	96	996	38,656
26	1,328	138	97	929	46,084
27	1,515	116	97	1,000	52,249
28	1,223	148	84	951	50,855
29	1,293	134	88	848	54,546
30	1,215	127	87	891	38,085

b. Show your regression output.

c. Build and present the regression model (long equation) to predict Q (dependent variable).

d. Interpret the coefficient estimate of each independent variable (i.e, when "A" increases by one unit, what changes occur in "Q"?).