please help, this is all 1 problem. I solved the first part but can't solve the rest.

SALES	ADV	BONUS	MKTSHR	COMPET
963.5	374.27	230.98	33	202.22
893	408.5	236.28	29	252.77
1057.25	414.31	271.57	34	293.22
1183.25	448.42	291.2	24	202.22
1419.5	517.88	282.17	32	303.33
1547.75	637.6	321.16	29	353.88
1580	635.72	294.32	28	374.11
1071.5	446.86	305.69	31	404.44
1078.25	489.59	238.41	20	394.33
1122.5	500.56	271.38	30	303.33
1304.75	484.18	332.64	25	333.66
1552.25	618.07	261.8	34	353.88
1040	453.39	235.63	42	262.88
1045.25	440.86	249.68	28	333.66
1102.25	487.79	232.99	28	232.55
1225.25	537.67	272.2	30	273
1508	612.21	266.64	29	323.55
1564.25	601.46	277.44	32	404.44
1634.75	585.1	312.35	36	283.11
1159.25	524.56	292.87	34	222.44

Sales Management I: The Director of Sales at the Meddicorp company wants to develop a model to help explain and predict sales figures as a function of advertising expenditure (in hundreds of dollars) (ADV), bonus expenditure (in hundreds of dollars) (BONUS), market share (as a percent) (MKTSHR), and largest competitor's sales (in hundred of dollars) (COMPET). Data are available from a random sample of n = 20 territories that the company operated within during the year 2018. Sales figures are measured in thousands of dollars. The data are in the worksheet named MEDDICORP,HW7 (See the instructions of this assignment for the link to download the data in the file HW7 Data.xls). Build a regression model in EXCEL to predict Meddicorp's sales (SALES) from the four explanatory variables mentioned above. Use the results to help answer the following questions. (a) State the model equation. OSALES = B1ADV + B2BONUS + B3MKTSHR + B4COMPET O COMPET = Bo + B1ADV + B2BONUS + B3MKTSHR + B4SALES OSALES = B1ADV + B2BONUS SALES = Bo + B1ADV + B2BONUS + B3MKTSHR + B4COMPET O MKTSHR = Bo + B1ADV + B2BONUS + B3SALES + B4COMPET O SALES = Bo + B1ADV + B2BONUS (b) A consultant believes that the key to increasing sales is to increase the amount the company pays out in bonuses. More specifically, based on his independent analysis, the consultant believes that the company can expect a return on investment (ROI) of $4,500 in sales for every $100 the company spends on bonuses. Test this claim. Use a = 0.05. HINT: Remember to account for the measurement units for the response variable SALES. State the hypotheses to be tested. O Ho: B3 = 4.5 Ha: B3 4.5 O Ho: B2 = 4.5 Ha: B2 #4.5 O Ho: B2 = 0 Ha: B2 = 0 O Ho: B3 = 0 Ha: B3 0 0 O Ho: B4 = 4.5 Hai B4 4.5 Interpret the hypotheses you specified above. Ho: The ROI for spending $100 in bonuses is $4,500 in sales. The consultant's claim is supported. Ha: The ROI for spending $100 in bonuses is NOT $4,500 in sales. The consultant's claim is NOT supported. O Ho: All of the explanatory variables are important in explaining/predicting sales. Ha: None of the explanatory variables are important in explaining/predicting sales. O Ho: The ROI for spending $100 in bonuses is NOT $4,500 in sales. The consultant's claim is NOT supported. Ha: The ROI for spending $100 in bonuses is $4,500 in sales. The consultant's claim is supported. O Ho: None of the explanatory variables are important in explaining/predicting sales. Ha: At least one explanatory variable is important in explaining/predicting sales. State the decision rule. Reject Ho if p 0.05. O Reject Ho if p 0.025. Reject Ho if p > 0.025. Do not reject Ho if p 0.05. Do not reject Ho if p 0.05. O Reject Ho if p 0.025. Reject Ho if p > 0.025. Do not reject Ho if p 0.05. Do not reject Ho if p