If you are the manager of a grocery company with 50 stores across the city. The excel file contains data on the demand for grocery delivery:

Delivery (Y) is the number of delivery per month

Price (X1) is the average price of a delivery

Income (X2) is average income in the neighbourhood

Location	Delivery (Y)	Price (X1)	Income (X2)
1	13282	20	60371
2	10819	20	38520
3	9134	27	35696
4	13639	25	68050
5	13312	30	75874
6	17351	11	76808
7	16048	18	80063
8	8449	20	18456
9	7517	28	24949
10	6821	23	8352
11	16779	22	89554
12	9822	26	38471
13	14579	11	53832
14	9855	14	18661
15	14731	22	73971
16	8834	16	13187
17	6712	20	5922
18	8760	12	7423
19	15741	15	71016
20	9516	18	23251
21	6601	29	19886
22	17522	16	86367
23	16293	22	88489
24	17862	19	95633
25	12225	24	58008
26	11424	28	54972
27	16781	25	95223
28	18150	16	91013
29	9769	19	26102
30	14890	24	78469
31	8580	20	19684
32	9419	12	14296
33	16676	13	77037
34	15603	17	73354
35	15903	30	97961
36	17785	14	87681
37	13250	25	67846
38	7959	12	2895
39	6142	26	7867
40	10895	21	40952
41	18763	10	86958
42	9212	25	34244
43	13941	26	73846
44	16636	19	84855
45	16785	19	86327
46	9149	25	32398
47	5341	28	7312
48	13066	18	52588
49	10264	21	35408
50	9509	21	30831

You are required to use the Microsoft Excel package (the data analysis section) to finish the specific tasks below.

Task 1: Trend Line (Curve-Fitting) Analysis

Plot the scatter plot for #Delivery (y-axis) and Income (x-axis). Add the trendline with the equation and R-square. Interpret the R-square and comment on the slope coefficient. Suggest one use or application for this information.

Task 2: Simple Regression

Estimate the regression model for #delivery (Y) against price (X1). Interpret the estimated value of the coefficient for price. What are the values of the 95% confidence interval for price and what does it mean? Plot and comment on the residuals

Task 3: Multiple Regression

Estimate the regression model for #delivery (Y) against price (X1) and income (x2). What does the Rsquare say about the model? Why is the R-square in this model higher than the model in task 2 (both in terms of statistical and economics principles? Comment on the coefficients of price and income. Why is the coefficient of price different in this model than the one in task 2 (both in terms of statistical and economics principles? What is the substitution rate/ratio or trade-off between income and price, keeping quantity demanded constant?

Task 4: Application

• If you were to buy a franchise and two locations were offered to you, with the following characteristics:

	Location Alpha	Location Beta
Average Price	18	23
Average Income	60,000	45,000

If the cost of delivery is $5 a parcel and overheads (fixed cost) are the same for both locations ($3000), which option would you choose. Provide evidence to support your choice.

Answer rating: 100% (QA)

Task 1 R square of 09168 suggests strong correlation between variables number of deliveries per month and income as 9168 of deliveries are explained by income Slope coefficient is positive equal to 01... View the full answer