Simple linear regression analysis using excel

Reference no: EM1322291

A brokerage house wants to predict the number of trade executions per day, using the number of incoming phone calls as a predictor variable. Data were collected over a period of 35 days and are stored in the file trades.xls attached.

a. Use the least-squares method to compute the regression of coefficients b0 and b1.

b. Interpret the meaning of b0 and b1 in this problem.

c. Predict the number of trades executed for a day in which the number of incoming calls is 2,000.

d. Should you use the model to predict the number of trades executed for a day in which the number of incoming calls is 5,000? Why or why not?

e. Determine the coefficient of determination, r2, and explain its meaning in this problem.

f. Plot the residuals against the number of incoming calls and also against the days. Is there any evidence of a pattern in the residuals with either of these variables? Explain.

g. Determine the Durbin-Watson statistic for these data.

h. Based on the results of (6) and (7), is there reason to question the validity of the model? Explain.

i. At the 0.05 level of significance, is there evidence of a linear relationship between the volume of trade executions and the number of incoming calls?

j. Construct a 95% confidence interval estimate of the mean number of trades executed for days in which the number of incoming calls is 2,000.

k. Construct a 95% prediction interval of the number of trades executed for a particular day in which the number of incoming calls is 2,000.

l. Construct a 95% confidence interval estimate of the population slope.

m. Based on the results of (1) through (9), do you think the brokerage house should focus on a strategy of increasing the total number of incoming calls or on a strategy that relies on trading by a small number of heavy trades? Explain.

Day	Calls	Trade Executions	X	Y	X2	XY
1	2591	417	2591	417	6713281	1080447
2	2146	321	2146	321	4605316	688866
3	2185	362	2185	362	4774225	790970
4	2245	364	2245	364	5040025	817180
5	2600	442	2600	442	6760000	1149200
6	2510	386	2510	386	6300100	968860
7	2394	370	2394	370	5731236	885780
8	2486	376	2486	376	6180196	934736
9	2483	463	2483	463	6165289	1149629
10	2297	389	2297	389	5276209	893533
11	2106	302	2106	302	4435236	636012
12	2035	266	2035	266	4141225	541310
13	1936	339	1936	339	3748096	656304
14	1951	369	1951	369	3806401	719919
15	2292	403	2292	403	5253264	923676
16	2094	319	2094	319	4384836	667986
17	1897	306	1897	306	3598609	580482
18	2237	397	2237	397	5004169	888089
19	2328	365	2328	365	5419584	849720
20	2078	330	2078	330	4318084	685740
21	2134	312	2134	312	4553956	665808
22	2192	340	2192	340	4804864	745280
23	1965	339	1965	339	3861225	666135
24	2147	364	2147	364	4609609	781508
25	2015	295	2015	295	4060225	594425
26	2046	292	2046	292	4186116	597432
27	2073	379	2073	379	4297329	785667
28	2032	294	2032	294	4129024	597408
29	2108	329	2108	329	4443664	693532
30	1923	274	1923	274	3697929	526902
31	2069	326	2069	326	4280761	674494
32	2061	306	2061	306	4247721	630666
33	2010	352	2010	352	4040100	707520
34	1913	290	1913	290	3659569	554770
35	1904	283	1904	283	3625216	538832
			75483	12061	164152689	26268818

Average	2156.657	344.6
	200.1277	47.65266

Reference no: EM1322291

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