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# Regression

You work for a brokerage house and management has asked that you develop a model that will 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 presented below.

1) Chart the data.
2) Comment on the relationship of the two variables.
3) Produce the regression output. What do the following statistics tell you, explained in both conceptual terms and in relation to the business transaction.
a) What does y and r2 tell us?
b) What do t-stat tell us for "calls"?
c) What does the p-value tell us about "calls"?
d) What is the confidence interval telling us about calls and trades?

4) Predict the number of trades executed for a day in which the number of incoming calls is 2,000.
5) 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?
6) You want to create a summary table for management that shows the relationship of calls in trades in increments of 100 with results rounded to the nearest 10s place. Based on your answer to #5, and the technique that you used in #4, build such a summary table.

Data
1 2591 417
2 2146 321
3 2185 362
4 2245 364
5 2600 442
6 2510 386
7 2394 370
8 2486 376
9 2483 463
10 2297 389
11 2106 302
12 2035 266
13 1936 339
14 1951 369
15 2292 403
16 2094 319
17 1897 306
18 2237 397
19 2328 365
20 2078 330
21 2134 312
22 2192 340
23 1965 339
24 2147 364
25 2015 295
26 2046 292
27 2073 379
28 2032 294
29 2108 329
30 1923 274
31 2069 326
32 2061 306
33 2010 352
34 1913 290
35 1904 283

** See attachment **

#### Solution Summary

Develop a regression model that will predict the number of trade executions per day, using the number of incoming phone calls as a predictor variable.

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