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Correlation and regression analysis for brokerage house

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:

a. Use the least-squares method to compute the regression 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 5000? Why or why not?
e. Determine the coefficient of determination, r^2, and explain its meaning in this problem.
f. n/a
g. n/a
h. n/a
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 2000.
k. Construct a 95% prediction interval of the number of trades executed for a particular day in which the number of incoming calls is 2000.

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Solution Summary

Step by step method for regression analysis is discussed here. Regression coefficients, coefficient of determination, scatter diagram and significance of regression model are explained in the solution.

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