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Data Models and Decisions

Autor:   •  April 17, 2018  •  1,230 Words (5 Pages)  •  719 Views

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(Thus, for example, Region 11 had 54,061 portable phones and its peak demand was 6,878 circuits.) David used linear regression for this purpose. The resulting computer output is shown below:

SUMMARY

OUTPUT

[pic 2]

Regression Statistics

Multiple R

0.97533

R Square

0.95127

Adjusted R Square

0.94856

Standard Error

3381.29

Observations

20

ANOVA

df

SS

MS

F

Regression

1

4,017,125,436

4,017,125,436

351.358278

Residual

18

205,796,369

11,433,131

Total

19

4,222,921,805

Coefficients

Standard Error

t Stat

P-value

Intercept

-9,159.91

1,396.64

-6.55855 3.6647E-06

Number of Phones

0.336564

0.017955

18.74455

2.9406E-13

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15.063 Summer 2003 Final Examination p.5 of 7

- (5 points) Write a complete equation for the simple linear regression model that incorporates the estimated coefficients provided by this computer output. Make sure to define in words all the variables used in this equation.

- (5 points) Interpret, in managerial terms, the meaning of each of the two regression coefficients (including the units in which each is expressed).

David also produced a histogram of the regression residuals, and a plot of the residuals against number of portable phones, as shown below.

Frequency

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6

5

4

3

2

1

0

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Histogram

Residuals

10000

8000

6000

Residual

4000

2000

0

0

50,000

100,000

150,000

200,000

-2000

[pic 3]

-4000

-6000

-5000 -3000 -1000 1000 3000 5000 7000 More

Number of Phones

Residual Values

For each of the statements (A) to (E) below, indicate, by circling the correct answer, whether you think the statement is true or false. If you answered “TRUE”, please provide a brief justification of your answer in the space provided. (No justification is needed if you answered “FALSE”).

(c) (5 points) These graphs show evidence of:

1. non-Normal noise.

TRUE - FALSE

If “TRUE”, how do you know?

2. autocorrelation.

TRUE - FALSE

If “TRUE”, how do you know?

3. overspecification.

TRUE - FALSE

If “TRUE”, how do you know?

4. multicollinearity.

TRUE - FALSE

If “TRUE”, how do you know?

5. heteroscedasticity.

TRUE - FALSE

If “TRUE”, how do you know?

(d) (5 points) Explain how you would go about correcting the most serious problem of the ones you found in part (c). If applicable, how would you modify the data and/or the model to implement this change?

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