Question

USING MINITAB

13.2 - Multiple Linear Regression Exercise – Stock Price

Data File: as above

The stock broker now wants to include additional predictor variables to determine the driving factors for the stock price increase of Company A. Perform a multiple linear regression analysis between Company A and the 10 Yr. T, GDP and Unemployment (columns C1, C3, C4, and C5). Use the Minitab data file Regression.MTW.

Company A   Company B   10-Yr T   GDP ($millions)   Unemployment
36.25   30   2.33   7308755   0.049999
37.25   31.13   2.35   7664060   0.0489
37.75   29.63   2.59   8100201   0.046998
38.25   29.75   2.35   8608515   0.0456518012316533
39.88   34.25   2.33   9089168   0.0455796
40.88   32.13   2.39   9660624   0.0440165865291262
39.13   30.88   2.35   10284779   0.0434307310031873
40   39.13   2.26   10621824   0.0430990467039838
41.5   42.38   2.61   10977514   0.0430125479866333
38.63   35.88   2.34   11510670   0.0424427282488884
40.13   32   2.37   12274928   0.0418140882794917
41.88   46.13   2.37   13093726   0.0417299158834746
40.5   42.88   2.46   13855888   0.0412573398952287
42.75   47.5   2.78   14477635   0.041
42.88   49.25   2.81   14718582   0.040777693159957
42.5   46.5   2.73   14418739   0.0407039599845414
41.13   45.25   2.97   14964372   0.0406274797887733
43.5   54.88   2.89   15517926   0.0402249821009884
42.88   48.88   2.87   16155255   0.039987
43.75   56.25   3   16691517   0.0398462357373634
44   60.13   2.9   17427609   0.0396872926778427
44.5   56.88   3.09   18120714   0.0390638072400817
43.88   59.75   3.14   18624475   0.03758
45.5   60.25   3.17   19390604   0.03665

Answer 1

Solution:

Here, we have to use regression model for the prediction of the dependent variable Company A based on the independent variables such as 10 Yr. T, GDP and Unemployment.

A regression model by using Minitab is given as below:

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Regression Analysis: Company A versus 10-Yr. T, GDP ($millions), ...

The regression equation is

Company A = 50.3 + 1.70 10-Yr. T +0.000000 GDP ($millions) - 378 Unemployment

Predictor        Coef     SE Coef          T        P

Constant        50.28       10.11       4.97    0.000

10-Yr. T        1.696       1.482       1.14    0.266

GDP ($mi   0.00000019 0.00000025       0.74    0.471

Unemploy       -378.0       207.2      -1.82    0.083

S = 0.8913      R-Sq = 88.9%     R-Sq(adj) = 87.2%

Analysis of Variance

Source            DF          SS          MS         F       P

Regression         3     127.109      42.370     53.33    0.000

Residual Error    20      15.890       0.794

Total             23     142.999

Source       DF      Seq SS

10-Yr. T      1      99.236

GDP ($mi      1      25.230

Unemploy      1       2.644

Unusual Observations

Obs   10-Yr. T    Company         Fit      SE Fit    Residual    St Resid

10       2.34     38.630      40.344       0.331      -1.714       -2.07R

R denotes an observation with a large standardized residual

The P-value for this regression model is given as 0.00 which indicate that the given regression model is statistically significant and we can use this regression model for the prediction of the dependent variable.

The regression equation is given as below:

Company A = 50.3 + 1.70 10-Yr. T +0.000000 GDP ($millions) - 378 Unemployment

The coefficient of determination or the value of R square is given as 0.889 which means about 88.9% of the variation in the dependent variable is explained by the independent variable.