# Regression Project

Topics: Regression analysis, Linear regression, Errors and residuals in statistics Pages: 18 (4804 words) Published: March 31, 2013

Regression Project
Estimating Stock Prices of Independent E&P Companies

Assignment for Course: HR 533, Applied Managerial Statistics Submitted to:
Professor Mohamed Nayebpour
Submitted by:
Leah A. O’Daniels

Location of Course: Blended – Houston Campus & On-line Date of Submission: December 16, 2011

Regression Analysis: StockPrice versus Sales(B)
The regression equation is
StockPrice = 15.64 + 4.441 Sales(B)
S = 11.2028 R-Sq = 33.6% R-Sq(adj) = 31.8%
Analysis of Variance
Source DF SS MS F P
Regression 1 2353.31 2353.31 18.75 0.000
Error 37 4643.62 125.50
Total 38 6996.93

As x increases y increases therefore there is a positive relationship between x and y. Outlier strong effects the graph of the regression line.

Regression Analysis: StockPrice versus EBITDA(B)
The regression equation is
StockPrice = 13.10 + 16.88 EBITDA(B)
S = 8.79860 R-Sq = 59.1% R-Sq(adj) = 58.0%
Analysis of Variance
Source DF SS MS F P
Regression 1 4132.56 4132.56 53.38 0.000
Error 37 2864.37 77.42
Total 38 6996.93

As x increases y increases, therefore there is a positive relationship between x and y. Outliers affect the graph of the regression line.

Regression Analysis: StockPrice versus Profit Margin
The regression equation is
StockPrice = 17.09 + 12.97 Profit Margin
S = 13.4077 R-Sq = 4.9% R-Sq(adj) = 2.4%
Analysis of Variance
Source DF SS MS F P
Regression 1 345.58 345.578 1.92 0.174
Error 37 6651.35 179.766
Total 38 6996.93

As x increases y increases between -0. 1 and 0.5 which shows no real pattern to the scatter plot. Therefore this is little or no relationship between x and y.

Regression Analysis: StockPrice versus Earning
The regression equation is
StockPrice = 7.166 + 5.977 Earning
S = 7.50264 R-Sq = 70.2% R-Sq(adj) = 69.4%
Analysis of Variance
Source DF SS MS F P
Regression 1 4914.21 4914.21 87.30 0.000
Error 37 2082.72 56.29
Total 38 6996.93

As x increases y increases revealing a pattern which indicates a strong relationship between x and y

Regression Analysis: StockPrice versus Equiv.(MMboe)
The regression equation is
StockPrice = 13.78 + 0.02077 Equiv.(MMboe)
S = 9.73518 R-Sq = 49.9% R-Sq(adj) = 48.5%
Analysis of Variance
Source DF SS MS F P
Regression 1 3490.30 3490.30 36.83 0.000
Error 37 3506.63 94.77
Total 38 6996.93

As x increases y increases therefore this is a positive relationship between x and y. Outliers affect the graph of the regression.

Regression Analysis: StockPrice versus (%)Liquid
The regression equation is
StockPrice = 21.31 - 3.649 (%)Liquid
S = 13.7214 R-Sq = 0.4% R-Sq(adj) = 0.0%
Analysis of Variance
Source DF SS MS F P
Regression 1 30.67 30.668 0.16 0.689
Error 37 6966.26 188.277
Total 38 6996.93

Scatterplot shows no pattern, therefore there is no relationship between x and y.

Regression Analysis: StockPrice versus (%)Domestic
The regression equation is
StockPrice = 42.84 - 26.17 (%)Domestic
S = 12.8618 R-Sq = 12.5% R-Sq(adj) = 10.2%
Analysis of Variance
Source DF SS MS F P
Regression 1 876.16 876.163 5.30 0.027
Error 37 6120.77 165.426
Total 38 6996.93

As x increases y decreases. Negative relationship between x and y

Results for: Regression_Project.MTW

Correlations: StockPrice, Sales(B), EBITDA(B), Profit Margi, Earning, ...

StockPrice Sales(B) EBITDA(B) Profit Margin Sales(B) 0.580
0.000

EBITDA(B) 0.769 0.693...