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Amaf

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Submitted By arny617
Words 1203
Pages 5
Assignment on Regression
Objective of the Analysis
To find a regression equation to forecast the closing price of sensex by estimating its relationship with various other independent parameters
Description of Data
Data Frequency: Monthly Data
Data Span: From year 2010 to 2014
Following independent variables are used to estimate the GDP:
1: Call Money Rate: This variable
2: Dollar Price:
3: Oil Price:
4: Gold Price:
5: IIP:

Empirical Analysis
Following steps were performed to find out the relationship between Sensex Closing Price and other independent variables:
1: Regression command entered in E-Views: “LS SensexClosePrice C CallMoeyRate DollarPrice OilPrice GoldPrice IIP”

Dependent Variable: GDP | | | Method: Least Squares | | | Date: 08/01/14 Time: 23:09 | | | Sample: 1 41 | | | | Included observations: 41 | | | | | | | | | | | | | Variable | Coefficient | Std. Error | t-Statistic | Prob. | | | | | | | | | | | C | 2027.956 | 2505.872 | 0.809281 | 0.4238 | POWER_C | 0.015238 | 0.009099 | 1.674679 | 0.1029 | EXPORTS | -0.026773 | 0.022733 | -1.177689 | 0.2469 | IMPORTS | -0.001196 | 0.009135 | -0.130908 | 0.8966 | HIGHWAY | 0.006676 | 0.002728 | 2.446933 | 0.0196 | TAX | 0.072515 | 0.013733 | 5.280252 | 0.0000 | | | | | | | | | | | R-squared | 0.995336 | Mean dependent var | 34045.72 | Adjusted R-squared | 0.994670 | S.D. dependent var | 23515.70 | S.E. of regression | 1716.835 | Akaike info criterion | 17.86881 | Sum squared resid | 1.03E+08 | Schwarz criterion | 18.11958 | Log likelihood | -360.3106 | F-statistic | 1493.890 | Durbin-Watson stat | 1.119398 | Prob(F-statistic) | 0.000000 | | | | | | | | | | |

As the Durbin Watson statistic is sufficiently less than 2 there is chance of Auto Correlation so first

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