Difference between revisions of "Manuals/calci/REGRESSION"

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=REGRESSIONANALYSIS(A1:A4,B1:B4)=
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REGRESSIONANALYSIS(A1:A4,B1:B4)=
 
Unit sales  -  Ads  -    population
 
Unit sales  -  Ads  -    population
 
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4000  -      12000 -    300000
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10000  -      15000 -    800000
=REGRESSIONANALYSIS(B1:B5,C1:D5)=
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REGRESSIONANALYSIS(B1:B5,C1:D5)=
  
  

Revision as of 10:48, 20 January 2014

REGRESSIONANALYSIS(y,x)


  • is the set of dependent variables .
  • is the set of independent variables.


Description

  • This function is calculating the Regression analysis of the given data.
  • This analysis is very useful for the analyzation of large amounts of data and making predictions.
  • This analysis give the result in three table values.
  1. Regression statistics table.
  2. ANOVA table.
  3. Residual output.
  • 1.Regression statistics :
  • It contains multiple R, R Square, Adjusted R Square, Standard Error and observations.
  • R square gives the fittness of the data with the regression line.
  • That value is closer to 1 is the better the regression line fits the data.
  • Standard Error refers to the estimated standard deviation of the error term. It is called the standard error of the regression.
  • 2.ANOVA table:
  • ANOVA is the analysis of variance.
  • This table splits in to two components which is Residual and Regression.
  • Total sum of squares= Residual (error) sum of squares+ Regression (explained) sum of squares.
  • Also this table gives the probability, T stat, significance of F and P.
  • When the significance of F is < 0.05, then the result for the given data is statistically significant.
  • When the significance of F is > 0.05, then better to stop using this set of independent variables.
  • Then remove a variable with a high P-value and returnun the regression until Significance F drops below 0.05.
  • So the Significance of P value should be <0.05.
  • This table containing the regression coefficient values also.
  • 3.Residual output:
  • The residuals show you how far away the actual data points are fom the predicted data points.


Examples

  1. Temperature - Drying time(hours)

54 - 8 63 - 6 75 - 3 82 - 1 REGRESSIONANALYSIS(A1:A4,B1:B4)= Unit sales - Ads - population 4000 - 12000 - 300000 5200 - 13150 - 411000 6800 - 14090 - 500000 8000 - 11900 - 650000 10000 - 15000 - 800000 REGRESSIONANALYSIS(B1:B5,C1:D5)=


See Also

References