Difference between revisions of "Manuals/calci/REGRESSION"
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==Examples== | ==Examples== | ||
+ | {| class="wikitable" | ||
+ | |+Spreadsheet | ||
+ | |- | ||
+ | ! !! A !! B !! C !! D!! E | ||
+ | |- | ||
+ | ! 1 | ||
+ | | Temperature || Drying Time(Hrs) | ||
+ | |- | ||
+ | ! 2 | ||
+ | | 54 || 8 | ||
+ | |- | ||
+ | ! 3 | ||
+ | | 63 || 6 | ||
+ | |- | ||
+ | ! 4 | ||
+ | | 75 || 3 | ||
+ | |- | ||
+ | ! 5 | ||
+ | | 82 || 1 | ||
+ | |} | ||
+ | |||
+ | |||
#Temperature - Drying time(hours) | #Temperature - Drying time(hours) | ||
54 - 8 | 54 - 8 | ||
Line 44: | Line 66: | ||
10000 - 15000 - 800000 | 10000 - 15000 - 800000 | ||
REGRESSIONANALYSIS(B1:B5,C1:D5)= | REGRESSIONANALYSIS(B1:B5,C1:D5)= | ||
− | |||
==See Also== | ==See Also== |
Revision as of 00:52, 23 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.
- Regression statistics table.
- ANOVA table.
- 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
A | B | C | D | E | |
---|---|---|---|---|---|
1 | Temperature | Drying Time(Hrs) | |||
2 | 54 | 8 | |||
3 | 63 | 6 | |||
4 | 75 | 3 | |||
5 | 82 | 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)=