Difference between revisions of "Manuals/calci/REGRESSION"
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+ | =REGRESSIONANALYSIS(A2:A5,B2:B5) | ||
− | + | {| class="wikitable" | |
− | + | |- | |
− | + | ! v11031 !! DSIN | |
− | + | |- | |
− | + | | Multiple R || 0.01745240643728351 | |
− | + | |- | |
+ | | R Square || 0.03489949670250097 | ||
+ | |- | ||
+ | | v14193 || 0.05233595624294383 | ||
+ | |- | ||
+ | | v15308 || 0.0697564737441253 | ||
+ | |- | ||
+ | | 5 || 0.08715574274765817 | ||
+ | |- | ||
+ | | 6 || 0.10452846326765346 | ||
+ | |- | ||
+ | | 7 || 0.12186934340514748 | ||
+ | |- | ||
+ | | 8 || 0.13917310096006544 | ||
+ | |- | ||
+ | | 9 || 0.15643446504023087 | ||
+ | |- | ||
+ | | 10 || 0.17364817766693033 | ||
+ | |} | ||
Revision as of 01:17, 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 | |
---|---|---|
1 | Temperature | Drying Time(Hrs) |
2 | 54 | 8 |
3 | 63 | 6 |
4 | 75 | 3 |
5 | 82 | 1 |
=REGRESSIONANALYSIS(A2:A5,B2:B5)
v11031 | DSIN |
---|---|
Multiple R | 0.01745240643728351 |
R Square | 0.03489949670250097 |
v14193 | 0.05233595624294383 |
v15308 | 0.0697564737441253 |
5 | 0.08715574274765817 |
6 | 0.10452846326765346 |
7 | 0.12186934340514748 |
8 | 0.13917310096006544 |
9 | 0.15643446504023087 |
10 | 0.17364817766693033 |
Unit sales - Ads - population
4000 - 12000 - 300000
5200 - 13150 - 411000
6800 - 14090 - 500000
8000 - 11900 - 650000
10000 - 15000 - 800000
REGRESSIONANALYSIS(B1:B5,C1:D5)=