Manuals/calci/REGRESSION

REGRESSIONANALYSIS(y,x)

• $y$ is the set of dependent variables .
• $x$ 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.

Spreadsheet

	A	B
1	Temperature	Drying Time(Hrs)
2	54	8
3	63	6
4	75	3
5	82	1

=REGRESSIONANALYSIS(A2:A5,B2:B5)

Summary Output

Regression	Statistics
Multiple R	-0.9989241524588298
R Square	0.9978494623655915
v14193	0.9967741935483871
v15308	0.7071067811865362

ANOVA

Source of Variation	Sum Of Squares	Degree Of Freedom	Mean Of Squares	F	Significance F
Regression:	464	1	464	928	0.0010758475411702228
Residual:	1	2	0.5
Total:	465	3

ANOVA

	Coefficients	Standard Error	T Statistics	Probability	Lower 95%	Upper 95%
Intercept:	86.5	0.6885767430246738	125.62143708199632	0.00006336233990811291	83.53729339698289	89.46270660301711
X Variable	-4	0.13130643285972046	-30.463092423456118	0.0010758475411701829	-4.564965981777541	-3.435034018222459

Residual Output

Observation	Predicted Y	Residuals	Standard Residuals
1	54.5	-0.5	-0.8660254037844387
2	62.5	0.5	0.8660254037844387
3	74.5	0.5	0.8660254037844387
4	82.5	-0.5	-0.8660254037844387

Unit sales - Ads - population 4000 - 12000 - 300000 5200 - 13150 - 411000 6800 - 14090 - 500000 8000 - 11900 - 650000 10000 - 15000 - 800000 REGRESSIONANALYSIS(B1:B5,C1:D5)=