# Regression Analysis

Produces the regression analysis of a data set

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Choose Data - Statistics - Regression

For more information on regression analysis, refer to the corresponding Wikipedia article.

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Input Range: The reference of the range of the data to analyze.

Results to: The reference of the top left cell of the range where the results will be displayed.

### Grouped By

Select whether the input data has columns or rows layout.

## Output Regression Type

Set the regression type. Three types are available:

• Linear Regression: find a straight line in the form of y = a.x + b, where a is the slope and b is the intercept that best fits the data.

• Logarithmic regression: find a logarithmic curve in the form of y = a.ln(x) + b, where a is the slope, b is the intercept and ln(x) is the natural logarithm of x, that best fits the data.

• Power regression: Find a power curve in the form of y = a.x^b, where a is the coefficient, b is the power that best fits the data.

#### Example

The following table has samples of a physical phenomenon taken in 1 second interval.

 A B 1 Time Measurement 2 1 2.7 3 2 4.0 4 3 4.4 5 4 7.1 6 5 4.9 7 6 3.6 8 7 4.0 9 8 0.6 10 9 1.0 11 10 4.3

The results of the three types of regression analysis of the measurements in the table above are shown below.

 Regression Regression Model แฌแ แคแแแ Logarithmic แซแแแ R^2 0.1243901235 0.036283506 0.0884254697 Standard Error 1.8692568609 1.9610483597 0.7746321053 Slope -0.2193939394 -0.4894112008 4.812672931 Intercept 4.8666666667 4.3992268695 -0.3103085297 1 4.6472727273 4.3992268695 4.812672931 2 4.4278787879 4.0599928755 3.8812728356 3 4.2084848485 3.8615537101 3.4224061924 4 3.9890909091 3.7207588815 3.1301272785 5 3.7696969697 3.6115499281 2.9207204651 6 3.5503030303 3.5223197161 2.7600654308 7 3.3309090909 3.4468766468 2.6311476385 8 3.1115151515 3.3815248876 2.5243514679 9 2.8921212121 3.3238805506 2.4337544465 10 2.6727272727 3.2723159341 2.3554713075