Regression Analysis

Performs linear, logarithmic, or power regression analysis of a data set comprising one dependent variable and multiple independent variables.

For example, a crop yield (dependent variable) may be related to rainfall, temperature conditions, sunshine, humidity, soil quality and more, all of them independent variables.

Aby wykonać tę operację...

Choose Data - Statistics - Regression


Ikona notatki

Więcej informacji na temat analizy regresji: powiązany artykuł z Wikipedii.


Data

Independent variable(s) (X) range:

Enter a single range that contains multiple independent variable observations (along columns or rows). All X variable observations need to be entered adjacent to each other in the same table.

Dependent variable (Y) range:

Enter the range that contains the dependent variable whose regression is to be calculated.

Both X and Y ranges have labels

Check to use the first line (or column) of the data sets as variable names in the output range.

Results to:

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

Grupowanie według

Wybierz, czy dane wejściowe mają układ kolumnowy lub wierszowy.

Output Regression Types

Ustal typ regresji. Dostępne są trzy rodzaje:

Options

Confidence level

A numeric value between 0 and 1 (exclusive), default is 0.95. Calc uses this percentage to compute the corresponding confidence intervals for each of the estimates (namely the slopes and intercept).

Calculate residuals

Select whether to opt in or out of computing the residuals, which may be beneficial in cases where you are interested only in the slopes and intercept estimates and their statistics. The residuals give information on how far the actual data points deviate from the predicted data points, based on the regression model.

Force intercept to be zero

Calculates the regression model using zero as the intercept, thus forcing the model to pass through the origin.

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