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.

Så här använder du det här kommandot...

Från menylisten:

Choose Data - Statistics - Regression

Från gränssnittet med flikar:

Choose Data - Statistics - Regression.

On the Data menu of the Data tab, choose Statistics - Regression.


note

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


Data

Oberoende variabel(er) (X) område:

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.

Underordnad variabel (Y) område:

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

Både X och Y områden har etiketter

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

Resultat till:

Referensen till den övre vänstra cellen i intervallet där resultaten kommer att visas.

Grouped By

Select whether the input data has columns or rows layout.

Regressionstyper

Set the regression type. Three types are available:

Alternativ

Konfidensnivå

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).

Beräkna rester

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.

Tvinga skärning att vara noll

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

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