# 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.

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Choose Data - Statistics - Regression

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

### Független változók (X) tartománya:

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.

### Függő változók (Y) tartománya:

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.

### Eredmények:

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

#### Csoportosítva

Válassza ki, hogy a bemenő adatok oszlopok vagy sorok szerint vannak-e rendezve.

## Kimeneti regressziótípusok

Állítsa be a regresszió típusát. Három típus érhető el:

• Linear Regression: finds a linear function in the form of y = b + a1.[x1] + a2.[x2] + a3.[x3] ..., where ai is the i-th slope, [xi] is the i-th independent variable, and b is the intercept that best fits the data.

• Logarithmic regression: finds a logarithmic curve in the form of y = b + a1.ln[x1] + a2.ln[x2] + a3.ln[x3] ..., where ai is the i-th coefficient, b is the intercept and ln[xi] is the natural logarithm of the i-th independent variable, that best fits the data.

• Power regression: finds a power curve in the form of y = exp( b + a1.ln[x1] + a2.ln[x2] + a3.ln[x3] ...), where ai is the i-th power, [xi] is the i-th independent variable, and b is intercept that best fits the data.

## Beállítások

### Megbízhatósági szint

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).