Regresszióanalízis

Tartalom

# 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.

Ennek a parancsnak az eléréséhez...

#### From the menu bar:

Choose Data - Statistics - Regression

#### From the tabbed interface:

Choose Data - Statistics - Regression.

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

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

## Adatok

### 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.

### Az X és Y tartományok is tartalmaznak címkéket

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

### Eredmények:

Hivatkozás az eredmények megjelenítésére használni kívánt tartomány bal felső cellájára.

#### 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).

### Maradványértékek számítása

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.

### A metszéspont mindenképp zérus legyen

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

Kapcsolódó témakörök

Mintavétel

Descriptive Statistics

Analysis of Variance (ANOVA)

Korreláció

Kovariancia

Exponential Smoothing

Moving Average

Regression Analysis

Paired t-test

F-próba

Z-próba

Chi-square test

Fourier Analysis