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# Análisis de regresión

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.

Para acceder a esta orden…

Vaya a Datos ▸ Estadísticas ▸ Regresión

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

## Datos

### Intervalo de variables independientes (X):

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.

### Intervalo de variable dependiente (Y):

Introduzca el intervalo que contiene la variable dependiente cuya regresión se ha de calcular.

### 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:

La referencia de la celda superior izquierda del intervalo en donde se mostrarán los resultados.

#### Agrupados por

Seleccione si los datos de entrada se organizarán en columnas o en filas.

## Tipos de regresión de salida

Establezca el tipo de regresión. Hay tres tipos disponibles:

- 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.

## Opciones

### Nivel de confianza

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.

Temas relacionados

Sampling

Descriptive Statistics

Analysis of Variance (ANOVA)

Correlación

Covariance

Alisamiento exponencial

Media móvil

Análisis de regresión

Prueba t pareada

Prueba F

Z-test

Prueba de la ji al cuadrado

Análisis de Fourier