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# Analisa Regresi

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.

Untuk mengakses fungsi ini...

#### 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.

Untuk informasi lebih lanjut tentang analisis regresi, lihat tautan artikel Wikipedia yang sesuai.

## Data

### Rentang (X) variabel independen:

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.

### Rentang variabel (Y) dependen:

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

### Rentang X dan Y memiliki label

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

### Hasil ke:

Rujukan sel kiri atas dari rentang di mana hasil akan ditayangkan.

#### Dikelompokkan menurut

Pilih apakah data input memiliki tata letak kolom atau baris.

## Jenis Regresi Keluaran

Setel jenis regresi. Tiga jenis tersedia:

- 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.

## Pilihan

### Tingkat kepercayaan

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).

### Hitung residu

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.

### Paksa memintas menjadi nol

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

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