Advanced Multiple Linear Regression Calculator

1. IBM SPSS Statistics

Menu navigation instructions:

1. Go to the main menu: Analyze → Regression → Linear...
2. Move your outcome variable into the Dependent box.
3. Move your predictor variables into the Independent(s) box.
4. (Optional) Click Statistics and check Collinearity diagnostics to review VIF values.
5. Click OK to run the analysis and generate output.

2. JASP

Interface instructions:

1. Click on the Regression module in the top header → select Linear Regression.
2. Drag your dependent variable into the Dependent Variable field.
3. Drag continuous predictors into the Covariates field (and categorical ones into Factors).
4. Expand Statistics and check Collinearity diagnostics if needed.

3. jamovi

Interface instructions:

1. Select Analyses tab → Regression → Linear Regression.
2. Assign your target variable to Dependent Variable.
3. Assign your predictor variables to Covariates.
4. Under Model Assumptions, check Collinearity statistics.

4. R / RStudio

Code example:

# Sample dataset creation
data <- data.frame(
  y = c(12, 15, 18, 20, 25),
  x1 = c(2, 3, 5, 7, 8),
  x2 = c(10, 11, 9, 12, 15)
)
# Fit multiple linear regression model
model <- lm(y ~ x1 + x2, data = data)

# Display regression summary and statistics
summary(model)

5. Python

Code example (using statsmodels for detailed statistical summary):

import pandas as pd
import statsmodels.api as sm

# Sample dataset creation
df = pd.DataFrame({
    'y': [12, 15, 18, 20, 25],
    'x1': [2, 3, 5, 7, 8],
    'x2': [10, 11, 9, 12, 15]
})

# Define predictor matrix and add intercept constant
X = df[['x1', 'x2']]
X = sm.add_constant(X)
y = df['y']

# Fit Ordinary Least Squares (OLS) model and output summary
model = sm.OLS(y, X).fit()
print(model.summary())