Regression models frequently asked questions

Regression models frequently asked questions

What types of regression models are available?
  • Linear Regression – For modeling relationships between dependent and independent variables using a straight line.
  • Multiple Linear Regression – Extends linear regression with multiple predictor variables.
  • Polynomial Regression – Fits a nonlinear relationship using polynomial equations.
  • Logistic Regression – Used for binary classification problems (Yes/No outcomes).
  • Stepwise Regression – A technique to select significant variables automatically.
How do I perform a regression analysis?
  • Load Data: Open Sigma Magic and import your dataset (Excel, CSV, or manual entry).
  • Select Regression Tool: Navigate to Statistics > Regression Analysis.
  • Choose Variables: Select the dependent variable (Y) and independent variables (X1, X2, etc.).
  • Configure Model Settings: Choose regression type (Linear, Polynomial, etc.), confidence level, and significance threshold.
  • Run Analysis: Click "Run" to execute the regression.
  • Interpret Results : View regression coefficients, R² value, p-values, and residual plots.
    •
    What is p-value, and how do I use it in Sigma Magic?
    • The p-value indicates whether a predictor variable significantly impacts the dependent variable.
    • Threshold (Significance Level, α): Usually 0.05 (5%).
    • Interpretation:
      • p-value < 0.05 → The variable is statistically significant.
      • p-value > 0.05 → The variable is NOT statistically significant and may be removed.
    How do I handle outliers in regression analysis?
  • Residual Plot Analysis – Check for extreme deviations.
  • Cook’s Distance – Identifies influential points.
  • Box Plot – Helps visualize outliers.
  • Solutions:
    • Transform data (log, square root).
    • Remove extreme outliers if justified.
    • Use robust regression techniques.
    •
    How do I select the best regression model?
  • Adjusted R² – More reliable than R² when adding variables.
  • Akaike Information Criterion (AIC) – Lower values indicate better models.
  • Bayesian Information Criterion (BIC) – Helps avoid overfitting.
  • Cross-validation – Splits data into training and test sets to validate performance.
    •
    What are common errors in regression modeling?

    1. Overfitting – Too many variables make the model fit noise rather than real trends.

    Solution: Use cross-validation, regularization (Ridge/Lasso).

    2. Multicollinearity – Highly correlated predictors lead to unreliable coefficients.

    Solution: Remove correlated variables or use Principal Component Analysis (PCA).

    3. Non-linearity – Linear models may not fit complex relationships.

    Solution: Use Polynomial or Decision Tree Regression.
    How do I use Sigma Magic for time-series regression?
    • Trend Analysis – Identifies patterns over time.
    • Seasonality Detection – Accounts for periodic fluctuations.
    • ARIMA Modeling – Used for forecasting future trends.
    ​
     Reference: Some of the text in this article has been generated using AI tools such as ChatGPT and edited for content and accuracy.

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