Regression Analysis Training - Morocco

Regression Analysis Training Course Outline

Module 1: Introduction

• What is Regression Analysis?
• Publicly Available Data Sets
• Selected Application of Regression Analysis
• Step in Regression Analysis

Module 2: Simple Linear Regression

• Introduction
• Covariance and Correlation Coefficient
• Simple Linear Regression Model
• Parameter Estimation
• Test of Hypotheses
• Confidence Intervals
• Predictions
• Measuring the Quality of Fit
• Regression Line Through the Origin
• Trivial Regression Models

Module 3: Multiple Linear Regression

• Introduction
• Description of the Data and Model
• Parameter Estimation
• Interpretations of Regression Coefficients
• Centring and Scaling
• Properties of the Least Squares Estimators
• Multiple Correlation Coefficient
• Inference for Individual Regression Coefficient
• Test of Hypotheses in a Linear Model
• Predictions

Module 4: Regression Diagnostics: Detection of Model Violations

• Standard Regression Assumptions
• Various Types of Residuals
• Graphical Methods
• Graphs Before Fitting a Model
• Graphs After Fitting a Model
• Checking Linearity and Normality Assumptions
• Leverage, Influence, and Outliers
• Measures of Influence
• Potential-Residual Plot
• What to do with the Outliers?
• Role of Variables in a Regression Equation
• Effects of an Additional Predictor

Module 5: Qualitative Variables as Predictors

• Introduction
• Salary Survey Data
• Interaction Variables
• System of Regression Equations
• Other Application of Indicator Variables
• Seasonality

Module 6: Transformation of Variables

• Introduction
• Transformations to Achieve Linearity
• Bacteria Deaths Due to X-Ray Radiation
• Transformations to Stabilise Variance
• Detection of Heteroscedastic Errors
• Removal of Heteroscedasticity
• Weighted Least Squares
• Logarithmic Transformation of Data
• Power Transformation

Module 7: Problem of Correlated Errors

• Introduction: Autocorrelation
• Consumer Expenditure and Money Stock
• Durbin-Watson Statistic
• Removal of Autocorrelation by Transform
• Iterative Estimation with Autocorrelated Errors
• Autocorrelation and Missing Variables
• Analysis of Housing Starts
• Limitations of the Durbin-Watson Statistic
• Indicator Variables to Remove Seasonal

Module 8: Working with Collinear Data

• Principal Components
• Computations Using Principal Components
• Imposing Constraints
• Searching for Linear Functions
• Biased Estimation of Regression Coefficients
• Principal Components Regression
• Reduction of Collinearity in the Estimation Data
• Constraints on the Regression Coefficients
• Principal Components Regression: A Caution
• Ridge Regression
• Estimation by the Ridge Method
• Ridge Regression: Some Remarks

Who should attend this Regression Analysis Training Course?

The Regression Analysis Training Course aims to provide professionals with a comprehensive understanding of regression techniques and their application in various domains. This course can help various professionals, including:

• Data Scientists
• Data Analysts
• Statisticians
• Market Researchers
• Economists
• Social Scientists
• Financial Analysts
• Research Scholars

Prerequisites of the Regression Analysis Training Course

There are no formal prerequisites required for the Regression Analysis Training Course.

Regression Analysis Training Course Overview

Regression Analysis is a pivotal statistical method that enables examining the relationship between a dependent variable and one or more independent variables. This technique is vital across various fields for predicting outcomes, assessing trends, and making informed decisions. Its relevance spans economics, engineering, biological, and social sciences, proving essential for data-driven insights.

Proficiency in this Regression Analysis Training Course is indispensable for professionals who rely on data to guide decision-making processes. Data Scientists, Economists, Engineers, and Research Analysts must extract insights, predict trends, and optimise operations. Mastering this skill enables professionals to validate theories, influence policy decisions, and enhance business outcomes through robust analytical evidence.

This intensive 1-day Regression Analysis Training Course by The Knowledge Academy is designed to equip delegates with the foundational knowledge and practical skills in regression analysis. Delegates will learn to interpret and apply regression models to real-world data accurately, enhancing their analytical capabilities. The hands-on approach ensures that delegates can confidently utilise regression analysis to support evidence-based decision-making in their respective fields by the end of the session.

Course Objectives

• To introduce the fundamentals of regression analysis and its applications
• To demonstrate how to formulate and test hypotheses using regression models
• To provide insight into selecting appropriate regression techniques for different types of data
• To enhance the ability to interpret the results of regression analysis critically
• To teach the application of regression analysis in predicting outcomes and trends

After completing this Regression Analysis Training, delegates will receive a certification in this training, affirming their competence in understanding and applying regression models. This certification enhances their professional profile and opens up opportunities for advanced roles in data analysis, research, and policymaking.

What’s included in this Regression Analysis Training Course?

• World-Class Training Sessions from Experienced Instructors
• Regression Analysis Certificate
• Digital Delegate Pack