What is Regression Analysis

Guide: Regression Analysis

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Daniel Croft

Daniel Croft is an experienced continuous improvement manager with a Lean Six Sigma Black Belt and a Bachelor's degree in Business Management. With more than ten years of experience applying his skills across various industries, Daniel specializes in optimizing processes and improving efficiency. His approach combines practical experience with a deep understanding of business fundamentals to drive meaningful change.

Welcome to this beginner-friendly guide on Regression Analysis in the context of Continuous Improvement! If you’re looking to make data-driven decisions to enhance your business processes, you’re in the right place. This guide will introduce you to two fundamental types of regression: Simple Linear Regression Multiple Regression. Don’t worry if these terms sound complex; we’ll break them down into bite-sized pieces for easy understanding.

Due to the length of the topic you can find the guide on Multiple Regression Analysis in its own guide.

In Simple Linear Regression, we’ll explore how one factor, like the speed of a production line, can predict another, such as the quality of products. In Multiple Regression, we’ll look at how multiple factors together can predict an outcome—like how both traffic conditions and distance can affect delivery times.

We’ll also walk you through real-world industry applications, demonstrating how these techniques can provide valuable insights into areas like manufacturing, logistics, and the public sector. So, let’s dive in and unravel the power of regression analysis in driving continuous improvement in your operations!

Table of Contents

Pre-requisites of Regression Analysis

Before diving into the nitty-gritty details of Regression Analysis in Continuous Improvement, it’s essential to have some foundational knowledge. This will not only make your learning journey smoother but also help you grasp complex concepts more easily. Here’s what you’ll need:

Basic Understanding of Statistics

Statistics is the backbone of any data-driven approach like regression analysis. It provides the tools to collect, analyze, interpret, present, and organize data.

  • Descriptive Statistics: Understand basics like mean, median, and mode, as well as how to read graphs and charts.
  • Inferential Statistics: Basic concepts like hypothesis testing, p-values, and confidence intervals.

Familiarity with Continuous Improvement Concepts

Introduction to the Project Management Triangle

The Project Management Triangle, often referred to as the Triple Constraint, Iron Triangle, or Project Triangle, is a model that demonstrates the constraints of project management. It highlights the balance between three primary forces: Time, Cost, and Scope. This guide will delve into each of these aspects, providing insight into how they interact with one another and influence the overall success of a project.

Understanding the Three Constraints

Time: The Chronological Constraint

Time in project management is a critical constraint that refers to the schedule or timeline for completing the project. It involves setting realistic deadlines for the completion of the project and its individual tasks. Time management in a project requires meticulous planning, scheduling, and monitoring to ensure that the project stays on track. Delays in the timeline can lead to cost overruns and impact the project’s overall success.

Cost: The Financial Constraint

Cost is another key element of the Project Management Triangle. It represents the budgetary limitations of the project, encompassing all financial resources required for its execution. This includes labor costs, material expenses, equipment, and any other costs associated with the project. Effective cost management entails accurate budget forecasting, cost tracking, and controlling expenditures to ensure the project is completed within the allocated budget.

Scope: The Qualitative Constraint

Scope refers to the specific goals, deliverables, features, and functions that the project is expected to deliver. It defines what is and is not included in the project. Managing the scope involves clear communication of project objectives, stakeholder expectations, and a thorough understanding of the project requirements. Scope changes, often known as scope creep, can significantly affect the other two constraints of time and cost.

Understanding the principles of Continuous improvement will help you know where and how to apply regression analysis effectively.

  • PDCA Cycle (Plan-Do-Check-Act): The fundamental framework for implementing continuous improvement.
  • Lean and Six Sigma: Basic knowledge of these methodologies can be useful, as they often use regression analysis.

Software Tools for Regression Analysis

While it’s possible to do regression analysis by hand, software tools make the process faster, more accurate, and easier to interpret.

  • Excel: Great for beginners and small datasets. Excel has built-in functionalities for regression analysis.
  • R: An open-source tool that’s powerful but has a steep learning curve. Ideal for more complex analyses.
  • Minitab: Often used in Six Sigma projects, it’s user-friendly and offers various statistical features.

Resources for Learning

  • Tutorials: Numerous online tutorials teach how to perform regression analysis in Excel, R, or Minitab.
  • User Guides: Software often comes with extensive documentation and user guides.

Simple Linear Regression

In this section, we’ll delve into the world of Simple Linear Regression. We’ll start by demystifying what it actually means and then move on to its components and assumptions. This is a foundational block, so let’s make sure we get it right!

What is Simple Linear Regression?

Simple Linear Regression is a statistical method that allows us to study the relationship between two continuous variables. Simply put, it helps you predict the value of one variable (the “dependent” variable) based on the value of another variable (the “independent” variable).

For example, you might want to know how the speed of a production line (independent variable) affects the number of defective products (dependent variable).

  • Dependent Variable: This is what you’re trying to predict or understand. In our example, it’s the number of defective products.
  • Independent Variable: This is the variable you think is influencing the dependent variable. In our example, it’s the speed of the production line.


Understanding the assumptions in Simple Linear Regression is crucial because if these assumptions are not met, the results may not be reliable. Here are the key assumptions:


  • What It Means: The relationship between the independent and dependent variable should be linear. This means that a change in the independent variable will result in a proportional change in the dependent variable.

  • How to Check: Plot the data points on a scatter plot. If they roughly form a straight line, this assumption is likely met.

regression analysis - checking for linearity


  • What It Means: Each data point should be independent of the others. This means that the value of one observation should not influence or be influenced by another observation.

  • How to Check: If you collected your data randomly and your data points are not part of a time series, this assumption is usually met.


  • What It Means: The variability of the dependent variable should be the same across all levels of the independent variable.

  • How to Check: Look at the scatter plot. If the spread of the data points is roughly the same across all levels of the independent variable, this assumption is likely met.

Regression Analysis - Checking for Homoscedasticity


  • What It Means: The residuals (differences between observed and predicted values) should be normally distributed.

  • How to Check: You can use a Q-Q plot or statistical tests like the Shapiro-Wilk test to check for normality.

Regression Analysis - Checking for normal residuals Q-Q Plot

Now that we’ve covered the basics and assumptions of Simple Linear Regression, you’re well-equipped to understand how it works and when to use it in your continuous improvement projects.

Steps to Perform Simple Linear Regression

Data Collection

  • What It Means: The first step is to gather data for both the dependent and independent variables.
  • Best Practices: Make sure the data is accurate, relevant, and collected over a sufficient period to make it representative.

Example data table

Distance (miles)Traffic ConditionsDelivery Time (minutes)

Data Visualization

  • What It Means: Before jumping into calculations, visualize the data to get a sense of the relationship between the variables.
  • Best Practices: Use scatter plots to plot the dependent variable against the independent variable. Look for patterns, outliers, or any other insights.

Regression Analysis - Data Visualization

Model Fitting

  • What It Means: This step involves using statistical methods to fit a line that best represents the relationship between the variables.
  • Best Practices: Use software tools like Excel, R, or Minitab for more accurate results. The formula for the line is often written as is the coefficient.

Regression Analysis - Model Fitting


  • What It Means: Once the model is fitted, the next step is to interpret what the numbers mean for your specific context.
  • Best Practices: Look at the coefficient, intercept, and various statistical measures like R-squared to understand the quality and implications of your model.

Interpretation of Results

Coefficient and Intercept

  • Coefficient (b): This number tells you how much the dependent variable is expected to increase (or decrease) for a one-unit increase in the independent variable.
  • Intercept (a): This is the expected value of the dependent variable when the independent variable is zero. In many contexts, the intercept may not have a meaningful interpretation.

R-squared Value

  • What It Means: The R-squared value, also known as the coefficient of determination, tells you how well your model explains the variability in the dependent variable.
  • Interpretation: An R-squared value closer to 1 indicates a better fit. However, a high R-squared doesn’t mean the model is perfect or that it will predict future outcomes accurately.

Regression Analysis - R Squared Value

In the updated plot above, the red dashed line still represents the line of best fit. This time, we’ve included the R-squared value, which is approximately 0.83

What It Means:

  • R-squared Value: The R-squared value of 0.83 tells us that approximately 83% of the variability in delivery time can be explained by the distance. This is generally considered a good fit, as the value is closer to 1.


  • Closer to 1: An R-squared value closer to 1 indicates a better fit between your model and the observed data.

  • Not Perfect: While a high R-squared value suggests a good fit, it doesn’t mean the model is perfect or that it will predict future outcomes accurately. It’s essential to consider other metrics and validations.

Significance Testing

  • What It Means: This involves testing whether the relationships that the model has identified are statistically significant or just due to random chance.
  • Best Practices: Look at p-values associated with the coefficients. A p-value less than 0.05 is generally considered statistically significant.

With these steps and interpretations, you’ll be well on your way to employing Simple Linear Regression in your continuous improvement projects. Armed with this understanding, you can make data-driven decisions that are both impactful and statistically sound.

After covering the steps and interpretations of Simple Linear Regression, it’s time to dive into a real-world industry example and discuss some important limitations and caveats. This will help you better understand how to apply this statistical tool in practice.

Industry Example: Simple Linear Regression in Manufacturing

Predicting Defect Rates Based on Speed of Conveyor Belt

In a manufacturing setting, one of the common goals is to minimize defects while maximizing output. Let’s explore how Simple Linear Regression can be used to predict defect rates based on the speed of a conveyor belt.

Scenario: A manufacturing plant wants to find out if increasing the speed of the conveyor belt affects the defect rate of a product. The plant gathers data over several weeks, varying the speed and recording the corresponding defect rates.

Data Collection:

  • Independent Variable: Speed of the conveyor belt (in meters per minute)
  • Dependent Variable: Defect rate (percentage of defective products)
Shortened data table:
Speed (m/min)Defect Rate (%)

Data Visualization:

  • A scatter plot is created, which shows a general upward trend: as the speed of the conveyor belt increases, so does the defect rate.
regresssion example - data visualisation

Model Fitting:

  • Using a software tool like Minitab, the line of best fit is calculated. Assume the equation is
    Defect Rate=0.5+0.2×Speed

regresssion example - data visualisation with line of best fit


  • Coefficient (0.2): For each meter per minute increase in speed, the defect rate increases by 0.2%.
  • Intercept (0.5): The defect rate when the conveyor belt is not moving is 0.5%, which may not be practically meaningful in this context.

regresssion example - Interpreation Residual Plot

Decision Making: Based on the model, the plant can make informed decisions about the optimal speed of the conveyor belt to balance productivity and quality.

Decision Making Risk Reward Plot

The Risk-Reward Decision based on the Regression Analysis:

Model Equation:

The equation for the line of best fit was

Defect Rate=0.5+0.2×Speed


  • Coefficient (0.2): For each meter per minute increase in speed, the defect rate increases by 0.2%.
  • Intercept (0.5): The defect rate when the conveyor belt is not moving is 0.5%, which may not be practically meaningful in this context.

Risk-Reward Analysis:

  1. Lower Speeds (10-30 m/min):
    • Risk: Lower production output.
    • Reward: Lower defect rates (3-6%).
  2. Medium Speeds (30-50 m/min):
    • Risk: Moderate production output and moderate defect rates (6-9%).
    • Reward: Balanced between production and quality.
  3. Higher Speeds (50-60 m/min):
    • Risk: Higher defect rates (9-11%).
    • Reward: Higher production output.


  1. Quality Focus: If minimizing defects is a priority, it’s advisable to operate at lower speeds (10-30 m/min).
  2. Balanced Approach: For a balance between production and quality, operate at medium speeds (30-50 m/min).
  3. Production Focus: If the focus is on maximizing output, be prepared for higher defect rates when operating at speeds between 50-60 m/min.

Given that the increase in the defect rate is linear and relatively moderate (0.2% increase for each m/min increase in speed), the plant could consider a speed range that balances both output and defect rates, perhaps in the 30-50 m/min range.


we delved into the fundamentals of Simple Linear Regression, covering its definition, assumptions, steps, and interpretation. Through an industry example in manufacturing, we showcased how to predict defect rates based on the speed of a conveyor belt. The analysis revealed a clear, linear relationship between speed and defect rate, encapsulated in the equationDefect Rate=0.5+0.2×Speed. This formula serves as a powerful tool for decision-making, allowing the plant to optimize speed settings in line with quality objectives. By understanding the trade-offs between speed and defect rate, organizations can make informed, data-driven decisions to balance productivity and quality.


A: The primary purpose is to understand the relationship between two variables: an independent variable (like speed of a conveyor belt) and a dependent variable (like defect rates). This helps in predicting future outcomes and making data-driven decisions to improve processes.

A: Simple Linear Regression assumes a linear relationship between variables. If the relationship is not linear, the model may not provide accurate predictions. In such cases, you might want to explore non-linear regression models or data transformations.

A: The key assumptions include Linearity, Independence, Homoscedasticity, and Normality. Violations of these assumptions can lead to biased or misleading results. It’s essential to visualize the data and conduct diagnostic tests to verify these assumptions.

A: A low R-squared value indicates that the model doesn’t explain much of the variability in the dependent variable. In such cases, consider revisiting the assumptions, adding more relevant variables, or using a more complex model like Multiple Regression.


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Daniel Croft

Daniel Croft is a seasoned continuous improvement manager with a Black Belt in Lean Six Sigma. With over 10 years of real-world application experience across diverse sectors, Daniel has a passion for optimizing processes and fostering a culture of efficiency. He's not just a practitioner but also an avid learner, constantly seeking to expand his knowledge. Outside of his professional life, Daniel has a keen Investing, statistics and knowledge-sharing, which led him to create the website learnleansigma.com, a platform dedicated to Lean Six Sigma and process improvement insights.

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