Revitalize Your Strategic Planning with Regression Analysis

Trying to understand how different factors impact your business operations in ways you had never thought of before? As a business owner, you need to know how each element facilitates or hinders your production. Regression analysis is a powerful tool to identify the relationship between two variables and reveal the causation effects so you can focus resources on money-making projects.

Here at Business2Community, we’re want to help you master regression analysis so you can transform your strategic planning process. You’ll find everything you need to know about this statistical method, including a step-by-step guide to real-life examples, you will gain a deeper understanding of this technique with ease.

What is a Regression Analysis?

Regression analysis is a statistical method that highlights the relationship between two variables. By building a simple linear regression model or a multiple linear regression model, you can better understand how your variables interact with each other.

The two types of variables in regression analysis are dependent and independent variables. Dependent variables are the outcome variables and the independent variables are factors that affect the outcomes.

Regression analysis deduces the magnitude of the relationship between independent and dependent variables. A regression line is a straight line that demonstrates how an independent variable impacts a dependent variable. It gives the best estimate of the value of one variable as the other variable changes so you can adjust production levels, allocate resources, and formulate effective business strategies.

Using regression analysis can systematically organize your data sets so they are easily readable by your team or stakeholders. It allows you as a decision-maker to utilize the data to forecast changes in advance and prepare accordingly.

This technique is similar to correlation analysis as they are both statistical methods used to analyze the interactions between two variables. However, they differ in several key aspects. Only regression analysis can demonstrate the causation effects between dependent and independent variables and make predictions about the values.

Who Needs to Do a Regression Analysis?

Conducting regression analysis is vital in the data analysis process for companies that are expanding or need to find a way to boost revenue or productivity. By locating highly correlated variables, firms can have a clearer idea of the impact of different business decisions, the market risk premium, and other internal and external factors that play a significant role in affecting profits.

Whether you’re a business owner, marketing expert, or data science student, mastering this statistical method is one of the first things you need to do to be successful in the field.

For example, multiple regression analysis may show factors like payment options, delivery speed, and price affect sales volume more than factors like online customer service quality for an ecommerce company. With this information, the company can centralize resources on the several independent variables that can generate the greatest improvement.

Regression models put factors under the microscope, revealing hidden patterns and causal relationships you may not have been aware of. By examining these data points, you can accurately predict future outcomes and produce effective strategies.

How to Perform a Regression Analysis

Analyzing the relationship between variables can sound intimidating for beginners. Don’t worry, performing a regression analysis is often much simpler than you’d think. Once you’ve familiarized yourself with the regression equation, the calculations become straightforward.

Here is the regression equation:

The error term explains the deviation from reality due to an imperfect data collection method. Statistical methods that involve making inferences will never perfectly fit real-world data. Therefore, an error term is essential to consider this discrepancy.

Most data analysts choose to work with statistical software programs to minimize calculation mistakes and speed up the process. Depending on your budget and needs, you can choose a statistical program that fits your goals.

To conduct a regression analysis, follow these steps.

Step 1: Choose the Dependent Variable and Independent Variable to Study

Decide which dependent variable and independent variables you’re interested in. Dependent variables are sometimes known as response variables and independent variables are known as explanatory variables or predictor variables. This could be your staff’s working hours and factory outputs or marketing spend and incoming leads, for example.

Depending on your scope of study, you can choose to study more than one independent variable.

Step 2: Collect Your Data

Once you’ve picked the response variable and explanatory variables you’re going to study, you need to choose an appropriate data collection method. Surveys, company records, and interviews are a few practical methods to gather first-hand information.

To get an actual value that reflects reality, the sample size needs to be big enough and the sample population should be completely random. A biased selection process will lead to misleading results, tainting the effectiveness of this technique.

Step 3: Plot the Data Points on a Graph

If you’re observing one independent variable and one dependent variable, you can use the simple linear regression technique. If you want to study multiple independent variables, you’ll need to use the multiple linear regression method instead.

Use a scatter plot to present your data set. At this point, you should already notice a flat, upward, or downward pattern within your data. Regardless of your chosen method, the dependent variable will always be on the Y-axis and the predictor variables will be on the X-axis.

Step 4: Draw a Regression Line

Next, you need to draw a linear regression line that fits the data. It should go through the center of the observed data. Using the regression equation provided above, you can calculate the expected value of the dependent variable at any given point.

If the straight line is steep, there is a high correlation between the dependent and independent variables and if your line is shallow, there is a low correlation.

Your linear regression model provides an estimate of the dependent variable when an independent variable changes.

Step 5: Share the Results with Your Team

Now, you can share the observed relationship with your team members and discuss possible solutions to refine the user experience or bring up company performance.

To further validate your results, you can conduct a hypotheses test to see the statistical significance of your findings. It gives you an idea if your data follows a normal distribution and if the results are significant. Then, you can draw conclusions about the relationship between variables with greater confidence and present your findings.

Regression Analysis Examples

Now that we’ve covered the benefits and detailed instructions about performing a regression analysis, it’s time to look at several business scenarios where this technique can improve performance and production efficiency.

These examples demonstrate the varied applications of this tool and how you can use it in a range of business settings.

Example 1: Use Multiple Linear Regression Analysis to Identify Important Independent Variables

Regression analysis can help you prioritize important factors with a strong linear relationship. Let’s say you are the HR manager trying to observe your business’s employee satisfaction rate as the dependent variable. You can create a multiple regression analysis model of multiple variables like salaries, work hours, and team sizes to identify variables with a bigger influence – those with a steeper linear regression line.

Make sure the sample size is large enough to represent the population. With the multiple regression analysis results, you can formulate a package of employee benefits that will generate the highest return.

Example 2: Use a Simple Regression Analysis to Predict Values

You own a bricks and mortar baby clothing store and you want to predict your sales levels if birth rates in your city continue to increase. Since you are only studying a single variable, you can use a simple regression model to map out the relationship between the independent variable and the dependent variable.

With the simple linear regression equation, you can get the predicted value of your sales level at any given birth rate. Choose the x-value you are interested in and look at the corresponding y-value, which represents the estimated sales levels.

Example 3: Use Regression Analysis to Discover Hidden Factors

Regression methods are powerful in discovering hidden factors that influence your business. Sometimes, the correlation between dependent and independent variables is not obvious and you may not immediately realize how certain elements hinder your growth until you perform a regression analysis to find out the causal relationship. Inputting a range of independent variables from your website traffic such as traffic sources, time spent on page, bounce rate, and so on could uncover links to your conversions to sales that may be surprising.

You can conduct a multiple regression analysis or a simple linear regression analysis as you see fit. Including less common independent variables assists you in locating the hidden obstacles hindering your growth.

How to Adjust a Regression Analysis

Understanding regression analysis paves the way for a successful business journey. When you’re starting your own business, you need to know how to interpret the relationship between dependent and independent variables as well as how to adjust the regression analysis.

Let’s say you’ve discovered a positive relationship between variables and want to understand the magnitude of change to one variable when you adjust the other. You can invest in increasing the value of one variable and observe the changes in the regression line.

If the regression line is very receptive to changes, it should become significantly steeper or flatter after an adjustment. This can help you to pinpoint key factors and strategies you can adopt to better control the regression analysis results.

For example, you’re running an ad campaign on social media and the link between increasing spending and more post engagement is shallow. You can work with your social media team to improve targeting, produce better visuals, or change the advertisement CTA and see if you can improve the link between higher budgets and better engagement.

If you are adjusting a multiple linear regression, follow the same principle and remember to update the following steps so you don’t get misleading results.

You can also consider adopting machine learning to facilitate calculations and reduce risks of human error. Machine learning can analyze a large volume of data points and adjust a regression analysis effectively.

Limitations of Regression Analysis

Despite being a potent tool, regression analysis comes with a few major drawbacks that you should be mindful of when utilizing this tool. When you’re using this technique, you should always incorporate other analytical tools to compensate for its shortcomings.

It Can’t Identify Hidden Variables not Included in the Study

Regression analysis can only reveal relationships of the variables you’re studying. It can’t guide you to discover factors that aren’t included in the analysis. Therefore, even though this technique can find hidden relationships, you still have to take the initiative to include them in the research first.

Factor analysis is one method that dissects and groups variables based on their characteristics, helping you to understand your data better.

Overfitting and Underfitting

Regression analysis may seem like a simple tool at first glance, however, it still requires experts to fine-tune the parameters and results. Overfitting happens when the analysis paints data more complex than they actually are and underfitting happens when the analysis oversimplifies the results.

Professional researchers have to finely adjust the analysis settings to read the most accurate results. To avoid being misled by overfitting and underfitting, introducing other techniques like multivariate analysis allows you to cross-examine your results.

Multicollinearity Prevents Accurate Interpretation of the Dependent Variable

When two or more independent variables are strongly correlated, this technique fails to dismantle their corresponding and total effects on the dependent variable. Multicollinearity is a common obstacle for researchers using this technique.

The Value of Regression Analysis

Regression analysis offers invaluable insights into the elements in your operation. With this tool, you can predict the value of a variable, understand the causation effects, and study multiple variables together. It is an effective technique for curating profit-oriented strategies.

While you may need to invest in statistical tools or even hiring or contracting a data analyst, performing regression analysis can unlock valuable insights for your business. You can find links between factors – or variables – you may never have expected and the link between other factors may not be as important as you think. As you prepare to expand or realign your business, this information can have an immense impact.

When you use regression analysis, keep in mind its various limitations. It is always a smart idea to include other quantitative and qualitative analytical tools in your study to produce the most fruitful results.

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