Regression analysis is among the most commonly used statistical methods for data analysis. Undoubtedly, there are many other statistical techniques except regression analysis that can be helpful in carrying out the data analysis, but regression analysis, over the years, has maintained its credibility. The reason is that it is common, easy to understand, and perform. Still, many students face common problems in regression analysis. Such problems hinder the process of effective analysis and do not allow you to get to the desired conclusion.
So, how can you avoid the common problems in regression analysis? What should you do to avoid them in your analysis? If you are looking for the answers to these questions, you have come to the right place. In today’s post, we will unpack the 4 best tips to avoid problems in regression analysis. Before that, let’s define the regression analysis and enlist the common problems that students face.
What Does It Mean By Regression Analysis?
Regression analysis is a statistical method of data analysis which allows for the measurement of the impact of different variables on the topic. It is an analysis which tells you which factors matter the most, which can be ignored, and how all the factors influence the overall research problem. In order to understand the regression analysis fully, it is important that you have an idea of the following terms:
Dependent variable. This is the main factor that you are trying to understand or predict.
Independent variable. This is the factor that impacts the dependent variable.
Common problems faced by students in regression analysis
Different kinds of problems can encounter during the performance of regression analysis. Before discussing the tips to avoid those problems, it is important to have an idea of the common problems. Primarily, there are five kinds of problems faced by students, mostly during regression analysis. They are listed as follows:
- Non-linearity in the response-predictor relationships
- Correlation of error terms
- A non-constant variance of the error term
- Outliners and high leverage points
Hence, these are the problems that student researchers mostly face when doing regression analysis. If you are also facing one of these problems, read the tips below or contact a dissertation writing service to help you.
4 best tips to avoid common problems in regression analysis
After reading the information given above, you now have a good idea of the common problems that you might face during regression analysis. However, you still do not know how to avoid these problems. Hence, a brief description of the 4 best tips in this context is as follows:
Conduct thorough research before the analysis
Before you begin the regression analysis, make sure that you conduct a thorough research about the problem you are investigating. As an analyst, you must review the extensive literature to develop an understanding of all the variables, their relationships, and expected coefficient signs. Expanding your knowledge base helps you collect the right type of data for your analysis and reduces the occurrence of common problems in regression analysis.
Use a simple model if possible
Most problems in regression analysis occur when students use complex models to analyze the data. They use models that they do not know or use for the first time. This is not the right way to go ahead. The reason is that complex models require you to solve complex regression equations, which you cannot. So, always try to go for a model which is simple and does not require the formulation of complex equations.
Correlation does not mean causation
If there is a correlation between two things, it does not mean that there is causation too. Causation is entirely a different matter. In regression analysis, you only reveal the correlation present between the independent and dependent variables. It could be a linear correlation or any other, but it may not tell you exactly if the independent variable is causing the change in the dependent variable. So, always remember this golden rule, and you will save yourself from many common problems in regression analysis.
Check your residual plots
Residual plots are an easy and quick way to check for problems in your regression analysis. Residual plots are basically the scatter plots that help you make adjustments to your overall analysis. But how? It does so by telling you the places where you fail to model curvature present in your dataset. The primary function of this plot is to locate the problems in regression analysis, and you must perform it.
Conclusively, regression analysis is a very powerful statistical method to unearth the relationship between independent and dependent variables. However, facing common problems in regression analysis is also common. As the analyst, you can reduce all those problems by employing the 4 best tips mentioned above. So read and apply them carefully.