Regression analysis mathematically describes the relationship between independent variables and the dependent variable. It also allows you to predict the mean value of the dependent variable when you specify values for the independent variables. In this regression tutorial, I gather together a wide range of posts that I’ve written about regression analysis. My tutorial helps you go through the regression content in a systematic and logical order.
This tutorial covers many facets of regression analysis including selecting the correct type of regression analysis, specifying the best model, interpreting the results, assessing the fit of the model, generating predictions, and checking the assumptions. I close the post with examples of different types of regression analyses.
If you’re learning regression analysis, you might want to bookmark this tutorial!
When to Use Regression and the Signs of a High-Quality Analysis
Before we get to the regression tutorials, I’ll cover several overarching issues.
Why use regression at all? What are common problems that trip up analysts? And, how do you differentiate a high-quality regression analysis from a less rigorous study? Read these posts to find out:
- When Should I Use Regression Analysis?: Learn what regression can do for you and when you should use it.
- Five Regression Tips for a Better Analysis: These tips help ensure that you perform a top-quality regression analysis.
Tutorial: Choosing the Right Type of Regression Analysis
There are many different types of regression analysis. Choosing the right procedure depends on your data and the nature of the relationships, as these posts explain.
- Choosing the Correct Type of Regression Analysis: Reviews different regression methods by focusing on data types.
- How to Choose Between Linear and Nonlinear Regression: Determining which one to use by assessing the statistical output.
- The Difference between Linear and Nonlinear Models: Both kinds of models can fit curves, so what’s the difference?
Tutorial: Specifying the Regression Model
Selecting the right type of regression analysis is just the start of the process. Next, you need to specify the model. Model specification is the process of determining which independent variables belong in the model and whether modeling curvature and interaction effects are appropriate.
Model specification is an iterative process. The interpretation and assumption confirmation sections of this tutorial explain how to assess your model and how to change the model based on the statistical output and graphs.
- Model Specification: Choosing the Correct Regression Model: I review standard statistical approaches, difficulties you may face, and offer some real-world advice.
- Using Data Mining to Select Your Regression Model Can Create Problems: This approach to choosing a model can produce misleading results. Learn how to detect and avoid this problem.
- Guide to Stepwise Regression and Best Subsets Regression: Two common tools for identifying candidate variables during the investigative stages of model building.
- Overfitting Regression Models: Overly complicated models can produce misleading R-squared values, regression coefficients, and p-values. Learn how to detect and avoid this problem.
- Curve Fitting Using Linear and Nonlinear Regression: When your data don’t follow a straight line, the model must fit the curvature. This post covers various methods for fitting curves.
- Understanding Interaction Effects: When the effect of one variable depends on the value of another variable, you need to include an interaction effect in your model otherwise the results will be misleading.
- When Do You Need to Standardize the Variables?: In specific situations, standardizing the independent variables can uncover statistically significant results.
- Confounding Variables and Omitted Variable Bias: The variables that you leave out of the model can bias the variables that you include.
Tutorial: Interpreting Regression Results
Tutorial: Interpreting Regression ResultsAfter choosing the type of regression and specifying the model, you need to interpret the results. The next set of posts explain how to interpret the results for various regression analysis statistics:
- Coefficients and p-values
- Constant (Y-intercept)
- Comparing regression slopes and constants with hypothesis tests
- R-squared and the goodness-of-fit
- How high does R-squared need be?
- Interpreting a model with a low R-squared
- Adjusted R-squared and Predicted R-squared
- Standard error of the regression (S) vs. R-squared
- Five Reasons Your R-squared can be Too High: A high R-squared can occasionally signify a problem with your model.
- F-test of overall significance
- Identifying the Most Important Independent Variables: After settling on a model, analysts frequently ask, “Which variable is most important?”
Tutorial: Using Regression to Make Predictions
Tutorial: Using Regression to Make PredictionsAnalysts often use regression analysis to make predictions. In this section of the regression tutorial, learn how to make predictions and assess their precision.
- Making Predictions with Regression Analysis: This guide uses BMI to predict body fat percentage.
- Predicted R-squared: This statistic evaluates how well a model predicts the dependent variable for new observations.
- Understand Prediction Precision to Avoid Costly Mistakes: Research shows that presentation affects the number of interpretation mistakes. Covers prediction intervals.
- Prediction intervals versus other intervals: Prediction intervals indicate the precision of the predictions. I compare prediction intervals to different types of intervals.
Tutorial: Checking Regression Assumptions and Fixing Problems
Like other statistical procedures, regression analysis has assumptions that you need to meet, or the results can be unreliable. In regression, you primarily verify the assumptions by assessing the residual plots. The posts below explain how to do this and present some methods for fixing problems.
- The Seven Classical Assumptions of OLS Linear Regression
- Residual plots: Shows what the graphs should look like and why they might not!
- Heteroscedasticity: The residuals should have a constant scatter (homoscedasticity). Shows how to detect this problem and various methods of fixing it.
- Multicollinearity: Highly correlated independent variables can be problematic, but not always! Explains how to identify this problem and several ways of resolving it.
Examples of Different Types of Regression Analyses
The last part of the regression tutorial contains regression analysis examples. I’ll be adding more. Some of the examples are included in previous tutorial sections. Most of these regression examples include the datasets so you can try it yourself!
- Linear regression with a double-log transformation: Models the relationship between mammal mass and metabolic rate using a fitted line plot.
- Modeling the relationship between BMI and Body Fat Percentage with linear regression.
- Curve fitting with linear and nonlinear regression.
would you also throw some ideas on Instrumental variable and 2 SLS method please?
Those are great ideas! I’ll write about them in future posts.
such a splendid compilation, Thanks Jim
Thank you!
great work by great man,, it is easily accessible source to access the scholars,, sir i am going to analyse data plz send me guidlines for selection of best simple linear/ multiple linear regression model, thanks
Hi, thank you so much for your kind words. I really appreciate it! I’ve written a blog post that I think is exactly what you need. It’ll help you choose the best regression model.
Hi Jim!
Can you write on Logistic regression please!
Thank you
Hi! You bet! I plan to write about it in the near future!
Hi, Jim!
I’m really happy to find your blog. It’s really helping, especially that you use basic English so non-native speaker can understand it better than reading most textbooks. Thanks!
Hi Dina, you’re welcome! And, thanks so much for your kind words–you made my day!
yes.the language of the topic is very easy , i would appreciate you sir ,if you let me know that ,If rank
correlation is r =0.8,sum of “D”square=33.how we will calculate /find no. observations (n).
I’m not sure what you mean by “D” square, but I believe you’ll need more information for that.
Hello Jim,
I am using Step-wise regression to select significant variables in the model for prediction.how to interpret BIC in variable selection?
regards,
Zishan
Hi, when comparing candidate models, you look for models with a lower BIC. A lower BIC indicates that a model is more likely to be the true model. BIC identifies the model that is more likely to have generated the observed data.
Hello Jim
I’d like to
Know what your suggestions are with regards to choice of regression for predicting:
the likelihood of participants falling into
One of two categories (low Fear group codes 1 and high Fear 2 … when looking at scores from several variables ( e.g. external
Other locus of control, external social locus of control , internal locus of control and social phobia and sleep quality )
It was suggested that I break the question up to smaller components … I’d appreciate your thoughts on it …. thanks!
Because you have a binary response (dependent variable), you’ll need to use binary logistic regression. I don’t know what types of predictors you have. If they’re continuous, you can just use them in the model and see how it works.
If they’re ordinal data, such as a Likert scale, you can still try using them as predictors in the model. However, ordinal data are less likely to satisfy all the assumptions. Check the residual plots. If including the ordinal data in the model doesn’t work, you can recode them as indicator variables (1s and 0s only based on whether an observation meets a criteria or not. For example, if you have a scale of -2, -1. 0, 1, 2 you could recode it so observations with a positive score get a 1 while all other scores get a 0.
Those are some ideas to try. Of course, what works best for your case depends on the subject area and types of data that you have.
I hope this helps!
thank you Jim this is helpful
You’re very welcome, Lisa! I’m glad you found it to be helpful!
Hi Jim, I would like to see you writing something about Cross Validation (Training and test).
Patrik
Very Good Explanation about regression ….Thank you sir for such a wonderful post….
Independent variables range from 0 to 1 and corresponding dependent variables range from 1 to 5 . If we apply regression analysis to above and predict the value of y for any value of x that also ranges from 0 to 1, whether the value of y will always lie in the range 1 to 5?
In my experience, the predicted values will fall outside the range of the actual dependent variable. Assuming that you are referring to actual limits at 1 and 5, the regression analysis does not “understand” that those are hard limits. The extent that the predicted values fall outside these limits depends on the amount of error in the model.
Hi jim
i’m really happy to find your blog
Hello Jim
I’d like to
Know what your suggestions are with regards to choice of regression for predicting:
dependent variable is count data but it does not follow a poisson distribution
independent variables include categorical and continuous data
I’d appreciate your thoughts on it ….
thanks!
Hi Sarkhani,
Having count data that don’t follow the Poisson happens fairly often. The top alternatives that I’m aware of are negative binomial regression and zero inflated models. I talk about those options a bit in my post about choosing the correct type of regression analysis. The count data section is near the end. I hope this information points you in the right direction!
Hello Jim,
I have a set of data consisting of dependent variable which is of continuous type and independent variables which are of categorical type. The interesting thing which I found is that majority (more than 70%)of the independent variables belong to the category 1. The category values range from scale 1 to 5. I would like to know the appropriate sampling technique to be used. Is it appropriate to use linear regression or should I use other alternatives? Or any preprocessing of data is required? Please help me with the above.
Thanks in advance
Raju.
Hi Raju,
I’d try linear regression first. You can include that categorical variable as the independent variable with no problem. As always, be sure to check the residual plots. You can also use one-way ANOVA, which would be the more usual choice for this type of analysis. But, linear regression and ANOVA are really the same analysis “under the hood.” So, you can go either way.
I hope this helps!
Hi Jim,
I have a doubt regarding which regression analysis is to be conducted. The data set consists of categorical independent variables (ordinal) and one dependent variable which is of continuous type. Moreover, most of the data pertaining to an independent variable is concentrated towards first category (70%). My objective is to capture the factors influencing the dependent variable and its significance. In that case should I consider the ind. variables to be continuous or as categorical? Thanks in advance.
Raju.
Hi Raju,
I think I already answered your question on this. Although, it looks like you’re now saying that you have an ordinal independent variable rather than a categorical variable. Ordinal data can be difficult. I’d still try using linear regression to fit the data.
You have two options that you can try.
1) You can include the ordinal data as continuous data. Doing this assumes that going from 1 to 2 is the same scale change as going from 2 to 3 and so on. Just like with actual continuous data. Although, you can add polynomials and transformations to improve the fit.
2) However, that doesn’t always work. Sometimes ordinal data don’t behave like continuous data. For example, the 2nd place finisher in a race doesn’t necessarily take twice as long as the 1st place finisher. And the difference between 3rd and 2nd isn’t the same as between 1st and 2nd. Etc. In that case, you can include it as a categorical variable. Using this approach, you estimate the mean differences between the different ordinal levels and you don’t have to assume they’ll be the same.
There’s an important caveat about including them as categorical variables. When you include categorical variables, you’re actually using indicator variables. A 5 point Likert scale (ordinal) actually includes 4 indicator variables. If you have many Likert variables, you’re actually including 4 variables for each one. That can be problematic. If you add enough of these variables, it can lead to overfitting. Depending on your software, you might not even see these indicator variables because they code and include them behind the scenes. It’s something to be aware of. If you have many such variables, it’s preferable to include them as continuous variables if possible.
You’ll have to think about whether your data seems more like continuous or categorical data. And, try both methods if you’re not sure. Check the residuals to make sure the model provides a good fit.
Ordinal data can be tricky because they’re not really continuous data nor categorical data–a bit of both! So, you’ll have to experiment and assess how well the different approaches work.
Good luck with your analysis!
Thank you Jim.
Jim, could you recommend a model based on the following:
1. I can see a strong visual correlation between the left side trough and peak and the right side. When the left has a steep vector so does the left, for example.
2. This does not need to be the case, the left could provide a much steeper slope compared to right or a much more narrow slope.
3. The parallels intrigue me and I would like to measure if the left slope can be explained by the right to any degree.
4. I am measuring the rise and fall of industry valuations over time. (it is the rise and fall in these valuations over time that create these ~ parallel slopes.
5. My data set since 1886 only provides 6 events, but they are consistent as described.
6. I attempted correlate rising slope against declining.
Hi David,
I’m having time figuring out what you’re describing. I’m not sure what slopes you’re referring and I don’t know what you mean by the left versus right slopes?
If you only have 6 data points, you’ll only be able to fit an extremely simple model. You’ll usually need at least 10 data points (absolute minimum but probably more) to even include one independent variable.
If you have two slopes for something and you want to see if one slope explains the other, you could try using linear regression. Use one slope as an independent variable and another as a dependent variable. Slopes would be a continuous variable and so that might work. The underlying data for each slope would have to be independent from data used for other slopes. And, you’ll have to worry about time order effects such as autocorrelation.
Jim. Thank you so much. Especially for such a prompt response! The slopes are coming from IT segment stock valuations over 150 years. The slopes are derived from valuation troughs and peaks. So it is a graph like you’d see for the S&P. Sorry I was not clear on this.
Hi Jim. Did you write on Instrumental variable and 2 SLS method? I am interested in them. Thanks so all excellent things you did on this site.
Hi,
I haven’t yet, but those might be good topics for the future!
Hi Jim,
Thanks so much, your blog is really helpful! I was wondering whether you have some suggestions on published articles that use OLS (nothing fancy, just very plain OLS) and that could be used in class for learning interpreting regression outputs. I’d love to use “real” work and make students see that what they learn is relevant in academia. I mostly find work that is too complicated for someone just starting to learn regression techniques, so any advice would be appreciated!
Thanks,
Hanna