Regression analysis mathematically describes the relationship between a set of independent variables and a dependent variable. There are numerous types of regression models that you can use. This choice often depends on the kind of data you have for the dependent variable and the type of model that provides the best fit. In this post, I cover the more common types of regression analyses and how to decide which one is right for your data.

I’ll provide an overview along with information to help you choose. I organize the types of regression by the different kinds of dependent variable. If you’re not sure which procedure to use, determine which type of dependent variable you have, and then focus on that section in this post. This process should help narrow the choices! I’ll cover regression models that are appropriate for dependent variables that measure continuous, categorical, and count data.

**Related post**: Guide to Data Types and How to Graph Them

## Regression Analysis with Continuous Dependent Variables

Regression analysis with a continuous dependent variable is probably the first type that comes to mind. While this is the primary case, you still need to decide which one to use.

Continuous variables are a measurement on a continuous scale, such as weight, time, and length.

### Linear regression

Linear regression, also known as ordinary least squares (OLS) and linear least squares, is the real workhorse of the regression world. Use linear regression to understand the mean change in a dependent variable given a one-unit change in each independent variable. You can also use polynomials to model curvature and include interaction effects. Despite the term “linear model,” this type can model curvature.

This analysis estimates parameters by minimizing the sum of the squared errors (SSE). Linear models are the most common and most straightforward to use. If you have a continuous dependent variable, linear regression is probably the first type you should consider.

There are some special options available for linear regression.

**Fitted line plots**: If you have one independent variable and the dependent variable, use a fitted line plot to display the data along with the fitted regression line and essential regression output. These graphs make understanding the model more intuitive.**Stepwise regression and Best subsets regression**: These automated methods can help identify candidate variables early in the model specification process.

### Advanced types of linear regression

Linear models are the oldest type of regression. It was designed so that statisticians can do the calculations by hand. However, OLS has several weaknesses, including a sensitivity to both outliers and multicollinearity, and it is prone to overfitting. To address these problems, statisticians have developed several advanced variants:

**Ridge regression**allows you to analyze data even when severe multicollinearity is present and helps prevent overfitting. This type of model reduces the large, problematic variance that multicollinearity causes by introducing a slight bias in the estimates. The procedure trades away much of the variance in exchange for a little bias, which produces more useful coefficient estimates when multicollinearity is present.**Lasso regression**(least absolute shrinkage and selection operator) performs variable selection that aims to increase prediction accuracy by identifying a simpler model. It is similar to Ridge regression but with variable selection.**Partial least squares (PLS) regression**is useful when you have very few observations compared to the number of independent variables or when your independent variables are highly correlated. PLS decreases the independent variables down to a smaller number of uncorrelated components, similar to Principal Components Analysis. Then, the procedure performs linear regression on these components rather the original data. PLS emphasizes developing predictive models and is not used for screening variables. Unlike OLS, you can include multiple continuous*dependent*variables. PLS uses the correlation structure to identify smaller effects and model multivariate patterns in the dependent variables.

### Nonlinear regression

Nonlinear regression also requires a continuous dependent variable, but it provides a greater flexibility to fit curves than linear regression.

Like OLS, nonlinear regression estimates the parameters by minimizing the SSE. However, nonlinear models use an iterative algorithm rather than the linear approach of solving them directly with matrix equations. What this means for you is that you need to worry about which algorithm to use, specifying good starting values, and the possibility of either not converging on a solution or converging on a local minimum rather than a global minimum SSE. And, that’s in addition to specifying the correct functional form!

Most nonlinear models have one continuous independent variable, but it is possible to have more than one. When you have one independent variable, you can graph the results using a fitted line plot.

My advice is to fit a model using linear regression first and then determine whether the linear model provides an adequate fit by checking the residual plots. If you can’t obtain a good fit using linear regression, then try a nonlinear model because it can fit a wider variety of curves. I always recommend that you try OLS first because it is easier to perform and interpret.

I’ve written quite a bit about the differences between linear and nonlinear models. Read the following posts to learn the differences between these two types, how to choose which one is best for your data, and how to interpret the results.

- What is the Difference Between Linear and Nonlinear Models?
- How to Choose Between Linear and Nonlinear Regression?
- Curve Fitting with Linear and Nonlinear Regression

## Regression Analysis with Categorical Dependent Variables

So far, we’ve looked at models that require a continuous dependent variable. Next, let’s move on to categorical independent variables. A categorical variable has values that you can put into a countable number of distinct groups based on a characteristic. Logistic regression transforms the dependent variable and then uses Maximum Likelihood Estimation, rather than least squares, to estimate the parameters.

Logistic regression describes the relationship between a set of independent variables and a categorical dependent variable. Choose the type of logistic model based on the type of categorical dependent variable you have.

### Binary Logistic Regression

Use binary logistic regression to understand how changes in the independent variables are associated with changes in the probability of an event occurring. This type of model requires a binary dependent variable. A binary variable has only two possible values, such as pass and fail.

**Example:** Political scientists assess the odds of the incumbent U.S. President winning reelection based on stock market performance.

Read my post about a binary logistic model that estimates the probability of House Republicans belonging to the Freedom Caucus.

### Ordinal Logistic Regression

Ordinal logistic regression models the relationship between a set of predictors and an ordinal response variable. An ordinal response has at least three groups which have a natural order, such as hot, medium, and cold.

**Example:** Market analysts want to determine which variables influence the decision to buy large, medium, or small popcorn at the movie theater.

### Nominal Logistic Regression

Nominal logistic regression models the relationship between a set of independent variables and a nominal dependent variable. A nominal variable has at least three groups which do not have a natural order, such as scratch, dent, and tear.

**Example**: A quality analyst studies the variables that affect the odds of the type of product defects: scratches, dents, and tears.

## Regression Analysis with Count Dependent Variables

If your dependent variable is a count of items, events, results, or activities, you might need to use a different type of regression model. Counts are nonnegative integers (0, 1, 2, etc.). Count data with higher means tend to be normally distributed and you can often use OLS. However, count data with smaller means can be skewed, and linear regression might have a hard time fitting these data. For these cases, there are several types of models you can use.

### Poisson regression

Count data frequently follow the Poisson distribution, which makes Poisson Regression a good possibility. Poisson variables are a count of something over a constant amount of time, area, or another consistent length of observation. With a Poisson variable, you can calculate and assess a rate of occurrence. A classic example of a Poisson dataset is provided by Ladislaus Bortkiewicz, a Russian economist, who analyzed annual deaths caused by horse kicks in the Prussian Army from 1875-1984.

Use Poisson regression to model how changes in the independent variables are associated with changes in the counts. Poisson models are similar to logistic models because they use Maximum Likelihood Estimation and transform the dependent variable using the natural log. Poisson models can be suitable for rate data, where the rate is a count of events divided by a measure of that unit’s *exposure* (a consistent unit of observation). For example, homicides per month.

**Example**: An analyst uses Poisson regression to model the number of calls that a call center receives daily.

### Alternatives to Poisson regression for count data

Not all count data follow the Poisson distribution because this distribution has some stringent restrictions. Fortunately, there are alternative analyses you can perform when you have count data.

**Negative binomial regression**: Poisson regression assumes that the variance equals the mean. When the variance is greater than the mean, your model has overdispersion. A negative binomial model, also known as NB2, can be more appropriate when overdispersion is present.

**Zero-inflated models**: Your count data might have too many zeros to follow the Poisson distribution. In other words, there are more zeros than the Poisson regression predicts. Zero-inflated models assume that two separate processes work together to produce the excessive zeros. One process determines whether there are zero events or more than zero events. The other is the Poisson process that determines how many events occur, some of which some can be zero. An example makes this clearer!

Suppose park rangers count the number of fish caught by each park visitor as they exit the park. A zero-inflated model might be appropriate for this scenario because there are two processes for catching zero fish:

- Some park visitors catch zero fish because they did not go fishing.
- Other visitors went fishing, and some of these people caught zero fish.

Whew! That’s many different types of regression analysis! If you’re trying to figure out which one to choose, I hope you will use this information to point yourself in the right direction!

If you’re learning regression, check out my Regression Tutorial!

roy hampton says

Great post Jim. I really like the way you explain the different types of regression.

Jim Frost says

Thank you, Roy! I’m glad that you found it helpful!

Nicol says

Technically, regression examines a relationship between predictor and response variables. I wish people will stop using IV and DV incorrectly. There’s nothing the researchers are manipulating in your examples either.

Jim Frost says

Hi Nicol,

Predictor and response variables are synonyms for independent and dependent variables, respectively. You can use them interchangeably. Also, you’re correct that none of the examples have researchers setting the values for the independent (predictor) variables. However, that’s just fine in regression analysis. These examples are observational studies where you measure data and observe the relationships.

You can also use regression analysis in designed experiments where you use random assignment and the researchers set the values of the experimental variables. The designed experiment approach is particularly good when you want to establish causality (rather than just correlation) and it helps rule out confounding variables. However, this type of experiment isn’t always feasible, and it’s OK to use observational studies as long as you are aware of the limitations and potential problems.

Thanks for writing!

Jim

Mukesh Bishnoi says

Very knowledgeable points

Jim Frost says

Thank you, Mukesh!

Abhishek Singh (@abhi121289) says

Very intitutive. Loved the way you explained it. Thanks Jim.

Jim Frost says

Thank you, Abhishek! I really appreciate the kind words and I’m glad you found it to be helpful!

John Petroda says

For the count example (number of calls an analyst receives daily), curious about using Log transformation of the the dependent count variable and using random forest on that?

Would that work?

Than you…

Jim Frost says

Hi John, unfortunately I’m not overly familiar with random forest models. That’s something I should learn more about!

Renee Sartin says

Hi. I am a student, and I am having grave difficulty in determining what types of variables I have for my study. (still in the learning phase). This is my problem statement. It is not known if and to what extent a positive correlation exists between organizational commitment of supervisors and practicum success among students, and whether student intrinsic motivation moderates the relationship.

Please, if you were me what analysis would you use and why. And to your best knowledge what types of variables are these? I look so foraward to receiving yuour respose.

Jim Frost says

Hi Renee, most likely you are working with either continuous or ordinal variables. To determine which type of variable, check out my glossary definitions for both:

Continuous variables

Ordinal variables

For pairs of continuous variables, you can use the Pearson correlation. Be sure to create a scatterplot and determine whether the relationship is linear.

For pairs of ordinal variables, you can use Spearman’s correlation.

Best of luck!

nasim says

hi, i am a student and i have a problem, i want to predict bankruptcy in IRAN . and i want to use LASSO regression to choose more effective independent variables, i select dependent variable y(0 , 1), and i have 50 independent variables that are financial ratios , and i do analysis on Spss, but i have many problem with result, so i have a main question, can i use lasso to predictive with 0 and 1 dependent variable, can i use Spss to do it?

thank you Jim.

Jim Frost says

Hi Nasim, I haven’t done this myself but apparently it is possible. I recommend that you read this about using Lasso with logistic regression. This example uses R, but I’m not sure about SPSS.

Shiji says

Hai Jim,

It is very informative. I found it very useful for the researcher. I have a doubt in my study, i wish test the relationship between domestic tourists and foreign tourists. when we look at the total number the same trend is observed by the two . so I wish to know which method can be used to prove that the pattern of change of domestic and total are the same or the movement of total tourist is same as the domestic.

thanking you

Shiji

Jim Frost says

Hi Shiji, I’m not 100% sure I understand what you are studying. However, it sounds like you might need to include one or more interaction term in your model to determine whether the relationships between your independent variables and dependent variables depend on whether a tourist is a domestic or foreign tourist. I write about comparing regression lines in an article. Read that article and, in the graphs where I show the regression lines for two different groups, imagine that one group represents domestic tourists and the other represents foreign tourists. That might be what you’re looking for. I hope this helps!

CMO says

Hi Jim,

Thanks for posting this.

I would appreciate your thoughts on my analyses. I have an independent variable that is a count variable (number of days at work). My dependent variables are all continuous variables. Can I use a simple linear regression model to test a moderated mediation relationship with the the IV as a count variable?

Thanks!

Jim Frost says

Hi, I’d give the model a try but check the residual plots to be sure that the model satisfies the assumptions. If you’re fitting just the one independent variable, you can use a fitted line plot and really just see at glance if it provides a good fit. I show an example early in this post.

Raof says

Thanks Jim for this informative Blog

I want to examine the influence of predictor variables such as Physical activity (low, moderate,high), sedentary time and dietary habits ( fruits, vegetables, junk food etc.) on a dependent variable BMI, collapsed into lower level ordinal categories like underweight, normal, overweight and obese. If I have to see the odds of being overweight/obese for a person based on these behavioural practices. What would be the appropriate regression analysis. Or am i required to dichotomize (1.underweight/normal and 2.overweight/ obese) my dependent variable and use binary logistic regression. Your views will be much appreciated.

Jim Frost says

Hi Raof,

It sounds like you need to use Ordinal Logistic Regression. Your dependent variable is an ordinal variable. Unfortunately, I don’t have a good example of this type of regression to share with you, but it can do what you describe.

The one problem I see is that you also have an ordinal predictor (physical activity–high, medium, and low). That can be problematic. You can try to fit the model and check the residuals to see if you satisfy the assumptions. If it doesn’t work, you can try converting those three levels to two indicator variables. Indicator variables are binary variables where you have one for each level–however you need to leave one out of your model (e.g. High, Moderate). You need to leave one level out for the analysis to run so I intentionally didn’t include Low–but you can leave any level out.

But, for your ordinal response variable, use ordinal logistic regression.

I hope this helps!

Pankaj Kumar says

Hello Mr. Jim

I hope you are doing very well.

I am in confusion in the testing of regression analysis. Well, as we read in basic Statistics that F test is a two tailed test whereas when we use F test in testing of regression analysis then we always treat it as a one tailed test. Why so?

Thanks

Pankaj

Jim Frost says

Hi Pankaj,

That’s a great question. As it turns out, for regression and ANOVA, the F-test is always a one-tailed test. The F-test tests the ratio of two variances (technically mean squares rather than variances). In regression and ANOVA, it’s a one-tailed test because of the nature of what you’re testing. In One-Way ANOVA, you’re determining whether the between group variance is greater than the within group variance. In regression, you want to determine whether the model with all of your predictors is better than the model with no predictors (only the constant). Those are one-tailed tests by the definition of how the hypotheses are specified–you are determining whether one variance is significantly larger than the other variance.

To see how the F-test works in detail I suggest you read my post about the F-test and One-Way ANOVA. Regression analyses uses the F-test in a similar way but changes the variances in the ratio. You’re testing the model with all of your predictors compared to the model with no predictors (just the constant). You can also read my post about the F-test of overall significance.

You do use two-tailed F-tests for Variance Tests. In this case, you require the ability to determine whether the variance in the numerator is larger than or less than the variance in the denominator. You’re testing both directions (larger and smaller), hence it’s two-tailed.

I hope this helps!

Hassan Elkatawneh says

That is very helpful, but did not answer my own need. If you can advice my, I have 2 IV and one DV, in addition I have one moderator variable. What is the best test, all variables are ratio scale? thanks in advance for your help

Jim Frost says

Moderator variables are commonly used in psychology–which isn’t my field. However, from my understanding, they are essentially interaction effects. That is, the effect between an independent variable and a dependent variable depends on the value of another variable. To fit this type of model, you can use OLS multiple regression. You just need to include the appropriate interaction term in the model. For more information, read my post about understanding interaction effects.

Ahmad says

Hi Jim

Im student, have problem with how can choose which regression model i need to use in my case.

i have many variables with one response like mix design variables and the response is compresive strength of concrete

Jim Frost says

Hi Ahmad, choosing the best regression model is a very important task. In statistics, we call that process model specification. I’ve written an entire blog post about it that will help you. Model Specification: Choosing the Correct Regression Model

Best of luck with your analysis!

Sebastian says

Hello Mr. Frost,

first of all great website. Wish I knew the existence back when I was in my bachelors studies. My question is concerned with log-linear models and binary variables. I developed a model for a thesis that looks like this:

log y_t – log y_t-1 = beta_0 + beta_1 A + beta_2 B + u. The dependent variable is the percentage change of the Treasury yield and A and B are binary events like FOMC meetings. Is this example considered a log-linear regression model? Thanks in advanvce.

CMO says

Thank you, Jim. This is helpful

Jim Frost says

Hi, I’m so glad that is helpful!

Antoine says

Hey Jim,

Thank you for this post, really like the way you explain things.

I am working on a project where I am assessing the relation between discriminative attitude and healthcare provision in health care workers:

– Discriminative attitude: is the independent variable and will be measured using a series of 10 scaled question (scaled from 1 to 5). In that way any respondent will have a score somewhere between 1 and 5, hence i am assuming it is a continuous ordered variable right?

– Healthcare provision: is the dependent variable and will also be measured using a series of 10 scaled question (scaled from 1 to 4) – similarly to the independent variable, i am assuming this is a continuous ordered variable.

In your opinion, what analytical model would be most suitable for that purpose?

Thanks!

Kai says

This is fantastic! I’m a 3rd year statistics major at university, and it is so refreshing having this overview of regression set out in such a clear way. Major kudos for all your work!

Jim Frost says

Thank you, Kai!

Sandeep says

Sir which model is best for stock market short time prediction

Jim Frost says

Hi Sandeep, ah, I get asked that question many times. And, if I knew the answer to that one, I’d be so rich that I’d have more money than I’d know what to do with! The fact is that the stock market is fairly unpredictable. If you could predict it in the short term, then everyone would know exactly where to put there money at any given point. And, then the advantage is gone. So, it doesn’t work that way.

Prashant Dey says

Hey Jim,

Thanks a lot.

This is very good explanation of regression techniques.

This post will gain another boost if a flow chart or map for choosing the right technique is provided.

Just a suggestion.

Thank You again!

This is really helpful.

Lin says

Hi Jim,

I am currently doing a research on behavior pattern in Peru.

My dependent variable are binary but my independent variables is a mix between binary and continuous variables . I have to use data from previous round to predict the later round . For example dependent variable is smokes at age 15 which is binary and some of the independent variables are mathscore standardize at age 12 (continuous) and drinks alcohol at age 12 (binary). I also think that there is also an endogeneity problem in this setting. Hence, I do not know what regression in STATA is best for this kind problems? Also how do i solve the endogenous problem ?

I thought of using linear probability model, since it seems the easiest but I don’t think this is the best method .

Thanks in advance

Kind regard,

J.zhong

SK says

Hi Jim,

Really nice article

I am stuck in a problem where i have to do regression but I am unable to decide with which regression model i should proceed. To give you a background, I have sales (dependent variable) and lets say a, b, c,d and e are independent variable(The sales is triggered by these independent variable). My objective is to find the importance of each independent variable, so that we can prioritize on that channel. Now the values in independent variable can be either “Open”, “close” or blank. I thought of using Binary regression model but here i have three type of values but Binary takes only two.

Pls suggest

Thanks in advance

SK

Jim Frost says

Hi SK,

Assuming that sales is a continuous dependent variable, you do not want to use binary logistic regression or other specialized type. Binary logistic is for cases where the

DVis binary. You’re talking about independent variables.You have categorical independent variables, which you can include in linear regression. Most statistical software will code those as indicator variables automatically. Does “blank” represent a missing value or is that an actual value for the IVs? If you have “Open”, “Close”, and missing values, you’d just need one indicator variable for each IV. The indicator variable could be something like Open_A, which is a 1 if variable A = Open and 0 if variable A = Close. Repeat for the other channels. But, again, most software will do that automatically.

Finding the relative importance of each IV is a separate matter that I write about: Identifying the Most Important IVs

I hope this helps!

WW says

Hi Jim, Thanks for this post. It clears the regression clouds haunted me for a loooong time! It is really helpful! Statistics had been my nightmare since Uni but I guess no more. Look forward to your next posts!

Jim Frost says

Hi WW, I’m so happy to read your comment! I strive to make these posts as easy and intuitive to understand as possible. It makes my day to read that they’ve helped you!

Stayed tuned, I am writing more posts but taking a short break at the moment.

Anudeep Venkata says

Hi Jim,

I read your blog on regression and it was lucid. But I am confused about the regression testing with the model with below mentioned variables

I have control and test samples which have discrete quantitative variables.

If I have to model this with another independent variables like gender (dummy variable or independent), and one ordinal variable.

Since I have my control and test with other factors like gender and housing. I want to determine the link and analyze the effect of Housing and gender on my control and test samples.

Can you please suggest which regression model would be appropriate. Is that Binary logistic or Multi nominal logistic regression??

Thank you in advance.

Jim Frost says

Hi Anudeep,

It really depends on what type of dependent variable you have. Can you clarify the nature of your dependent variable?

Lokesh Gupta says

Hi Jim,

Is there any way we can mention a categorical dependent variable to be ordinal variable before passing into the logistic regression model as it might provide extra edge to the model output

Jim Frost says

Assuming that I understand your question correctly, if you have an ordinal dependent variable, you should use ordinal logistic regression to analyze your data.

Maro says

Hi Jim – Thank you so much for this clear post! This is very helpful.

I have a question. My dependent variable is categorical (3 categories). when I tried nominal logistic regression using minitab, the model didn’t converge. After some research I found out that my data has a collinearity problem making it difficult for the nominal logistic model to converge.

Instead, I tried both linear regression and partial least squares, using minitab, on the same data and the results seem reasonable. My question is, can I use linear or PLS regression if my response variable is categorical? or do I have to do nominal logistic regression?

Thanks again!

Jim Frost says

Hi Maro,

Thanks for writing with the great question. Unfortunately, when you have a true categorical variable, you cannot treat it as a numeric variable. Suppose you have three groups. You can label each one with numbers: 1, 2, 3. However, that doesn’t mean you can analyze them as numbers. Those numbers might represent: scratch, dent, and tear. Or maybe gold, silver and bronze. The numeric labels don’t measure/represent the actual characteristic that the groups are based on. To illustrate this, the value 2 doesn’t indicate that it is exactly twice the value of 1. The numbers also suggest a logical order to the groups that just doesn’t exist (otherwise you’d be using ordinal logistic regression).

Consequently, you can’t use linear or PLS regression. I don’t know what your model is or your other variables, but if you have only categorical variables, you can try the chi-square test of independence to look for relationships among categorical variables. Otherwise, I think nominal logistic regression is your best bet. To address the collinearity, you might try removing or linearly combining variables and using them in nominal logistic regression. By linearly combining them yourself, you’re incorporating some aspects of PLS into nominal logistic regression.

I hope this helps!

Maro says

Hi Jim

Thank you so much! This is very helpful. Here’s more information on what I’m trying to do.

The problem:

I’m studying the impact of adopting technological capabilities (independent variables) on teams performance (dependent variables) in IT.

The approach:

Identify significant performance clusters between teams, and understand how the adoption of technological capabilities impact teams performance clusters.

My dataset consists of:

1) 17 independent variables, one variable is ordinal (1 to 7 scale), 7 variables are categorical (true/false), 9 variables are continuous/numbers.

2) 3 dependent variables, these are continuous/numbers.

3) Dataset size is 36 subjects.

My analysis is two steps:

1) Run the 3 dependent variables (performance measures) through clustering algorithm and find out if there are significant clusters. This test was successful and I found 3 significant clusters (high, medium, low).

2) Now I want to test the influence of the 17 independent variables (technological capabilities) on the 3 clusters. I planned to use multinomial regression but it didn’t work due to the issue mentioned in the earlier post.

My questions:

1) Given the number of independent variable (17), is there a recommended data size for the multinomial logistic regression to work successfully? How many subject can be good enough?

2) Since my clusters consist of the 3 dependent variable, I’m thinking of testing the impact of the 17 independent variables on the 3 dependent variables that make the clusters instead of the clusters themselves using PLS or multi linear regressions? This is still a workaround but I may consider it just in case my logistic regression model continuous to fail.

3) Any other recommendations?

Alternatives:

I checked the linear discriminant function and it seems promising. I think the problem is I don’t know how to interpret its results to find out how the independent variables influence the 3 clusters. I’m not planning to build a prediction model with either logistic regression or discriminant function, I only need the “inferential” piece not the “prediction” piece since I just want to understand the influence of the independent variables on the cluster not interested in building a prediction model.

Your help is much appreciated! Thanks again!

Tony says

Hey Jim! Once I’ve trained a logistic model and know which predictors are important, is there a way that I can define an optimal range for my input variables? For example if I’m trying to adjust three settings on a machine to minimize my probability of introducing a defect, how could I use my coefficients from a logistic model to decide what the mean setting should be for all three to maximize probability of no defects? Thanks!

Jim Frost says

Hi Tony,

There are ways to do optimize your inputs. The process for doing this depends on the statistical software package that you are using. So, it’s hard for me to give any practical advice about it. Essentially the process takes the model that you settled on and then uses an optimization routine to determine which input values optimize the output. So, it’s a separate process from the model specification and fitting process–although your software might tie them together. Typically, you can specify whether you want to obtain a specific target response value, minimize the response value, or maximize the response value. In your case, you probably want to minimize the probability of defects.

Another approach is to perform a Monte Carlo simulation where you generate random data for the input values in your regression equation. The data for each input follow a distribution that you specify. You input these randomly generated data in your regression equation, which produces a distribution of outputs that you can then study. Additionally, you can change the distributions of the inputs to determine how that affects the distribution of the outputs. That allows you to answer “what if” types of questions about changing the inputs.

I hope this helps!

Kirti says

Hi. Thanks for this post. It is very informative.

I am a student. I have a dataset with 300,00 rows and 77 columns. How do I approach the data?

Also I have to do some predictive analysis. My independent variables are a mix of continuous and nominal categorical variables and my dependent variable is continuous. Which regression model should I use?

Jim Frost says

Hi Kirti, with a few exceptions, the type of regression analysis you should use doesn’t depend on the size of the dataset and number of variables. Usually, it’s the type of variables that you have.

For your case, I’d start with multiple linear regression. See if you can get a good fit to your data using that procedure.

Kelly Parris Yeldham says

Hi Jim,

Cool article!

How should I proceed when I want to compare a hypothetical non-linear graph with an experimental graph, when the function and the shape of graph is unknown? I want to quantify the two graph shapes, and compare their equations, as opposed to visually overlapping the two graphs.

I have a continuous independent variable of time, and the dependent variable of velocity calculated from previous iterations of velocity starting from 0. Increasing the order of the polynomial that I use brings me closer and closer to the shape, but I do not believe that my function is polynomial.

Thank you!

Jim Frost says

Hi Kelly,

I’m not 100% sure what you’re asking, but I think might be asking how to specify a model that fits the curve in your data and how to determine whether that model adequately fits the curvature. If so, I’ve got the perfect blog post for you: Curve Fitting Using Linear and Nonlinear Regression. I cover the different methods you can use to fit curves and how to determine which provides the best fit.

For your data, if the polynomials don’t provide a good fit, you might well need to transform your data or use a nonlinear model. Note that nonlinear models are different than linear models that use polynomials to model curvature, which is what you’re doing. I talk about all of that in that post!

I hope that helps!

Barney says

Hi Jim,

Very informative article.

Pardon me for perhaps a simplistic question, but is it considered regression analysis if the function is known, and I want to test the correlation of experimental data against the function? If so, what should I research to learn more about it?

Jim Frost says

Hi Barney,

Thanks! I’m glad it was helpful!

If you have a theoretical function and want to compare it to the fit you obtain for your data when you specify the same model as the theoretical function, you can use the confidence intervals (CIs) for the parameter estimates. If these CIs do not include the parameter values from the known function, you have sufficient evidence to conclude that the differences between your parameter estimates and the known function are statistically significant. Most statistical software should be able to produce this type of CI, although it might not always be included in the default output.

So, that’s what you should look into: CIs for the parameter estimates (coefficients) in a regression equation.

pipi says

Hi Jim,

I am now in reseach about regression model. I was wrong before because I used polynomial regression and Trend analysis (Time series) for predicting my data which the response variable is discrete and the predictor is continuous.

Actually I want to estimate and predict the travel time, so I’ll describe the way I collect the data,…

I collected the travel time datas (response variable)about 1 month, everyday.. And in everyday i collected the datas from 6am-10.59pm, where the interval per 1 hour.

Ex. on Sunday– 6am-6.59 am = 42 minutes, then 7am-7.59am= 40 minutes, and so on

so I want to estimate and predicting by using regression.

Is it right if i used Poison regression for solving my problem?

Can I combine with Trend Analysis especially Quadratic ? if I can combine how it would like?

Really need your suggest,

Thanks Jim

Jim Frost says

Hi Pipi,

Time series analyses require data that are collected at consistent intervals and that do not have any gaps. Your data have gaps (midnight – 5:59AM), so you can’t use time series analysis. Also, you use Poisson regression when your response variable is a count. Your response seems to be a continuous variable.

I would try using linear regression analysis and then including predictors such as time of day, maybe day of week, etc. Include the time components as predictors. You can include polynomials if needed. Regression with time related data can be tricky but it can work.

Trying fitting the models, checking the residuals, and adjusting as necessary. In addition to the regular residual plots, be sure to pay extra attention to the residuals vs order plot because you have time ordered data (assuming you record them in your worksheet in time order).

I have not done much regression with time related data, so I don’t have much more to suggest than trying that approach. Best of luck with your analysis!

pipi says

Thanks alot for your replying Jim,

Actually I kinda confused, because my supervisor said that my response variable which is travel time is discrete data. Because the way i collected, because in every interval time, i only have 1 data for every day..

ex. on Sunday– 6am – 6.59am = 42 minutes, 7am-7.59am = 32 minutes, so on

on Monday — 6 am – 6.59am = 40 minutes, 7 am-7.59am = 30 minutes, so on

on Tuesday — 6 am -6.59am = 30 minutes, 7am-7.59am =20 minutes, so on

is it still continuous data or discrete? Because at first I choose continuous too…

And my next question, if i choose time as my predictor variable, how it would like?

because as my example above, it is in interval, can it be like:

Y (mean of travel time at 6am-6.59am) = 37.33,

x (predictor variable) = 6

??

And Jim, can I contact you in private because I really need some suggestions about my research or about regression. Thank you so much

Jim Frost says

Hi Pipi,

Time is usually a continuous variable even if you collect it once per interval per day. The type of data doesn’t change based on how often you collect measurements. I suppose you could make the case that it’s a count of minutes if you only recorded whole minutes. In that case, you could try Poisson regression. But, generally time is considered a continuous variable. Personally, I’d try least squares regression first and see how well you can model the data using it.

The response variable, you’d just use the travel time.

For the predictor variables you’d include time related variables and you could possibly include other variables as well if you have that data. For example, you can trying including Hour of Day, Day of Week. And, if you have the data, you could try weather conditions too.

I wish I could help more in depth, but if I did that for everyone who asks, I woouldn’t have time for my own life! I get a lot of requests for that. As it is answering comments of a more general nature takes a lot of time! I hope you understand. But, I do try to provide general tips and points–like I am here! 🙂

pipi says

Thanks a lot Jim, it does help…..

Jim Frost says

You’re very welcome! Best of luck with your analysis!

Barney says

Thank you Jim!

I’ve been fiddling around with Minitab trying to get it to include CIs for the parameter estimates, but I just can’t find information online to learn to enable it specifically for parameter estimates. Would you know if this is possible on Minitab, or could you please name some software that I might be able to use to do the CIs? It’s for asymptotic regressions.

Jim Frost says

Hi Barney, yes, Minitab can display the CIs for parameter estimates. On the main dialog box, click the Results button. Under Display of Results, choose Expanded. When you rerun the analysis, it will now display those CIs along with various other additional results.

Torsha says

Hi Jim,

I found this extremely helpful!

I have a very elementary doubt. Is is practically feasible that both dependent and independent variables (all of them) are all dummies, i.e., are binary in nature?

Jim Frost says

Hi Torsha,

Yes, you can certainly do this using binary logistic regression. That type of regression allows you to use the binary dependent variable, which the other types of regression don’t allow. Then, you can add the binary independent variables, which isn’t unique to binary logistic regression.

I think the type of model you describe is relatively common in the health care field. That’s not my field but I attended a presentation by someone in the field who talked about how they use that type of model to assess the risk of a surgical procedure for different patients. All the independent variables are patient traits (e.g., high blood pressure, etc.) and the dependent variable was survival. The model allowed doctors to enter patient attributes and estimate survival probability. This type of model can also be used in other fields.

Seun Obed says

Hi Jim,.

Nice work you’re doing here. I’d like to find out the model to use in running a regression where the time period is in months and we are looking at 15months and we have five independent variables. The research aim at looking into factors that significantly increased unemployment during a period of recession which lasted 15 months in my country.

Jim Frost says

Thanks Seun! Performing regression analysis with time series data can be trick but it’s possible. At some point, I might write a post about that topic! I’d start out with linear regression. You’ll almost certainly need to include time information along with your independent variables. Very possibly include time lagged variables as well. For instance, the state of variable X in the previous time period might affect the current time period. You should always check the residual plots. However, when you’re working with time series data, be sure to check the Residuals versus Time Order plot to ensure that you’re accounting for all the time-related effects.

Seun Opaleye says

Thanks for your response Jim!

Recall that recession is measured on a quarterly basis and my country experienced recession for a period of 5 quarters which gives us 15 months.

Will it be proper to run an analysis based on data where T=5 (five quarters) & five independent variables.

Will GMM capture this data size? Or what do you suggest?

Jim Frost says

I think you might have a problem there. I’m not the most familiar with Generalized Methods of Moments (GMM). However, my understanding is that this method trades off efficieny in order to obtain more robust estimates using fewer assumptions. In other words, you need a larger sample size using this method compared to OLS. And, you’d have a problem with using OLS for your study. In OLS, you generally need at least 10 observations per term in your model. You’re nowhere near that. I don’t know the guidelines for GMM, but it is less efficient so presumably you’d need more observations per term.

You probably have an additional issue as well. If you have data only from times of recession, it limits the variability in your dependent variable and possibly the independent variables. This situation weakens the ability of the analysis to detect relationships in the data. It’s much better if you have data from strong and weak economic times because that allows the analysis to more easily determine which independent variables covary with the dependent variable. It’s much harder to determine how the variables covary when at least one of them (dependent variable) doesn’t vary that much.

I think you need more data and particularly include a variety of economic conditions.

Best of luck with your analysis!

Seun Opaleye says

Thank you Jim. I will increase the time period for the model to include period of growth together with the recession, then use structural breaks to identify effects during the period of recession. Right?

Lis Bittencourt says

Hi, Jim! Thank you for your post.

My doubt is: if I have a continuous dependent variable and a count independent variable, what is the most suited regression analysis?

I understand that if the count variable was my dependent variable, a poisson regression was OK. But I am not sure if the inverse situation demands a regular simple linear regression.

Thank you,

Lis

richard sadaka says

Hi, Jim

i am trying to find the regression line of 1 dependent variable and 35 independent variables (all categorical), but i faced a problem related to the significance of 33 out of 35 coef, all of them is insignificant

I could really appreciate any help

Jim Frost says

Hi Richard,

That’s a very difficult question to answer. The explanation can range from no relationship existing between those variables to an effect that is too small given your sample size to be detectable (i.e., your statistical power is too low).

Additionally, for 35 categorical independent variables, you probably need at least 700 observations. Possibly higher depending on the number of levels per categorical variable and the distribution of observations across those levels.

Can you narrow those variables down to a few that theory strongly suggests?

richard sadaka says

actually i can’t since they are the components of the consumption (i.e revenue = C1 + C2 +C3 ……..C35 + S)

Geo says

Hi jim,

Thanks for the post.I have a set of categorical & continous variables that need to predict the success of a event ( fail/pass).The categorical values in the data set take multiple values ( q1 to q400 ) which may be related to region code or some other parameter which may impact the final output.what may be the best model to fit in here.

Jim Frost says

Hi Geo, because of the binary dependent variable, you’d need to use binary logistic regression. Using this type of regression allows you to determine which variables are correlated with changes in the probabilities of the success of an event. You can use both continuous and categorical independent variables with this type of analysis.

Linch dan says

First of all, I want to thank you to maintain an excellent blog and it is very helpful for everyone.

I am one of a student who is struggling to find the best regression type for my study. In brief, Animals were fed with a supplement with different doses namely (0, .5 %, 1% ,2% and 3%). Each treatment group has 9 replicates ( ex .5% group has 9 animals). After feeding trail, Different blood parameters (Ex: Immune cells) are measured along with supplement concentration in blood. Now I want to correlate blood parameters(Ex: Immune cells counts ) with supplement concentration in blood.

For this experiment, What is the Correct Type of Regression Analysis?

What is Regression type that I need to work on is it linear regression or non-linear regression? I am still in the learning curve and your help is highly appreciated.

Jim Frost says

Hi Linch,

It could be either linear or nonlinear regression. It depends on the nature of the relationships between the variables. There’s no way I could possibly guess about that. Definitely start with linear regression and determine whether you can obtain an adequate fit by checking the residual plots. If you can get a good fitting using linear regression, you can avoid nonlinear regression, which is often more complicated.

Best of luck with your analysis!

Alex says

Hi Jim

Thanks for this constructive post. I have a question.

I run a multiple regression model in which my dependent variable is the vote for Social Democratic parties and my independent variables are associated with a range of factors.

Do you think that I should keep this single multiple regression model or could I divide this model into more multiple regression models? The advantage of dividing the model into more multiple regression models is that I acquire a better R squared value.

However, my argument in favour of keeping the single multple regression model is that the vote is a complicated phenomenon, which is affected by many factors and by controlling for more factors, we can explain more of the variation in y.

Thank you in advance.

Jim Frost says

Hi Alex,

I’m not quite sure how you would be dividing up the different models if you’re using the same dependent variable? Maybe by election, region, or year?

To make this determination, you’ll really have to use your subject area knowledge. If you think the relationships between the independent variables and the dependent variable are likely to be constant across the entire large model, that’s a good reason to use just one model. However, if those relationships change based on however you are dividing the models, that’s an argument for either dividing the model or modeling those changes themselves in the large model–possibly by including interaction effects.

Best of luck with your analysis!

vivi says

Hi Jim,

I wanna ask about type of data for multiple regression

I use google to get a rating from a place, for example the ranking for the Eiffel Tower is 4.6. I know, on Google itself, this value is the result of processing between the ratings given by the review of the place being assessed and other factors.

What I want to ask, is a value of 4.6 called ordinal data or numerical data (scale or interval)? so that it can be used as a variable from multiple regression. Because other variables are types of intervals or ratios.

Even if the ranking value is ordinal, should it be changed to numeric first so that it can be used in multiple regression models?

Jim Frost says

Hi Vivian,

Sorry about the delay in replying to your question. I’ve been away traveling.

Ratings are usually ordinal data. For example, if diners can rate a restaurant from 1 to 5 stars, it’s an ordinal scale. You can average the number of stars and obtain an average of 4.6 or other value. But the data points are ordinal.

However, I don’t know how Google determines the rankings. If it’s more complex than users simply entering an ordinal value, Google’s rating might not be ordinal. I don’t enough about it to really say for sure. But ratings are generally ordinal data.

If the rating is ordinal, you can’t just change it to numeric data. You can represent ordinal data using numbers, but it’s still ordinal data. Image that we use the numbers 1, 2, and 3 to represent first, second, and third in a race. Even though we are using numbers, they are not numeric or continuous data. For example, numerically, the number 2 is twice the value of 1. However, that does not mean that the second place finisher took twice as long as the first place finisher. And, the third place finisher isn’t necessarily three times as long. Etc. You can change how ordinal data are represented, but it doesn’t change the underlying fact that they are ordinal data.

If you have ordinal data and it’s the dependent variable, use ordinal logistic regression. If it’s an independent variable, it can be tricky. You can try using it as an independent variable, but pay extra attention to the residual plots. They may or may not provide a good fit for reasons that I describe in the race example!

Rajesh Kavediya says

Dear Jim. Really a good post explaining the type regressions to be used in various situation. I need your help. I am working on analysing the determinants of inflation expectations. My dependent variable is categorical, i.e between 0-1, 1-3, 3-5, 5-10 ,10-15 and above 15 (inflation expectations) and independent variables are either categorical (like age group, income and education level) or macro-economical variables like inflation, unemployment and growth. I will be grateful if you could suggest the appropriate regression framework/model.

ab says

Hi Jim, I have both independent and dependent variable in likert type (strongly agree, agree, somewhat agree, disagree and strongly disagree). What kind of regression method is helpful in order to find the effect of predictor variable on response variable. Thank you