Regression analysis

REGRESSION ANALYSIS

HOW TO PERFORM REGRESSION IN SPSS

LINEAR REGRESSION ANALYSIS USING SPSS STATISTICS

Introduction

Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable (or sometimes, the outcome variable). The variable we are using to predict the other variable’s value is called the independent variable (or sometimes, the predictor variable). For example, you could use linear regression to understand whether exam performance can be predicted based on revision time; whether cigarette consumption can be predicted based on smoking duration; and so forth. If you have two or more independent variables, rather than just one, you need to use multiple regression analysis.

This “quick start” guide shows you how to carry out linear regression using SPSS Statistics, as well as interpret and report the results from this test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for linear regression to give you a valid result. We discuss these assumptions next.

SPSS Statistics

Assumptions

When you choose to analyse your data using linear regression, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using linear regression. You need to do this because it is only appropriate to use linear regression if your data “passes” seven assumptions that are required for linear regression to give you a valid result. In practice, checking for these seven assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task.

Before we introduce you to these seven assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., not met). This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out linear regression when everything goes well! However, don’t worry. Even when your data fails certain assumptions, there is often a solution to overcome this. First, let’s take a look at these seven assumptions:

Assumption one: Your dependent variable should be measured at the continuous level (i.e., it is either an interval or ratio variable). Examples of continuous variables include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth.

Assumption two: Your independent variable should also be measured at the continuous level (i.e., it is either an interval or ratio variable). See the bullet above for examples of continuous variables.

Assumption three: There needs to be a linear relationship between the two variables. Whilst there are a number of ways to check whether a linear relationship exists between your two variables, we suggest creating a scatterplot using SPSS Statistics where you can plot the dependent variable against your independent variable and then visually inspect the scatterplot to check for linearity. Your scatterplot may look something like one of the following:

If the relationship displayed in your scatterplot is not linear, you will have to either run a non-linear regression analysis, perform a polynomial regression or “transform” your data, which you can do using SPSS Statistics. In our enhanced guides, we show you how to:

1. a) Create a scatterplot to check for linearity when carrying out linear regression using SPSS Statistics;

(b) Interpret different scatterplot results; and

(c) Transform your data using SPSS Statistics if there is not a linear relationship between your two variables.

Assumption Four: There should be no significant outliers. An outlier is an observed data point that has a dependent variable value that is very different to the value predicted by the regression equation. As such, an outlier will be a point on a scatterplot that is (vertically) far away from the regression line indicating that it has a large residual, as highlighted below:

The problem with outliers is that they can have a negative effect on the regression analysis (e.g., reduce the fit of the regression equation) that is used to predict the value of the dependent (outcome) variable based on the independent (predictor) variable. This will change the output that SPSS Statistics produces and reduce the predictive accuracy of your results. Fortunately, when using SPSS Statistics to run a linear regression on your data, you can easily include criteria to help you detect possible outliers. In our enhanced linear regression guide, we: (a) show you how to detect outliers using “casewise diagnostics”, which is a simple process when using SPSS Statistics; and (b) discuss some of the options you have in order to deal with outliers.

• Assumption FIVE:You should have independence of observations, which you can easily check using the Durbin-Watson statistic, which is a simple test to run using SPSS Statistics. We explain how to interpret the result of the Durbin-Watson statistic in our enhanced linear regression guide.
• Assumption SIX:Your data needs to show homoscedasticity, which is where the variances along the line of best fit remain similar as you move along the line. Whilst we explain more about what this means and how to assess the homoscedasticity of your data in our enhanced linear regression guide, take a look at the three scatterplots below, which provide three simple examples: two of data that fail the assumption (called heteroscedasticity) and one of data that meets this assumption (called homoscedasticity):

Whilst these helps to illustrate the differences in data that meets or violates the assumption of homoscedasticity, real-world data can be a lot more messy and illustrate different patterns of heteroscedasticity. Therefore, in our enhanced linear regression guide, we explain: (a) some of the things you will need to consider when interpreting your data; and (b) possible ways to continue with your analysis if your data fails to meet this assumption.

• Assumption SEVEN:Finally, you need to check that the residuals (errors) of the regression line are approximately normally distributed (we explain these terms in our enhanced linear regression guide). Two common methods to check this assumption include using either a histogram (with a superimposed normal curve) or a Normal P-P Plot. Again, in our enhanced linear regression guide, we: (a) show you how to check this assumption using SPSS Statistics, whether you use a histogram (with superimposed normal curve) or Normal P-P Plot; (b) explain how to interpret these diagrams; and (c) provide a possible solution if your data fails to meet this assumption.

EXAMPLES

SPSS Statistics

Example

A salesperson for a large car brand wants to determine whether there is a relationship between an individual’s income and the price they pay for a car. As such, the individual’s “income” is the independent variable and the “price” they pay for a car is the dependent variable. The salesperson wants to use this information to determine which cars to offer potential customers in new areas where average income is known.

SPSS Statistics

Setup in SPSS Statistics

In SPSS Statistics, we created two variables so that we could enter our data: Income (the independent variable), and Price (the dependent variable). It can also be useful to create a third variable, caseno, to act as a chronological case number. This third variable is used to make it easy for you to eliminate cases (e.g., significant outliers) that you have identified when checking for assumptions. However, we do not include it in the SPSS Statistics procedure that follows because we assume that you have already checked these assumptions. In our enhanced linear regression guide, we show you how to correctly enter data in SPSS Statistics to run a linear regression when you are also checking for assumptions.

regression analysis

SPSS Statistics\Test Procedure in SPSS Statistics

The five steps below show you how to analyse your data using linear regression in SPSS Statistics when none of the seven assumptions in the previous section, Assumptions, have been violated. At the end of these four steps, we show you how to interpret the results from your linear regression. If you are looking for help to make sure your data meets assumptions #3, #4, #5, #6 and #7, which are required when using linear regression and can be tested using SPSS Statistics, you can learn more about our enhanced guides on our Features: Overview page.

Note: The procedure that follows is identical for SPSS Statistics versions 18 to 28, as well as the subscription version of SPSS Statistics, with version 28 and the subscription version being the latest versions of SPSS Statistics. However, in version 27 and the subscription version, SPSS Statistics introduced a new look to their interface called “SPSS Light”, replacing the previous look for versions 26 and earlier versions, which was called “SPSS Standard”. Therefore, if you have SPSS Statistics versions 27 or 28 (or the subscription version of SPSS Statistics), the images that follow will be light grey rather than blue. However, the procedure is identical.

1. Click Analyze > Regression > L..on the top menu, as shown below:

Published with written permission from SPSS Statistics, IBM Corporation.

You will be presented with the Linear Regression dialogue box:

Published with written permission from SPSS Statistics, IBM Corporation.

2. Transfer the independent variable, Income, into the Independent(s):box and the dependent variable, Price, into the Dependent: You can do this by either drag-and-dropping the variables or by using the appropriate   buttons. You will end up with the following screen:

Published with written permission from SPSS Statistics, IBM Corporation.

3. You now need to check four of the assumptions discussed in the Assumptionssection above: no significant outliers (assumption #3); independence of observations (assumption #4); homoscedasticity (assumption #5); and normal distribution of errors/residuals (assumptions #6). You can do this by using the and features, and then selecting the appropriate options within these two dialogue boxes. In our enhanced linear regression guide, we show you which options to select in order to test whether your data meets these four assumptions.
4. Click on the button. This will generate the results.

HOW TO INTERPRETE REGRESSION RESULTS

regression analysis

THE OUT PUT OF SPSS ON REGRESSION ANALYSIS

Adjusted R-squared

Adjusted R-squared is a statistical measure that is closely related to the more commonly known R-squared (R²) value in the context of linear regression analysis. While R-squared measures the proportion of the variance in the dependent variable (the variable being predicted) that is explained by the independent variables (the predictors) in a regression model, Adjusted R-squared takes into account the number of independent variables used in the model, providing a more conservative and useful assessment of model fit.

R-squared (R²): R-squared is a measure of how well the independent variables in a regression model explain the variability in the dependent variable. It ranges from 0 to 1, with higher values indicating a better fit. Specifically, R-squared represents the proportion of the total variation in the dependent variable that is explained by the model. However, as you add more independent variables to the model, R-squared tends to increase, even if the additional variables do not significantly improve the model’s predictive power. This can lead to overfitting, where the model fits the training data extremely well but may not generalize well to new data. Adjusted R-squared: Adjusted R-squared addresses the issue of overfitting by penalizing the inclusion of unnecessary independent variables in the model. It takes into account the number of predictors in the model and adjusts R-squared accordingly.

The formula for Adjusted R-squared is; Adjusted R² = 1 – [(1 – R²) * (n – 1) / (n – k – 1)]

R² is the regular R-squared.

n is the number of data points (observations).

k is the number of independent variables in the model.

Adjusted R-squared will always be lower than R-squared when you have multiple independent variables, and it tends to decrease as you add irrelevant or redundant variables to the model. It provides a more realistic assessment of the model’s fit by accounting for model complexity.

In summary, while R-squared tells you how well your regression model fits the data, Adjusted R-squared helps you determine whether the improvement in model fit achieved by adding more independent variables is justified by the increased complexity. It is a useful tool for model selection and comparison, as it encourages the use of simpler models that explain the data adequately without unnecessary complexity.

SPSS Statistics will generate quite a few tables of output for a linear regression. In this section, we show you only the three main tables required to understand your results from the linear regression procedure, assuming that no assumptions have been violated. A complete explanation of the output you have to interpret when checking your data for the six assumptions required to carry out linear regression is provided in our enhanced guide. This includes relevant scatterplots, histogram (with superimposed normal curve), Normal P-P Plot, casewise diagnostics and the Durbin-Watson statistic. Below, we focus on the results for the linear regression analysis only.

regression analysis

The first table of interest is the Model Summary table, as shown below:

Published with written permission from SPSS Statistics, IBM Corporation.

This table provides the R and R2 values. The R value represents the simple correlation and is 0.873 (the “R” Column), which indicates a high degree of correlation. The R2 value (the “R Square” column) indicates how much of the total variation in the dependent variable, Price, can be explained by the independent variable, Income. In this case, 76.2% can be explained, which is very large.

The next table is the ANOVA table, which reports how well the regression equation fits the data (i.e., predicts the dependent variable) and is shown below:

Published with written permission from SPSS Statistics, IBM Corporation.

This table indicates that the regression model predicts the dependent variable significantly well. How do we know this? Look at the “Regression” row and go to the “Sig.” column. This indicates the statistical significance of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it is a good fit for the data).

The Coefficients table provides us with the necessary information to predict price from income, as well as determine whether income contributes statistically significantly to the model (by looking at the “Sig.” column). Furthermore, we can use the values in the “B” column under the “Unstandardized Coefficients” column, as shown below:

Published with written permission from SPSS Statistics, IBM Corporation.

to present the regression equation as:

Price = 8287 + 0.564(Income)

CORRELATION ANALYSIS

Pearson Correlation

The bivariate Pearson Correlation produces a sample correlation coefficient, r, which measures the strength and direction of linear relationships between pairs of continuous variables. By extension, the Pearson Correlation evaluates whether there is statistical evidence for a linear relationship among the same pairs of variables in the population, represented by a population correlation coefficient, ρ (“rho”). The Pearson Correlation is a parametric measure.

This measure is also known as:

• Pearson’s correlation
• Pearson product-moment correlation (PPMC)

Common Uses

The bivariate Pearson Correlation is commonly used to measure the following:

• Correlations among pairs of variables
• Correlations within and between sets of variables

The bivariate Pearson correlation indicates the following:

• Whether a statistically significant linear relationship exists between two continuous variables
• The strength of a linear relationship (i.e., how close the relationship is to being a perfectly straight line)
• The direction of a linear relationship (increasing or decreasing)

Note: The bivariate Pearson Correlation cannot address non-linear relationships or relationships among categorical variables. If you wish to understand relationships that involve categorical variables and/or non-linear relationships, you will need to choose another measure of association.

Note: The bivariate Pearson Correlation only reveals associations among continuous variables. The bivariate Pearson Correlation does not provide any inferences about causation, no matter how large the correlation coefficient is.

Data Requirements

To use Pearson correlation, your data must meet the following requirements:

1. Two or more continuous variables (i.e., interval or ratio level)
2. Cases must have non-missing values on both variables
3. Linear relationship between the variables
4. Independent cases (i.e., independence of observations)
   • There is no relationship between the values of variables between cases. This means that:
     • the values for all variables across cases are unrelated
     • for any case, the value for any variable cannot influence the value of any variable for other cases
     • no case can influence another case on any variable

The biviariate Pearson correlation coefficient and corresponding significance test