#Spss scatter plot with trend line how to#
#Spss scatter plot with trend line download#

When interpreting quadratic effects, however, we only interpret the significance of the highest-order effect – in this case, the squared predictor. Both of these are statistically significant (p <. Otherwise, we can clearly see that the unstandardized beta for conscientiousness is -23.864, and the unstandardized beta for its squared values is 3.106. If you need help reading this table, take a look at my Regression in SPSS guide. Place BOTH your predictors in the independent(s) box, as seen below: Place your outcome variable in the Dependent box, as seen below: Click on Analyze, Regression, and then Linear. Now, we are going to perform a regression as usual. Once you press OK, your dataset should look something like this: If you did it correctly, your window should look like this below. Lastly, label your target variable in the upper left-hand box. This will result in conscientiousness*conscientiousness, which is conscientiousness-squared.

Finally, click on conscientiousness in the left-hand-side again, then click on the arrow highlighted below. To do so, we can click on conscientiousness in the left-hand-side, then click on the arrow highlighted below. Now, we want to create a variable that is conscientiousness-squared. To do so in SPSS, go to Transform then click on Compute Variable. To perform a quadratic regression, we first need to create a new variable. As you can see, there is a clear U-shape to the data, which indicates that quadratic regression should be applied. The instructions below may be a little confusing if your data looks a little different.įirst, we could create a scatter plot of the relationship between conscientiousness and life satisfaction.

If your dataset looks differently, you should try to reformat it to resemble the picture above. The data should look something like this:

In the dataset, we are investigating the relationship of conscientiousness and life satisfaction.

#Spss scatter plot with trend line download#

If you don’t have a dataset, you can download the example dataset here. To calculate a quadratic regression, we can use SPSS. There is more that could be stated about quadratic regression, but we’ll keep it simple. If you are too hard working, then you may be stressed and less happy with your life. However, once you get to a certain level of conscientiousness, your life satisfaction might go back down. If you are hard working, then you are generally happier with your life. For example, conscientiousness may relate to life satisfaction. That is, when one variable goes up, then the other goes up too however, once you get to a certain point, the relationship goes back down. Often, we call the latter of these relationships (the upside down U) a “too much of a good thing” effect. In these instances, the relationship between two variables may look like a U or an upside-down U. In these cases, we need to apply different types of regression.Ī common non-linear relationship is the quadratic relationship, which is a relationship that is described by a single curve. Sometimes our effects are non-linear, however. As always, if you have any questions, please email me at typical type of regression is a linear regression, which identifies a linear relationship between predictor(s) and an outcome.

#Spss scatter plot with trend line how to#

This page is a brief lesson on how to calculate a quadratic regression in SPSS. For this reason, we should turn to other types of regression. Sometimes linear regression doesn’t quite cut it – particularly when we believe that our observed relationships are non-linear.