Specifically, we predict that life satisfaction is highest when conscientiousness is moderate, whereas life satisfaction decreases when conscientiousness is either low or high.Īs you can see, we have a predictor variable (conscientiousness) and an outcome variable (life satisfaction).įirst, let’s start by making a simple chart of the data in Excel. In this dataset, we are looking at the relationship between conscientiousness and life satisfaction. Fortunately, we can do this in Excel.Ĭlick here if you need to download the example dataset. It is sometimes difficult to know when to apply quadratic regression, but creating a scatterplot and adding a quadratic trendline can help you make this decision. If you need a refresher about the purpose of quadratic regression, check out my guide on calculating quadratic regressions in Excel. As always, if you have any questions, please email me at quadratic regression models a relationship using a single curve, such as the “too much of a good thing” effect. While there are many approaches to help you make this decision, this guide reviews one of the easiest approaches – creating a scatterplot and visually identifying whether a quadratic trendline fits your data.
Sometimes it is difficult to know when to conduct a quadratic regression.
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