You can find out more about our enhanced content as a whole here, or more specifically, learn how we help with testing assumptions here. The last three columns express the results of the dependent t-test, namely the t -value t the degrees of freedom df and the significance level Sig. At the end of these four steps, we show you how to interpret the results from your linear regression. Once your data is entered into spss, it can be cleaned and your data analyses can begin.
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Outliers are simply single data points within your data that do not follow the usual pattern (e.g., in a study of 100 students' IQ scores, where the mean score was 108 with only a small variation between students, one student had a score of 156. First, let's take a look at these four assumptions: Assumption #1: Your dependent variable should be measured on a continuous scale (i.e., it is measured at the interval or ratio level). It is used when we want to predict the value of a variable based on the value of another variable. 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. In our case this would be: t (19) -4.773,.0005. If you are using older versions of spss Statistics, you will need to transfer the variables using the former method. In our enhanced guides, we show you how to: (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. The dependent t-test can also be used to compare different subjects, but this does not happen very often. 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.
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