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However, a table of major importance is the coefficients table shown below. Unfortunately, SPSS gives us much more regression output than we need. REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT performance /METHOD=ENTER iq /SCATTERPLOT=(*ZRESID ,*ZPRED) /RESIDUALS HISTOGRAM(ZRESID).
![in the simple linear regression equation let b in the simple linear regression equation let b](https://miro.medium.com/max/1400/1*QS5Brc-Pp6Mhp3CyMJwb6g.png)
*Simple regression with residual plots and confidence intervals. Selecting these options results in the syntax below. The screenshots below show how we'll proceed. Rerunning our minimal regression analysis from However, a lot of information - statistical significance and confidence intervals- is still missing. Right, so that gives us a basic idea about the relation between IQ and performance and presents it visually. So for a job applicant with an IQ score of 115, we'll predict 34.26 + 0.64 * 115 = 107.86 as his/her most likely future performance score. That is, IQ predicts performance fairly well in this sample.īut how can we best predict job performance from IQ? Well, in our scatterplot y is performance (shown on the y-axis) and x is IQ (shown on the x-axis). R 2 = 0.403 indicates that IQ accounts for some 40.3% of the variance in performance scores. We now have some first basic answers to our research questions. Here we simply click the “Add Fit Line at Total” icon as shown below.īy default, SPSS now adds a linear regression line to our scatterplot. Right-clicking it and selecting Edit c ontent In Separate Window opens up a Chart Editor window. Let's now add a regression line to our scatterplot. There seems to be a moderate correlation between IQ and performance: on average, respondents with higher IQ scores seem to be perform better. So first off, we don't see anything weird in our scatterplot. GRAPH /SCATTERPLOT(BIVAR)=iq WITH performance /MISSING=LISTWISE /TITLE='Scatterplot Performance with IQ' /subtitle 'All Respondents | N = 10'. *Scatterplot with title and subtitle from Graphs -> Legacy Dialogs -> Scatter. Walking through the dialogs resulted in the syntax below. a subtitle that says which respondents or observations are shown and how many.a title that says what my audience are basically looking at and.
![in the simple linear regression equation let b in the simple linear regression equation let b](https://miro.medium.com/max/1568/1*GK1xDJVAhcwJNt8F1J7KVw.jpeg)
We'll create our chart fromĪnd we'll then follow the screenshots below. This will tell us if the IQ and performance scores and their relation -if any- make any sense in the first place. Create Scatterplot with Fit LineĪ great starting point for our analysis is a scatterplot. We'll answer these questions by running a simple linear regression analysis in SPSS. The main thing Company X wants to figure out isĭoes IQ predict job performance? And -if so- how? The resulting data -part of which are shown below- are in simple-linear-regression.sav.