SPSS Guide to Data Analysis Page 5 of 9 Select your tailed significance (one or two-tailed)depending on your hypotheses Remember directional hypotheses are one-tailed and non-directional hypotheses are two-tailed. If means and standard deviations are needed,click on Options and then click on Means and Standard Deviations Linear Regression Graphs→Scatter→Simple This will let you know if there is a linear relationship or not. Click on Define. The Dependent variable (criterion)should be put in the Y-axis box and the Independent variable(predictor)should be put in the X-axis box and hit OK .In your Output:Double click on the scatterplot.Go to Chart>Options.Click on Total in the Fit Line box,then OK. Make sure it is more-or-less linear.The next step is to check for normality Graphs→Q-Q Put both variables into the Variable box.Hit OK. .Look at the Normal Q-Q Plot of ....not the Detrended Normal Q-Q of... box.If the points are a "smiley face"they are negatively-skewed.This means you will have to raise them to a power greater than one.If the points make a "frown face"then they are positively-skewed.This means you will have to raise them to a power less than one (but greater than zero).To do this,go to the DATA screen then: Transform→Compute Give the new variable a name in the Target Variable box.Since you will be doing many of these (because it is a guess and check)it may be easiest to name it the old variable and then the power you raised it to.For example,SLEEP.2 if I raised it to the.2 power,or SLEEP3 if I raised it to the third power. Click the old variable(that you want to change)into the Numeric Expressions box.Type in the exponent function (**)and then the power you want to raise it to.Hit OK. Redo the Q-Q plot with the NEW variable (i.e.SLEEP.2,and not SLEEP) Repeat until you have the best fit data.The variable that you created with the best-fit data (i.e.SLEEP.2)will be the variable that you will use for the REST OF THE REGRESSION(no more SLEEP). The next step is to remove Outliers.To do so,run a regression: Analyze→Regression→LinearSPSS Guide to Data Analysis Page 5 of 9 · Select your tailed significance (one or two-tailed) depending on your hypotheses. Remember directional hypotheses are one-tailed and non-directional hypotheses are two-tailed. · If means and standard deviations are needed, click on Options and then click on Means and Standard Deviations. Linear Regression Graphs ‡ Scatter ‡ Simple · This will let you know if there is a linear relationship or not. · Click on Define . · The Dependent variable (criterion) should be put in the Y-axis box and the Independent variable (predictor) should be put in the X-axis box and hit OK. · In your Output: Double click on the scatterplot. Go to Chart ‡ Options. Click on Total in the Fit Line box, then OK. · Make sure it is more-or-less linear. The next step is to check for normality. Graphs ‡ Q-Q · Put both variables into the Variable box. Hit OK. · Look at the Normal Q-Q Plot of …., not the Detrended Normal Q-Q of … box. If the points are a “smiley face” they are negatively-skewed. This means you will have to raise them to a power greater than one. If the points make a “frown face” then they are positively-skewed. This means you will have to raise them to a power less than one (but greater than zero). To do this, go to the DATA screen then: Transform ‡ Compute · Give the new variable a name in the Target Variable box. Since you will be doing many of these (because it is a guess and check) it may be easiest to name it the old variable and then the power you raised it to. For example, SLEEP.2 if I raised it to the .2 power, or SLEEP3 if I raised it to the third power. · Click the old variable (that you want to change) into the Numeric Expressions box. Type in the exponent function (**), and then the power you want to raise it to. Hit OK. · Redo the Q-Q plot with the NEW variable (i.e. SLEEP.2, and not SLEEP). · Repeat until you have the best fit data. The variable that you created with the best-fit data (i.e. SLEEP.2) will be the variable that you will use for the REST OF THE REGRESSION (no more SLEEP). · The next step is to remove Outliers. To do so, run a regression: Analyze ‡ Regression ‡ Linear