When only advertising budget is used as a predictor, this is the simple correlation between advertising and album sales (0.578). The column labelled R contains the values of the multiple correlation coefficient between the predictors and the outcome. Under this table SPSS tells us what the dependent variable (outcome) was and what the predictors were in each of the two models.
![spss ibm andy field non parametric models spss ibm andy field non parametric models](https://www.findcareerinfo.com/wp-content/uploads/2019/08/Best-SPSS-Books-To-Learn-Everything-about-IBM-SPSS.png)
Model 2 refers to when all three predictors are used. Model 1 refers to the first stage in the hierarchy when only advertising budget is used as a predictor.
![spss ibm andy field non parametric models spss ibm andy field non parametric models](https://i.ytimg.com/vi/ADDR3_Ng5CA/maxresdefault.jpg)
In the case of the b for advertising budget this result means that the advertising budget makes a significant contribution ( p < 0.001) to predicting album sales. In other words, the bs are significantly different from 0. For both ts, the probabilities are given as 0.000 (zero to 3 decimal places), and so the probability of these t values (or larger) occurring if the values of b in the population were zero is less than 0.001. If this probability is less than 0.05, then people interpret that as the predictor being a ‘significant’ predictor of the outcome. contains the exact probability that a value of t at least as big as the one in the table would occur if the value of b in the population were zero. The t-test and associated p-value tell us whether the b-value is significantly different from 0. If a predictor is having a significant impact on our ability to predict the outcome then its b should be different from 0 (and large relative to its standard error). This investment is pretty useless for the record company: it invests £1000 and gets only 96 extra sales! Fortunately, as we already know, advertising accounts for only one-third of the variance in album sales. Our units of measurement were thousands of pounds and thousands of albums sold, so we can say that for an increase in advertising of £1000 the model predicts 96 (0.096 × 1000 = 96) extra album sales. In other words, if our predictor variable is increased by one unit (if the advertising budget is increased by 1), then our model predicts that 0.096 extra albums will be sold. This value represents the change in the outcome associated with a unit change in the predictor. This value can be interpreted as meaning that when no money is spent on advertising (when X = 0), the model predicts that 134,140 albums will be sold (remember that our unit of measurement is thousands of albums).
#Spss ibm andy field non parametric models free
![spss ibm andy field non parametric models spss ibm andy field non parametric models](https://img.youtube.com/vi/u5JnILqlX9w/0.jpg)
#Spss ibm andy field non parametric models series
This tutorial is one of a series that accompanies Discovering Statistics Using IBM SPSS Statistics (Field 2017) by me, Andy Field.