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It is not possible to include a dummy variable for each level of the variable because the dummy variables would lack independence and create multicollinearity cheap 250 mg tetracycline. Therefore for k levels of a variable tetracycline 500 mg line, there will be k − 1 dummy variables purchase 500mg tetracycline mastercard, for example, for a variable with three levels, two dummy variables will be created. It is helpful in interpreting the results if each dummy variable has a binary coding of 0 or 1. The variable parity1 with three levels from Chapter 5, that is parity coded as babies with 0, 1 or 2 or more siblings, can be recoded into dummy variables using Transform → Recode into Different Variables. Dummy variables are invalu- able for testing the effects of ordered groups that are likely to be different, for example, lung function in groups of non-smokers, ex-smokers and current smokers. It is essen- tial that dummy variables are used when groups are non-ordered; for example, when marital status is categorized as single, married or divorced. Related dummy variables must always be included in a model together because they cannot be treated independently. If one dummy variable is significant in the model and a related dummy variable is not, they must both be left in the model together. In the Model Summary table, the adjusted R square value shows that the addition of the dummy variables for parity improves the fit of the model only slightly from 0. In the Coefficients table, the P values for the unstandardized coefficients show that both dummy variables are significant predictors of weight with P values of 0. However, the low standardized coefficients and the small partial correlations in the Excluded Variables table show that the dummy variables contribute little to the model compared to length and gender. Using the values in the Coefficients table, the regression equation is now as follows: Weight =−4. The coefficients for the final two terms indicate that after adjusting for length and gender, babies with one sibling are on average 0. The previous regression model with one conti- nuous and two categorical variables, that is, length, gender and parity, can be further extended with the addition of second continuous explanatory variable, that is, head circumference. The final predictive equation could be used to generate normal values for term babies, to calculate z scores for babies’ weights, or to calculate per cent predicted weights. Correlation and regression 229 The regression model obtained previously can be built on to test the influence of the variable, head circumference. The model in which parity2 was included as a binary vari- able is used because including parity with three levels coded as dummy variables did not substantially improve the fit of the model. The Model Summary table shows that the adjusted R square increases slightly from 55. The Change Statistics indicates that the increase in R2 from Model 1 to Model 2 is significant. In the Coefficients table, all predictors are significant and the standardized coefficients show that length contributes to the model to a greater degree than head circumference, but that head circumference makes a larger contribution than gender or parity. This is expected because the initial Pearson’s correlations showed a significant association between length and head circumference with an r value of 0. As a result of the mutlicollinearity, the standard error for length has inflated from 0. Head circumference is expected to vary with length as a result of common factors that influence body size and growth. In this situation, head circumference should be classified as an alternative 230 Chapter 7 outcome rather than an independent explanatory variable because it is on the same developmental pathway as length. Each model building situation will be different but it is important that the relationships between the variables and the purpose of building the model are always carefully considered. If an interactive effect is present, the two lines would have different slopes and would cross over or intersect at some point. To test for the presence of an interaction, the two variables are multiplied to create a cross-product term, which is included in the multiple regression model. In the following equation, the fourth term represents an interaction between length and gender. Also, once again, coding of binary variables as 0 and 1 is helpful for interpreting interactions.

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