Our multicollinearity in SPSS assignment help experts calculating the odds ratio
- The table was reconstructed to show the number of occurrence for case and control
|central obesity||no central obesity||total|
|Occupation by education level|
The odds ratio for central obesity for the non-agricultural versus the agricultural for subjects with no education is 2.28 The odds ratio is
The odds ratio for central obesity for the non-agricultural versus the agricultural for subjects with no education is 0.73
Interpretation of the odds ratio resultsYes, there is evidence of a (potential) interaction between education level and occupation group for the risk of central obesity. This is because, for non-educated people, non-agriculture workers have higher odds of having central obesity than agriculture workers. Conversely, for educated respondents, non-agriculture workers have lower odds of having central obesity than agriculture workers. Since the odds ratio of non-agriculture versus agriculture changes with education level, there is evidence of a (potential) interaction between education level and occupation group for risk of central obesity. No, there is no evidence of a potential interaction between age and sex. This is because there is no variation in the mean earning of men and women across age group, and there is no variation in earnings of age groups across sex. Specifically, the mean earnings of men are greater than women, whether it is the age group 16-19 or 20-24, and the mean earnings of age group 20-24 are greater than the age 16-19, whether it is male or female.
Multicollinearity in SPSS homework help from our diligent expertThere is no multicollinearity in model 1, but there is in model 2. Because BMI is This means there will be a high correlation between BMI and height and BMI and weight. Therefore, including BMI, height, and weight will lead to a multicollinearity problem, and the evidence is a significant change as observed here or changes insignificance. The multicollinearity in the SPSS homework help expert, therefore, concludes that there is a negative association between BMI and forced vital capacity. This is because the second model does not satisfy the assumption of no multicollinearity, which means that the model is not robust. Therefore, we use model 1 which does not have this problem as the best model and from this model, is a negative association between BMI and forced vital capacity,
Online multicollinearity in SPSS tutors fitting a regression model for hypothesis testingThe null and alternative hypothesis is given as
The p-value for this coefficient is 0.024<0.05, which means we will reject the null hypothesis that the coefficient of the interaction is not significantly different from 0. We, therefore, conclude that there is significant interaction between age and sex. Age has a positive association with blood pressure as the coefficient estimate for age is positive. Moreover, this effect is significant as p<.0001. With the interaction, the effect of age on blood pressure is
The effect of age on blood pressure for a male is thus 0.94, while for a female is . This means the association between age and blood pressure is stronger for females. There is a change in sign and significance because the interaction of age and solvent was included in the model. This is because the interaction is not significant, and including it has used up the degree of freedoms, which leads to high p-values. There a significant association between solvent exposure and PTA in the bivariate model, but not the multivariate model because of possible multicollinearity that arises with the inclusion of interaction between age and solvent exposure. Therefore, our online multicollinearity in SPSS online tutors conclude that there a significant association between age and PTA in the bivariate model, but not the multivariate model because of possible multicollinearity that arises with the inclusion of interaction between age and solvent exposure.