Our multicollinearity in SPSS assignment help experts calculating the odds ratio

  1. The table was reconstructed to show the number of occurrence for case and control
central obesity no  central obesity total
Education level
None 604 345 949
Any 665 307 972
Occupation group
Agricultural 642 406 1048
Non-agricultural 627 246 873
Occupation by education level
No education
Agricultural 459 303 762
Non-agricultural 145 42 187
Any education
 Agricultural 201 85 286
 Non-agricultural 434 252 686
Our multicollinearity in SPSS assignment help experts then used the formula below to calculate the odds ratio. The odds ratio is

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 results

Yes, 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 expert

There 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 testing

The 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.

Let’s take a look at another example on the odds ratio

In this SPSS homework help analysis, it was concluded that there is no significant difference in the odds of more new carries for newly formulated fluoride varnish and the standard varnish as p=0.38>0.05.  This means that the newly formulated fluoride varnish is not effective as its effect is not different from the standard varnish. The null and alternative hypothesis is given as The p-value for this coefficient is 0.007<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 sound teeth and type of varnish. There is no significant difference in the odds of more new carries for newly formulated fluoride varnish and the standard varnish as p=0.006<0.05.  The odds of new carries for newly formulated fluoride varnish are lower than that of standard varnish. This means that the newly formulated fluoride varnish is more effective than the standard varnish.

Conclusions

At this point, we conclude that a positive interrelationship association exists between the two variables. The SPSS assignment help expert can identify that the impact of blood lead levels on systolic blood pressure is greater for white, >=high school is lesser than for white, <high school.  The impact of blood lead level on systolic blood pressure is greater for black >=high school than black, <high school at lower levels of blood lead level but greater for black <high school than black >=high school at higher values of blood lead levels. Generally, the effect of blood lead levels on systolic blood pressure is greater for black than white.