STAT1132-Statistics

Part A 

Oral Health As part of the National Health and Nutrition Examination Survey run by the American Centers for Disease Control and Prevention, a public health study into oral health was conducted. A sample of 8,027 adults was selected and underwent dental examinations. Among other information, the gender and ‘periodontal status’ of each individual was recorded. The ‘periodontal status’ was recorded as either “healthy”, “gingivitis” or “perio”. Assume that the sample may be considered as a random sample of the adult American population. Use a chi-squared test to determine if the distribution pattern of periodontal status in males is different from that in females (in the population). The data frame of 8,027 subjects is available in the text file Oral.Health.dat.

Answer all questions

1. Describe the variable(s) relevant to this analysis. 

2. Which sub-class of goodness of fit tests is appropriate for this analysis? 

3. Summarize the data using relevant tables, graphs and plots (such as the contingency table, the row and/or column percentage tables, etc) as shown in the Subject Reader. (Hint: Table or figure caption must be informative.

4. State and assess the model assumption(s) [ Marks] 5. State the null hypothesis

6. Perform the chi-square test and provide the R output. 

7. Quote the chi-square test results and interpret the results. 

8. Give your conclusion. 

Part B –Age and Depression

Consider the Depression Study presented in Data Set A.25 in Appendix A of the Subject Reader. Assume that the sample may be considered as a random sample of all depression patients in Australia. Use a chi-squared test to determine if the proportions of depression patients in the three age groups (in the population) are the same. The dataframe is available in the text file Depression.Study.dat.

Answer all questions.

1. Describe the variable(s) relevant to this analysis. 

2. Which sub-class of goodness of fit tests is appropriate for this analysis? 

3. Summarize the data using relevant tables, graphs and plots generated by R. 

4. State and assess the model assumption(s)

5. State the null hypothesis 

6. Perform the chi-square test and provide the R output.

7. Quote the chi-square test results and interpret the results.

8. Give your conclusion.