Chi -square statistic- New Jersey USA

Final Project. In this final assignment, we will revisit datasets that we have utilized in previous assignments, but with new objectives.  

• In the Week One assignment, you looked at mortality in your particular state, with two different metrics: the first was numbers of deaths, and the second was years of life lost. For this question, return to the original dataset, but this time first pool all cancer causes of death together, so that cancer constitutes the only category for cause of death. Then, repeat your analyses from Week One. How do your conclusions change? • In the Week Two assignment, you looked at sex ratios for births in your state.  

o Take the data you have assembled from the second part of your Week Two assignment, namely, numbers of first-born boy and girl births in your state between 2007 and 2012, separately by racial group (i.e., American Indians, Asians, Blacks, and Whites). Form a two-by-four contingency table from these data: the two row categories are female (girl) and male (boy), and the four column categories are the four racial groups. Calculate the chi-square statistic from this contingency table, and interpret the result. o Return to the CDC Wonder website, and obtain the numbers of births in your state between 2007 and 2012, by month. (Disregard gender, or race, or birth order—you want all births). Calculate a chi-square statistic to assess whether there is any seasonality to births. (Your null hypothesis is that births should be equally likely to occur in any of the 12 months. We are ignoring the varying lengths of the months to simplify calculations.) How would you interpret your findings?  Explain in 500 words in APA format supported by scholarly sources.  

BONUS: Give a graphical representation of your findings for this portion highlighting what you consider significant.   

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MHA610: Introduction to Biostatistics       COURSE GUIDE  

• In the Week Three assignment, you were given levels of tumor-associated antigens in a sample of 90 normal (non-cancer) individuals, and 160 hepatocellular carcinoma (HCC) patients. Here is a proposed diagnostic test for HCC: o For each individual, calculate a numerical score:  score = -3.95 + 10.7 * HCC1 - 4.14 * P16 + 13.95 * P53 + 28.92 * P90 + 6.48 * survivin   (This equation was derived from logistic regression.)  o If this score is positive (i.e., > 0), diagnose this individual as an HCC patient; if this score is negative (i.e., <0), diagnose this individual as normal (i.e., non-cancer). o Apply this rule to the entire cohort of 250 individuals. Report the sensitivity of this rule, the specificity, the false positive rate, the false negative rate, and the overall accuracy. Do you think the score function provides a good diagnostic test for HCC? Explain.  

• In the Week Four assignment, we considered a simple two-by-two crossover trial of a new experimental treatment for interstitial cystitis. We calculated t tests for carryover and treatment effects, but we have not yet considered period effects. It is unlikely that there are any period effects in this trial, but we may want to test this formally. If there were a period effect, then patient responses under either treatment would likely be systematically higher in one period than the other. (Here's an analogy: Think of taking the same test twice. You would likely perform better on the test the second time, since you have learned from your experience of taking the first test.) Explain how you would devise a t test for assessing a period effect in this trial. (Hint: look at the explanation of the t test for treatment effects given in the Week Four assignment. There, we based the test on the random variable X - Y. Suppose we look instead at X + Y?)   

• In the Week Five assignment, you investigated measures of brain size and intelligence in a sample of 20 youths. A potential shortcoming of your prior analyses is that you did not take into account all available information in the dataset, in particular, gender. Answer the following questions and explain your answers: o Do any of the physiologic variables CCSA, HC, TOTSA, TOTVOL, and WEIGHT differ significantly between males and females? o Do IQs differ significantly by gender? o Undertake a paired analysis of IQs, in order to assess whether firstborns have higher IQs than non-firstborns. In this regard, there are 10 pairs of related youths, as denoted by the variable   

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MHA610: Introduction to Biostatistics       COURSE GUIDE PAIR.  

Completing the Final Project The Final Project: 1. Must include a title page with the following: a. Title of paper b. Student’s name c. Course name and number d. Instructor’s name e. Date submitted 2. Must contain 5 sections, each starting on a new page; the section headings can be called Question 1, Question 2, Question 3, Question 4, Question 5 respectively. 3. Each section must have two subsections, with headings Results and Conclusions.  4. The Results subsections must include your analyses of that particular question. Your results may include figures, tables, and statistical analyses, laid out in a logical fashion.  5. The Conclusions subsections must contain your inferences relative to that question based on your results, and any discussion points you wish to raise.  6. Length of the Results subsections must vary by question, but should encompass all of your relevant analyses. 7. Length of the Conclusions subsections typically will not exceed one page.  8. If you have used any external references (e.g., the text), you should include a separate reference page, formatted according to APA style as outlined in the Ashford Writing Center.