Assignment: Computation by Hand Table 1: Survival Status Donor’s SexAliveDeadTotalFemale52456Male11027137Total16231193 Compute the simple odds ratio of the association of donor’s sex and survival status of the infant. Be sure to answer all four parts to this question (a, b, c, and d), including manual calculation of the chi-square value. (40 Points) Manually calculate a simple odds ratio to test the hypothesis of no association between donor’s sex and the survival status of the infant, without the inclusion of the variable severity using a 2 x 2 table for sex and survival (10 Points). Manually calculate the confidence interval associated with that odds ratio using the appropriate formula (10 Points). Manually compute the Chi Square test statistic for this table (10 Points). Interpret the results. Include an interpretation of the odds ratio, the confidence interval, and the Chi Square test statistic in your response (10 Points). Table 2: Survival Status Disease SeverityDonor’s SexAliveDeadTotalNoneFemale14115 Male21223MildFemale17118 Male40242ModerateFemale15116 Male33639SevereFemale617 Male161733Total 16231193 Using data in table 2, compute the common odds ratio of the association between donor’s sex and the survival status of the infant, after controlling for severity (30 Points). Manually calculate a common odds ratio to test the hypothesis of no association between donor’s sex and the survival status of the infant, after the inclusion of the variable severity using the common odds ratio (10 Points). Interpret the results. How does the common odds ratio differ from the simple odds ratio computed in part 1? What effect might it have on your decision from part 1 to reject or fail to reject the null hypothesis? (10 Points). Why is it important to know the effect of severity on the association of gender and survival? (10 Points) Perform a simple logistic regression using SPSS and the Week 6 Dataset (SPSS document). Answer the following questions based on your SPSS output (30 Points) Are the results of the simple logistic regression similar to or different from the results of the simple odds ratio (10 Points)? How are they similar or different? Include output from SPSS and an interpretation of the OR and confidence intervals in your response (10 Points). What can you do using logistic regression to duplicate the results from part 2 of this application (the use of CMH for common odds) (10 Points)?

Suppose that there are two (2) candidates (i.e., Jones and Johns) in the upcoming presidential election.  Sara notes that she has discussed the presidential election candidates with 15 friends, and 10 said that they are voting for candidate Jones. Sara is therefore convinced that candidate Jones will win the election because Jones gets more than 50% of votes. Answer the following questions in the space provided below: 1.  Based on what you now know about statistical inference, is Sara’s conclusion a logical conclusion? Why or why not? 2.  How many friend samples Sara should have in order to draw the conclusion with 95% confidence interval? Why? 3.  How would you explain your conclusion to Sara without using any statistical jargon? Why?

Attached Files: Data MIning Assignment_with rubric                   (181.304 KB)  usoccupations.xlsx               (621.785 KB)     uscars.xlsx               (31.055 KB)  uspopulation.xlsx               (142.184 KB)  This assignment provides you with practice using R for data mining techniques. You will use R to classify and cluster dataset to show how data mining methods can be used to classify and cluster data.  Before beginning this assignment, review the learning resources for this module, especially Introduction to Data Mining with R from R DataMining.com, reviewing the steps taken to classify and cluster the iris data set in R.  The purpose of clustering is to form new classification from numerical variables. Therefore, it is important that you remove original classification from the data set prior to conducting clustering. For example, the species variable needs to be removed from the Iris data set because species is a classification. You may then merge back the original specifies variable and compare the newly formed clusters against the original classification to see how they differ.  Complete the following steps and write a report to record your work, results and analysis.  Install and load the *factoextra and **NbClust packages.  Select an appropriate data set in R or the MASS library and use the sample(), ctree() and predict() functions to build a decision tree and plot it. You may also use one of the data sets (usoccupations, uscars, uspopulation) attached to this module for this assignment (you may import directly or convert to CSV first). Determine the appropriate number of clusters and produce a k-means cluster. Explain your findings.  Produce a density-based cluster with DBSCAN or use logistic regression to construct a binary classification and explain your findings.  *The factoextra package is used to determine the optimal number clusters for a given clustering methods and for data visualization  **The NcClust package provides 30 indices for determining the relevant number of clusters and the best clustering scheme from the different results obtained by varying all combinations of number of clusters, distance measures, and clustering methods. It can simultaneously compute all the indices and determine the number of clusters in a single function call.  Report  Your assignment/project should have a good cover/title page, introduction of what the goals of the project and the methods you use. It also should follow APA format with at least 1000 words (excluding title page and references page) and references page. In the body of your project you should incorporate the R codes and R outputs with interpretation of your results. Be sure to show all the elements in the official hypothesis, including the null and alternative hypothesis, the critical values, calculation of the test statistics and p-values. Finally, you need to make sense of your results to make good points with proper conclusions, to show your understanding of the course material and its application to the dataset.  Graphs, figures, charts, tables are very useful to increase visual effects to impress your readers. You also should do your best to give insight and understanding to the project with a good conclusion. Please use subtitles to make your assignment more reader friendly as well.