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For this Assignment, you use multiple logistic regression to analyze a dataset. You identify assumptions required by multiple logistic regression and evaluate whether they have been met by the data. Finally, you interpret your results and evaluate the use of multiple logistic regression.
The Assignment
Use the Week 7 Dataset (SPSS document) from the Learning Resources area to complete this assignment.
Variables and variable selection (20 Points)Use a table to list the variables, Sex, Age in Years, Serum Cholesterol, Obese, and Hypertension, and each of their levels of measurement. (10 Points)
Create new variables Age_Cat and Chole_Cat:
Age_Cat: Convert Age in Years into a categorical variable with 2 categories, Less than 40, 40 and greater
Chole_Cat: Convert Serum Cholesterol into 3 categories, Under 200, 200-299, and 300 and greater

Add the new variables to each record by coding the responses to the original variable using the assigned categories. Be sure that the variable view in SPSS has the correct information on the 2 new variables. (10 Points)
Simple Binary Logistic Regression (30 Points)
Use Hypertension as the dependent variable and Chole_Cat as the independent variable in the first model. Report the Odds Ratio and significance of the Odds Ratio for the relationship between the dependent and independent variables. (10 Points)
Use Hypertension as the dependent variable and Serum Cholesterol (the original variable) as the independent variable in the second model. Report the Odds Ratio and significance of the Odds Ratio for the relationship between the dependent and independent variables. (10 Points)
How does the level of measurement for the independent variable affect the outcome (include the OR and its significance in your response)? How does the level of measurement of the independent variable change your interpretation of the Odds Ratio? (10 Points)
Multivariate Logistic Regression (50 Points)
Run a multivariate binary logistic regression model using SPSS and Hypertension as the dependent variable, Chole_Cat, Age_Cat, Obese, and Sex as the Covariates. Include the output in your submission. (10 Points)
Identify the Odds Ratio and the significance of the Odds Ratio for each of the covariates. How has the relationship between Chole_Cat and Hypertension changed with the addition of the other variables (compare to the output from # 2a)? (15 Points)
Test the assumption that the model fits the data using the Hosmer-Lemeshow Goodness of Fit test. Interpret the Chi Square statistic given in the output of this test and state what it means in terms of the assumptions needed to use logistic regression with this data. (10 Points)
Rerun the logistic regression model from #3a and use the save function to create the following new variables: Predicted Probabilities, Deviance Residuals, and Cook’s Distance. Evaluate the model using these saved variables and the following Scatter Plots. (15 Points)
Create a Scatter Plot of the Deviance Residuals (DEV) and the variable ID: Are there any outliers? What does this mean when evaluating your model?
Create a Scatter Plot of Cook’s Distance (COO) and the variable ID: Are there any influential cases? What does this mean when evaluating your model?
Create a Scatter Plot of Deviance (DEV) and the Predicted Probabilities (PRE). Discuss whether anything in this scatterplot could cause you some concern in terms of your model.
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## why is quantitative research so important to the social sciences? How do statistics inform our ideas on human behavior?

why is quantitative research so important to the social sciences? How do statistics inform our ideas on human behavior? does it look like the variables marital status and general happiness have a relationship? What stands out to you?[supanova_question]

## Module 11 – Treating Depression Lab Part 2 (groupwork 3)

Context
Clinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who have an episode of major depression will have a recurrence within 2-3 years. Being able to prevent the recurrence of depression in people who are at risk for the disease would go a long way to alleviate the pain and suffering of patients. During the 1980s the federal government, through the National Institutes of Health (NIH), sponsored a large clinical trial to evaluate two drugs for depression. There were 3 treatment groups. Patients received either Imipramine (Imip), Lithium (Li), or a Placebo (Pl). Researchers randomly assigned patients to one of the 3 treatment groups and followed them for 2-4 years to track any recurrences of depression. (Prien et al., Archives of General Psychiatry, 1984).
VariablesHospt: Which hospital the patient was from Labeled 1, 2, 3, 5 or 6
Treat: 0=Lithium; 1=Imipramine; 2=Placebo
Outcome: 0=Success 1=Failure (recurrence of depression)
Time: Number of weeks until a recurrence (if outcome=1) or until the study ended (if outcome=0)
AcuteT: How long the patient was depressed before the start of the current study, measured in days
Age: Age in years
Gender: 1=Female 2=Male
DataIf you have not already done so, open the depression data set in the Stats at Cuyamaca Collegegroup on StatCrunch (directions – opens in a new tab)
https://www.statcrunch.com/app/index.php?dataid=35…
We will analyze the data to answer the second research question: Which of the drugs (if either) delayed the recurrence of depression longer relative to the placebo?
In the previous lab-preparation activity, we identified Treat as the explanatory variable and Time as the response variable. We also determined that we will analyze the data using side-by-side boxplots and descriptive statistics (i.e. 5-number summaries since the graphs are boxplots).
Make graphs and tables.Use StatCrunch to produce side-by-side boxplots. (directions) Embed your graphs into the textbox, and be sure to include the Alt Text. To recall how to embed a picture into a textbox, see the StatCrunch directions below.
Use StatCrunch to produce the descriptive statistics (a single table containing the 5-number summaries for each comparison group). (directions) Copy and paste the StatCrunch output table into the textbox.

Analyze the data: Compare the distributions for the treatment groups as demonstrated in Unit 2. For example, compare medians and intervals of typical values. Describe the shape and any outliers. Be sure to write your comparisons so the reader can understand the context of the numbers. For example, don’t just say the median is 30; instead, say something like this: on average patients taking the placebo relapsed in 30 days (Q2=30 days).
Draw a conclusion: What can we conclude from your analysis? Did one drug successfully delay relapse of depression better than the others? What evidence supports your conclusion?
Summarize your conclusions in response to both research questions: In this lab you compared three treatments (two drugs and the placebo) using two different variables. In Part 1 you compared whether or not a relapse into depression occurred for each of the two drugs and the placebo. In Part 2 you compared the length of time until the next relapse for the two drugs and the placebo. What can you conclude in light of both analyses? Is one treatment better than the other? How does the data support your conclusion?

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## validity

Locate an example of test bias (any type of test) from the trade literature or the popular press (e.g., online newspaper or magazine). Describe the main points in the example, followed by your own opinion about this issue. Be sure to include a discussion of what can be done to minimize test bias in this specific situation. Include a copy of the article at the end of your paper.[supanova_question]

https://anyessayhelp.com/
Context
Clinical depression is a recurrent illness requiring treatment and often hospitalization. Nearly 50% of people who have an episode of major depression will have a recurrence within 2-3 years. Being able to prevent the recurrence of depression in people who are at risk for the disease would go a long way to alleviate the pain and suffering of patients. During the 1980s the federal government, through the National Institutes of Health (NIH), sponsored a large clinical trial to evaluate two drugs for depression. There were 3 treatment groups. Patients received either Imipramine (Imip), Lithium (Li), or a Placebo (Pl). Researchers randomly assigned patients to one of the 3 treatment groups and followed them for 2-4 years to track any recurrences of depression. (Prien et al., Archives of General Psychiatry, 1984).
VariablesHospt: Which hospital the patient was from Labeled 1, 2, 3, 5 or 6
Treat: 0=Lithium; 1=Imipramine; 2=Placebo
Outcome: 0=Success 1=Failure (recurrence of depression)
Time: Number of weeks until a recurrence (if outcome=1) or until the study ended (if outcome=0)
AcuteT: How long the patient was depressed before the start of the current study, measured in days
Age: Age in years
Gender: 1=Female 2=Male
DataIf you have not already done so, open the depression data set in the Stats at Cuyamaca Collegegroup on StatCrunch (directions – opens in a new tab)
https://www.statcrunch.com/app/index.php?dataid=35…
We will analyze the data to answer the second research question: Which of the drugs (if either) delayed the recurrence of depression longer relative to the placebo?
In the previous lab-preparation activity, we identified Treat as the explanatory variable and Time as the response variable. We also determined that we will analyze the data using side-by-side boxplots and descriptive statistics (i.e. 5-number summaries since the graphs are boxplots).
Make graphs and tables.Use StatCrunch to produce side-by-side boxplots. (directions) Embed your graphs into the textbox, and be sure to include the Alt Text. To recall how to embed a picture into a textbox, see the StatCrunch directions below.
Use StatCrunch to produce the descriptive statistics (a single table containing the 5-number summaries for each comparison group). (directions) Copy and paste the StatCrunch output table into the textbox.

Analyze the data: Compare the distributions for the treatment groups as demonstrated in Unit 2. For example, compare medians and intervals of typical values. Describe the shape and any outliers. Be sure to write your comparisons so the reader can understand the context of the numbers. For example, don’t just say the median is 30; instead, say something like this: on average patients taking the placebo relapsed in 30 days (Q2=30 days).
Draw a conclusion: What can we conclude from your analysis? Did one drug successfully delay relapse of depression better than the others? What evidence supports your conclusion?
Summarize your conclusions in response to both research questions: In this lab you compared three treatments (two drugs and the placebo) using two different variables. In Part 1 you compared whether or not a relapse into depression occurred for each of the two drugs and the placebo. In Part 2 you compared the length of time until the next relapse for the two drugs and the placebo. What can you conclude in light of both analyses? Is one treatment better than the other? How does the data support your conclusion?

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few seconds ago[supanova_question]