Discuss about the quantitative reasoning and analysis

Reference no: EM131924551

Question: In the prerequisite course, Quantitative Reasoning and Analysis, you constructed basic contingency (crosstab) tables. You might be surprised to learn that you can estimate a simple logistic regression model, with a categorical predictor, using the descriptive values presented in the crosstab table.

In this assignment, you use Microsoft Excel to construct a specialized tool that creates basic logistic regression models given a crosstab/contingency table. As if that were not useful enough, this Excel tool is not specialized-you can use it given any crosstab/contingency tables you encounter in research. In the field of statistical research, this is just about as exciting as you can get!

To prepare: • Review the sections in the Osborne text that present a template for constructing an Excel worksheet.

• Review the video in the Learning Resources, in which Dr. Matt Jones explains how to harness the power of Excel using contingency tables.

• Think about the types of variables that are useful for cross tab tables.

Using one of the datasets provided, select two variables that allow you to construct a 2×2 contingency table. Use SPSS to run the initial crosstab table, using any two variables that you think are appropriate. Then, use Excel to construct a table in which you report:

• Conditional probabilities

• Conditional odds

• Logits

• Odds ratios

• Relative risk

• Slope

Be sure to apply the template from the Osborne text. Note that page 42 has a completed example that should help you determine these values. Be sure to use formulas and cell references in Excel so that the spreadsheet you create can be used as a tool for calculating similar values for other datasets.

Once you have created the tool, write a 1- to 2-paragraph summary in APA format interpreting your results. Submit both your Excel file and your summary to complete this assignment.

Chapter 1: A Conceptual Introduction to Bivariate Logistic Regression

Chapter 2: How Does Logistics Regression Handle a Binary Dependent Variable?

Reference no: EM131924551

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