Competency Formulate and evaluate hypothesis tests for population parameters based on sample statistics using both Critical Regions and P-Values, and be able to state results in a non-technical way that can be understood by consumers of the data instead of statisticians. Dealing with Two Populations Inferential statistics involves forming conclusions about a population parameter. We do so by constructing confidence intervals and testing claims about a population mean and other statistics. Typically, these methods deal with a sample from one population. We can extend the methods to situations involving two populations (and there are many such applications). This deliverable looks at two scenarios. Concept being Studied Your focus is on hypothesis tests and confidence intervals for two populations using two samples, some of which are independent and some of which are dependent. These concepts are an extension of hypothesis testing and confidence intervals which use statistics from one sample to make conclusions about population parameters. What to Submit Your research and analysis should be presented on the spreadsheet provided below.

Weight IAT Variables Row Variable: BIRTH-GENDER Column Variable: Prefers StatCrunch to produce a two-way contingency table.Use the row variable  and the column variable listed above for your IAT data set.  Copy the table in your StatCrunch output window and paste it in the textbox with your response. After you paste the content of your StatCrunch output window into the textbox, keep the table titled Contingency table results, and delete the Chi-Square resultstable. VERY IMPORTANT: Perform each of the following to make your table more meaningful to the reader (and me, the grader). If your table is not meaningful, I cannot check (and therefore grade) your work.  Above your contingency table, provide a meaningful title that includes the name of your IAT Data set. The row and column labels in the contingency table are numbers. This makes the two-way table hard to read. Use the variable descriptions for your IAT data set to relabel the column and rows with meaningful words. (The variable description link for your IAT data set is provided above in the Variables section.)  Below the table, include the name of your row variable and the name of your column variable. Weight IAT: What percentage of IAT participants were born female? Weight IAT: What percentage of the IAT participants indicated that they moderately or strongly prefer thin people? Weight IAT: What percentage of born-males moderately or strongly prefer fat people? Weight IAT: What percentage of participants who moderately prefer thin people were born female? Weight IAT: Are born-male participants more likely to indicate that they strongly prefer thin people In the Variables section above, use the variable descriptions link to peruse the variables in your chosen IAT data set. Does your unique data set contain any continuous random variables? If so, give an example and explain why the variable is a continuous random variable. If not, explain why not? In the Variables section above, use the variable descriptions link to peruse the variables in your chosen IAT data set. Does your unique data set contain any discrete random variables? If so, give an example and explain why the variable is a discrete random variable. If not, explain why not?