See attached files.
Using the methods in Section 8.4, test the hypothesis (± = 0.05) that the population proportions of red and brown are equal (pred = pbrown). You are testing if their proportions are equal to one another, NOT if they are equal to one another AND equal to 13%. NOTE: These are NOT independent samples, but we will use this approach anyway to practice the method. This also means that n1 and n2 will both be the total number of candies in all the bags. The 'x' values for red and brown are the counts of each we found on the Data page. You will need to calculate the weighted p:
Be sure to state clear hypotheses, test statistic, critical value or p-value, decision (reject/fail to reject), and conclusion in English. Submit your answer as a Word, Excel, .rtf or .pdf format and place in the dropbox.
You can use StatCrunch or the TI to help with this test. Needed information for both tools include:
x1 = number of red
n1 = total number of candies
x2 = number of brown
n2 = total number of candies
For the TI, you will want 2-PropZTest. Then select the appropriate alternative (not equal), and Calculate then enter. The output will have the test statistic (z), p-value (p), sample p values, weighted p (), then repeat of sample sizes.
For StatCrunch, you will select Stat > Proportions > Two Sample > with summary. The output will contain the test statistic (Z-Stat) and p-value.
Additional help is available in the Online Math Workshop under MAT300 Archived Workshop. Specifically Two Sample Inferences and Using Technology - Two Sample.
At the end of this project, you will be writing a report, explaining the method and presenting the results from each part of the project. You might find it useful to write this as you complete the work, so the report will be mostly written by the time it is assigned.
The solution provides step by step method for the calculation of testing of hypothesis. Formula for the calculation and Interpretations of the results are also included. Interactive excel sheet is included. The user can edit the inputs and obtain the complete results for a new set of data.