Recall, from Module 1, numerical data is said to be quantitative. Examples of quantitative data are the weight of an adult, the number of home runs, the price of a new car, and the speed of a runner. Data that is a category and not necessarily numerical is said to be qualitative. Examples of qualitative data are hair color, human blood type, gender, nationality, and hurricane category.Image of a group of word bubbles(1) Collect 50 or more qualitative data items. Use the same method of collecting 50 or more data items that you used in the Module 1 discussion. Then, construct a 90% confidence interval for the population proportion, p.View an example( Attached) on how to use StatCrunch (with data) to compute confidence intervals for a population proportion.Then, answer the following:Briefly describe your data setInclude your Statcrunch report, either as an attachment or (even better) paste is into your discussion postWrite down your sample size.From your data, what is the point estimate, p̂ of the population proportion?Write down the confidence interval that you obtained. Interpret the result.What is the margin of error?Using the same data, construct a 98% confidence interval for the population proportion. Then, answer the following three questions:(i) What happens to the length of the interval as the confidence level is increased?(ii) How has the margin of error changed?(iii) If the sample size is increased, what do you think will happen to the margin of error? Why?
