Chapter 6 explores confidence intervals using both the z-distribution and the t-distribution. We know that we use the z-distribution when we have the population standard deviation O, and we use tl t-distribution when we do not have the population standard deviation (but we can calculate the sample standard deviation, S). As the sample size increases, it becomes closer to the population size (mak sense, right?). This means that the t-distribution will become closer to the z-distribution (because the sample standard deviation will become closer to the population standard deviation). Since it is very rare to actually have the population's standard deviation, we typically have to use the t-distribution when creating confidence intervals. Answer the following questions for this discussion: h. Construct a 95% confidence interval for the population mean given that X=25, n=51, ands=5. Write a conclusion for this interval (what does it represent?). 2. Suppose students are creating a confidence interval for the population mean using the same statistics from question 1. Erin is using a 90% confidence level and Robert is using a 99% confidence level. Who will create wider interval? Explain why without actually performing the calculations.

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Contingency Table

A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.

Binomial Distribution

Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.