Is the distribution of marital categories in the GSS representative of their distributions in the population? Let’s assume that in the U.S.: married = 48%, widowed = 6%, divorced = 12%, separated = 2%, never married = 32%. In the 2018 GSS, MARITAL is coded: 1=married, 2=widowed, 3=divorced, 4=separated, and 5=never married. Interpret the output and provide the output from
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
- Is the distribution of marital categories in the GSS representative of their distributions in the population? Let’s assume that in the U.S.: married = 48%, widowed = 6%, divorced = 12%, separated = 2%, never married = 32%. In the 2018 GSS, MARITAL is coded: 1=married, 2=widowed, 3=divorced, 4=separated, and 5=never married. Interpret the output and provide the output from this test as a part of your answer. Discuss your interpretation of the results using APA format.
1.) Assumption?
2.) Level of measurement?
3.) Null hypothesis?
4.) Research hypothesis?
5.) Select the sampling distribution and establish the critical region?
6.) Compute the test statistics?
7.) Interpret the results using APA format?
*Use the graphs inserted below in images.
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