Parametric and nonparametric statistics.
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Parametric and nonparametric statistics are explained.
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parametric and nonparametric statistics.
Explain the meaning of parametric and nonparametric statistics. Please provide real life examples.
Parametric test
The parametric test is an underlying distribution and a sample statistic is obtained to estimate the population parameter. Because this estimation process involves a sample, a sampling distribution, and a population, certain assumptions are required to ensure that all components are compatible with each other. Also know as classical or standard test, these are statistical tests which make certain assumptions about the parameters of the full population from which the sample is taken. Some statistical tests require us to be aware of and sometimes correct for inherent characteristics or parameters.
For example
Problem:
Students at the University of Arkansas randomly selected 217 student cars and found that they had ages with a mean of 7.89 years and a standard deviation of 3.67 years. They also randomly selected 152 faculty cars and found that they had ages with a mean of 5.99 years and a standard deviation of 3.65 years. Use a 0.01 significance level to test the claim that student cars are older than faculty cars. What is the critical value (3 places of significance) and t score (2 places of significance)? Are student cars older (Yes or No)?
Solution:
The information given in the problem can be represented with the following notations:
Student Cars Faculty Cars
Sample Size or No. of cars = 217
= 152
Sample Mean (mean ages) = 7.89
= 5.99
Sample Standard deviation = 3.67
= 3.65
Claim: Student cars are older than faculty cars.
Hypotheses:
Null Hypothesis:
H0:
That is, H0: Student cars ...
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