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Parametric and Nonparametric Test Analysis

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Label each with either P or NP for each example. Either Parametric or Nonparametric.

1. In a comparison of two towns, is the average height of its residents the same?

2. A manufacturer produces a batch of memory chips (RAM) and measures the mean-time-between-failures (MTBF). The manufacturer then changes a manufacturing process and produces another bath and again measures the MTBF. Did the change to the process improve the MTBF?

3. The average life span of a dog is proportional to the amount of calcium consumed?

4. The correlation between gene disorders and certain diseases.

5. From a written survey where the respondents were asked to rate an individual on a scale of 1 to 5, one group rated an individual a . 3.7, another group rated the individual a 4.3. Is the difference statistically significant?

6. A study of vehicle accidents on a military installation compared to drivers' rank.

7. Show that the numbers drawn in a state lottery are truly random.

8. There is a direct correlation to a student's grade and the student's rating of an instructor.

So each answer should be 'P' or 'NP'...

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Solution Summary

Parametric and nonparametric tests are analyzed in this question. Why these hypothesis tests are used are explained.

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In statistical terms a parameter is a statistic calculated from a set of data, e.g. a mean or a standard deviation are both parameters. In order to calculate parameters, and to use them in statistical analysis three assumptions must be tested against the data. The data is considered as parametric if these three assumptions are valid:

1. Can it be assumed that the data has been obtained from a population which is considered "normal" in the statistical sense. In other words if a sufficiently large sample of data was obtained from the population is it likely that it would be normally distributed. This may be known from similar research, but if not, and provided the sample is reasonably large, it ...

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  • BSc , Wuhan Univ. China
  • MA, Shandong Univ.
Recent Feedback
  • "Your solution, looks excellent. I recognize things from previous chapters. I have seen the standard deviation formula you used to get 5.154. I do understand the Central Limit Theorem needs the sample size (n) to be greater than 30, we have 100. I do understand the sample mean(s) of the population will follow a normal distribution, and that CLT states the sample mean of population is the population (mean), we have 143.74. But when and WHY do we use the standard deviation formula where you got 5.154. WHEN & Why use standard deviation of the sample mean. I don't understand, why don't we simply use the "100" I understand that standard deviation is the square root of variance. I do understand that the variance is the square of the differences of each sample data value minus the mean. But somehow, why not use 100, why use standard deviation of sample mean? Please help explain."
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