A company executive claims that employees in his industry get 100 junk emails per day. To further investigate this claim, the tech department of the company conducts a study. The executive selects a random sample of 10 employees and records the number of junk emails they received that day. Here are the data: 125, 101, 109, 94, 122, 92, 119, 90, 118, 122. The tech department would like to determine if the data provide convincing evidence that the true mean number of junk emails received this day by employees of this company differ from 100. They decide to carry out a test of H0: μ = 100 versus Ha: μ ≠ 100, where μ = the true mean number of junk emails received this day by employees of this company. What conclusion should be made
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
A company executive claims that employees in his industry get 100 junk emails per day. To further investigate this claim, the tech department of the company conducts a study. The executive selects a random sample of 10 employees and records the number of junk emails they received that day. Here are the data: 125, 101, 109, 94, 122, 92, 119, 90, 118, 122. The tech department would like to determine if the data provide convincing evidence that the true mean number of junk emails received this day by employees of this company differ from 100. They decide to carry out a test of H0: μ = 100 versus Ha: μ ≠ 100, where μ = the true mean number of junk emails received this day by employees of this company. What conclusion should be made? Use .
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