The frequency distribution shows the results of 200 test scores. Are the test scores normally distributed? Use a= 0.10. Complete parts (a) through (d). Class boundaries 49.5-58.5 58.5-67.5 67.5-76.5 76.5-85.5 85.5-94.5 Frequency, f 19 61 81 35 4 Using a chi-square goodness-of-fit test, you can decide, with some degree of certainty, whether a variable is normally distributed. In all chi-square tests for normality, the null and alternative hypotheses are as follows. Ho: The test scores have a normal distribution. H.: The test scores do not have a normal distribution, a. Find the expected frequencies. Class boundaries Frequency, f Expected frequency 49.5-58.5 58.5-67.5 85.5-94.5 67.5-76.5 81 76.5-85.5 19 61 35 4 (Round to the nearest integer as needed.)
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.
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