Here is a data set: 61100 51500 47100 32000 41200 69200 55900 50700 50300 62100 56900 45700 31000 48400 57700 34600 47000 53700 29100 24600 29400 48400 55800 46400 48200 46400 34100 53800 The goal is to construct a grouped frequency distribution table (GFDT) for this data set. The GFDT should have 10 classes with a "nice" class width. Each class should contain its lower class limit, and the lower class limits should all be multiples of the class width. This problem is to determine what the class width and the first lower class limit should be. What is the best class width for this data set? optimal class width = What should be the first lower class limit? 1st lower class limit =
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.
Here is a data set:
| 61100 | 51500 | 47100 | 32000 |
| 41200 | 69200 | 55900 | 50700 |
| 50300 | 62100 | 56900 | 45700 |
| 31000 | 48400 | 57700 | 34600 |
| 47000 | 53700 | 29100 | 24600 |
| 29400 | 48400 | 55800 | 46400 |
| 48200 | 46400 | 34100 | 53800 |
The goal is to construct a grouped frequency distribution table (GFDT) for this data set. The GFDT should have 10 classes with a "nice" class width. Each class should contain its lower class limit, and the lower class limits should all be multiples of the class width.
This problem is to determine what the class width and the first lower class limit should be.
What is the best class width for this data set?
optimal class width =
What should be the first lower class limit?
1st lower class limit =
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