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GXWeb: What are the Chances?
Saltire Software, home of Geometry Expressions and GXWeb
Symbolic computations on this page use the Nerdamer Symbolic JavaScript to complement the in-built computer algebra system of GXWeb.
Learn How: Create Your Own GXWeb Tasks (like this one!)
Learn How 2: Take GXWeb to the Next Level
A great introduction to probability distributions
Suppose you draw a random sample and measure the heights of the subjects. As you measure heights, you can create a distribution of heights. This type of distribution is useful when you need to know which outcomes are most likely, the spread of potential values, and the likelihood of different results.
This GXWeb-based utility offers tools to explore probability distributions and hypothesis tesing.
Probabilities for continuous distributions (like heights, IQ scores, even class test results - for a large enough group!) are measured over ranges of values rather than single points. A probability indicates the likelihood that a value will fall within an interval. On a probability plot, the entire area under the distribution curve equals 1.
The most well-known continuous distribution is the normal distribution, which is also known as the Gaussian distribution or the bell curve. This symmetric distribution fits a wide variety of phenomena, such as human height and IQ scores. It has two parameters - the mean and the standard deviation. The lognormal distribution is another common continuous distribution which can fit skewed data.
Distribution parameters are values that apply to entire populations. Unfortunately, population parameters are generally unknown because it is usually impossible to measure an entire population. However, you can use random samples to calculate estimates of these parameters.
The Bell-shaped Curve and Other Probability Distributions
Hypothesis Testing
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