(Basic Statistics for Citizen Data Scientist)
When you ask for a random set of say 100 numbers between 1 and 10, you are looking for a sample from a continuous uniform distribution, where α = 1 and β = 10 according to the following definition.
Definition 1: The continuous uniform distribution has probability density function (pdf) given by
where α and β are any parameters with α < β.
Observation: The corresponding cumulative distribution function (cdf) is
The inverse cumulative distribution function is
I(p) = α + p(β − α)
Other key statistical properties are:
- Mean = (α + β) / 2
- Median = (α + β) / 2
- Mode = any x, α ≤ x ≤ β
- Range = (-∞, ∞)
- Variance = (β – α)2 / 12
- Skewness = 0
- Kurtosis = -1.2
Real Statistics Functions: Excel doesn’t provide any functions for the uniform distribution. Instead you can use the following functions provided by the Real Statistics Resource Pack.
UNIFORM_DIST(x, α, β, cum) = the pdf of the continuous uniform distribution f(x) at x when cum = FALSE and the corresponding cumulative distribution function F(x) when cum = TRUE.
UNIFORM_INV(p, α, β) = x such that UNIFORM_DIST(x, α, β, TRUE) = p. Thus UNIFORM_INV is the inverse of the cumulative distribution version of UNIFORM_DIST.
Statistics for Beginners in Excel – Uniform Distribution
Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.