Model Validation, Selection, and Assessment
Data Set Splits in a Statistical Learning Procedure Training Set The subset of data used to fit the model –
S_Foundation, YES
YES
S_Foundation, YES
Descriptive Statistics : a glimpse
Notation Numeric / Count Data Central Tendency Mean (Arithmetic Mean) The arithmetic mean is the sum of all observed values
S_Foundation, YES
CRAFT – Art & Science of Visualization
A platform-independent thinking discipline for building visualizations. The Idea A visualization isn’t produced by picking a chart type. It is
S_Foundation, YES
The Story Behind DATA
A conceptual framework — from origin to value 1. Origin & Scope — Who Had Data First Data creation did
S_Foundation, YES
The Evolution of Statistics: an overview
A conceptual framework — from aristocratic administration to personal data A note on names: statistics was built by many hundreds
Distributions, YES
Probability Essentials
Introduction Probability allows us to quantify the variability in the outcome of any experiment whose exact outcome cannot be predicted
Distributions, YES
Transformation of Random Variables
Introduction In this note, we study how to find the probability distribution of functions of one or more random variables.
Distributions, YES
The Role of Parameters
Introduction In one of the previous notes, we discussed several common families of distributions. In this note, we discuss the
Distributions, YES
Expectation and Moments of Univariate Distributions
Introduction Random variable characterises a random phenomenon by listing the range and the corresponding probability distribution (that is, pmf in
Distributions, YES
Generating Functions in Random Variables
Introduction A random variable X is completly described by its CDF, PDF, or PMF. But summarizing a distribution would be