Business Analytics & Statistics

Looking at past performance and how it can be applied to business decisions.

Using data to predict what will happen in the future.

Recommends the best course of action based on data, using AI, machine learning, and algorithms to optimize decisions.

One that is derived from counting something.

Based on a continuous scale of measurement.

Measures the dispersion of a dataset, calculated as the square deviation from the mean and measured in square units.

The square root of the variance, measured in the units of the dataset; measures the spread of the dataset around the mean.

Approximately 68% of the data falls within one standard deviation of the mean in a normal distribution.

Approximately 95% of the data falls within two standard deviations of the mean in a normal distribution.

Approximately 99.7% of the data falls within three standard deviations of the mean in a normal distribution.

A formula (1-1/k^2) determining the minimum proportion of data within k standard deviations of the mean.

Provides a relative measure of dispersion relative to the mean; measured by standard deviation divided by the mean.

Measures the association between two variables by analyzing their linear associations.

Measures the strength and direction of the linear relationship between two variables; derived from covariance.

The likelihood of one or more events occurring simultaneously.

The probability of one event occurring, given that another event has already occurred.

Models the probability of a given number of events occurring in a fixed interval of time or space.

Rejecting the null hypothesis when it is actually true.

Failing to reject the null hypothesis when it is actually false.

Process of discovering patterns, trends, and useful insights from large datasets using statistical, machine learning, and database techniques to make data-driven decisions.