Statistical data types | Categorical data
In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property. In computer science and some branches of mathematics, categorical variables are referred to as enumerations or enumerated types. Commonly (though not in this article), each of the possible values of a categorical variable is referred to as a level. The probability distribution associated with a random categorical variable is called a categorical distribution. Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. More specifically, categorical data may derive from observations made of qualitative data that are summarised as counts or cross tabulations, or from observations of quantitative data grouped within given intervals. Often, purely categorical data are summarised in the form of a contingency table. However, particularly when considering data analysis, it is common to use the term "categorical data" to apply to data sets that, while containing some categorical variables, may also contain non-categorical variables. A categorical variable that can take on exactly two values is termed a binary variable or a dichotomous variable; an important special case is the Bernoulli variable. Categorical variables with more than two possible values are called polytomous variables; categorical variables are often assumed to be polytomous unless otherwise specified. Discretization is treating continuous data as if it were categorical. Dichotomization is treating continuous data or polytomous variables as if they were binary variables. Regression analysis often treats category membership with one or more quantitative dummy variables. (Wikipedia).
From playlist STAT 200 Lectures (OER)
1.9 factors and categorical variables in R | statistical analysis and data science course in Rstudio
In this chapter of the video series in the crash course in statistics and data science with R / Rstudio we will see the definition, creation, and importance of using factors in R. We discuss among others: - Why use factors in our analysis and models ? - nominals and categorical variabl
From playlist R Tutorial | Rstudio
Discovering Variables – Combining Numbers for More Powerful Statistics (1-4)
Combining numbers creates variables – values that can vary or take on more than one value. If a value can be measured among a group and that value will be different for at least some of the group members, then you are measuring a variable. You will learn about qualitative (categorical) and
From playlist WK1 Numbers and Variables - Online Statistics for the Flipped Classroom
IAML2.7: Categorical (nominal) attributes
From playlist Thinking about Data
VARIABLES in Statistical Research (2-1)
A variable is any characteristic that can vary. An organized collection of numbers can be a variable. Qualitative variables indicate an attribute or belongingness to a category. Dichotomous variables are discrete variables that can have two and only two values. Quantitative variables indic
From playlist Forming Variables for Statistics & Statistical Software (WK 2 - QBA 237)
Brief overview of several common variables in statistics and research: qualitative, quantitative, control, discrete, continuous, categorical and ordinal.
From playlist Types of Variables
R Programming: Introduction: Factors (R Intro-04)
[my R script is here https://github.com/bionicturtle/youtube/tree/master/r-intro] Factors are categorical vectors. Specifically, they are (integer) vectors that store categorical values, or ordinal values. Ordinal values are *ranked* categories (but they are not intervals).Factors can only
From playlist R Programming: Intro
Intro to a Variable as a Changing Value or Placeholder
This video defines a variable and provides examples of a variable used as a changing value or a placeholder http://mathispower4u.com
From playlist Algebraic Structures Module
Analysis of covariance using Python
This is the third video lecture in my seminar series on linear models. Here, I discuss analysis of covariance (ANCOVA). We combine what we have learned about linear regression and analysis of variance. In ANCOVA we have a categorical variable as independent variable and a continuous numer
From playlist Statistics
Descriptive Statistics for Categorical Data - Statistics with SPSS 27 for Beginners (4 of 8)
Dr. Daniel, Diva, and Desi explain categorical variables and show you how to display them in tables, as numbers, and with graphs. You learn the correct choices for describing categorical data using the Dog Toys dataset and the FREQUENCIES menu in SPSS. We create frequency tables and bar c
From playlist Introduction to Statistics with IBM SPSS 27 for Beginners (with Puppies)
RegressionANOVA.6.Cat vs. cont
This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources
From playlist Regression and ANOVA
Explore and Summarize Categorical (Nominal or Ordinal) Data in SPSS (Ep.5)
We dive deeper into exploring and summarizing categorical data with SPSS. We review levels of measurement so you can determine what kinds of data you have. Both nominal and ordinal are categorical variables because they each have limited number of distinct categories, but that ordinal data
From playlist Introduction to SPSS Statistics 27
Describing and Displaying Data using Descriptive Statistics Course Review (Weeks 1-4)
We review the highlights of the first four weeks of business statistics in which we learned levels of measurement (nominal, ordinal, interval, ratio), how to create tables and graphs for categorical and qualitative data, and the fundamentals of displaying data and datasets in business stat
From playlist Basic Business Statistics (QBA 237 - Missouri State University)
Python for Data Analysis: Exploring and Cleaning Data
This video examines a variety of data exploration and preparation tasks you should consider after loading a data a set to prepare it for analysis, an examples of how to perform those tasks in Python. Subscribe: ► https://www.youtube.com/c/DataDaft?sub_confirmation=1 This is lesson 14 of
From playlist Python for Data Analysis
SPSS - Mediation with PROCESS Categorical Variables (Model 4)
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2018 You will learn how to use the new version of the PROCESS version 3 plug in for SPSS by A Hayes with model 4. In this video, you will learn how to run a simple mediation model with categorical X variables, as well as dat
From playlist Mediation and Moderation
Applied Machine Learning 2019 - Lecture 05 - Preprocessing
Preprocessing: Scaling, working with categorical data, feature distributions. Working with Pipelines and ColumnTransformer in scikit-learn Slides and materials on the course website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019
R - Moderation with Categorical M MeMoBootR
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2018 Part 3 in our moderation R package series! I also added diagrams! This video covers a new package that I am writing to making mediation and moderation a one-function process. You will learn about the new MeMoBootR pack
From playlist Mediation and Moderation
Mode for Categorical Data? LET Function to Dynamically List all Modes with Category Labels EMT 1770
Download Excel File: https://excelisfun.net/files/EMT1770.xlsx Learn how the basics of how to use the LET function and define variables within the formulas with repeating formula elements more efficient. Learn how to use LET and six other functions to create a mode formula for categorical
From playlist Single Cell Formula Reporting in the Excel Worksheet
Data and Variables in Business Statistics – An Introduction (Week 1)
We begin Basic Business Statistics with an introduction to numbers and how they become variables. • Statistics give us a tool to evaluate claims of truth with the scientific method • We can use archival, observational, or experimental research • The practice, procedures, and products o
From playlist Basic Business Statistics (QBA 237 - Missouri State University)