Essentials of Business Statistics - Chapter 1: Data and Data Preparation

Vocabulary practice flashcards covering fundamental statistical terms, data classification, scales of measurement, and data preparation methods from Chapter 1.

Data

Collection of facts, figures, or other contents, both numerical and non-numerical.

Statistics

The science that deals with collecting, preparing, analyzing, presenting, and interpreting data.

Descriptive Statistics

Refers to the summary of important aspects of a data set, including collecting, organizing, and presenting data in the form of charts and tables.

Inferential Statistics

Refers to drawing conclusions about a larger set of data (population) based on a smaller set of data (sample).

Population

Consists of all items or members of interest in a statistical study.

Sample

A subset of the population used to make inferences about various characteristics of the population.

Sample Statistic

A sample statistic is a number calculated from a small group that you use as an educated guess for the whole population.

Population Parameter

A population parameter is the true, exact fact or measurement about an entire group of people or things, though it's usually impossible to calculate because the group is too massive.

Cross-sectional Data

looking at a bunch of different subjects right now, without tracking how they change over time.

EX: When you cut a tree and look at the flat surface, you see all the rings right there at once. You aren't watching the tree grow over the next 10 years. You are just looking at a frozen slice of reality today.

Time Series Data

information collected over a regular period of time to track how something changes or grows.

EX: YOUR HEIGHT over the years

Structured Data

information that is organized into a neat, predictable grid of rows and columns—like a spreadsheet or a table.

Unstructured Data

free-form information that doesn't fit into a neat grid of rows and columns. It includes things like words, pictures, audio, and video.

Big Data

A massive volume of structured and unstructured data that is extremely difficult to manage, process, and analyze using traditional data processing tools.

Variable

A characteristic that can be different from one observation to another.

• Height — people can be different heights.

• Age — people can have different ages.

• Eye color — people can have different eye colors.

Categorical Variable

Also known as qualitative data, data that represent categories and use labels or names to identify distinguishing characteristics of each observation.

EX: eye color, shirt size

Numerical Variable

Also known as quantitative data, data that represent meaningful numbers used to identify distinguishing characteristics of each observation.

Discrete Numerical Variable

A numerical variable that can only have certain countable values

EX: number of siblings cant have 2.5 siblings

USUALLY WHOLE NUMBER

Continuous Numerical Variable

A continuous variable is something you measure rather than count. It can be any value, including decimals and fractions, within a specific range. EX: height, weight, temperature

Nominal Scale

a type of categorical data where you put things into groups or labels, but there is no order, rank, or hierarchy between them. No group is "higher," "better," or "more" than another.

Ordinal Scale

categorical information that has a specific order or rank, but the actual mathematical distance between the ranks doesn't mean anything precise.

EX: race results, movie star ratings

Interval Scale

numerical information where the steps between numbers are equal and meaningful, but zero doesn't mean "nothing”

example: TEMP 0 Celsius doesn’t mean theirs no heat or coldness outside

Ratio Scale

the highest level of numerical measurement where the numbers are meaningful, the gaps between them are equal, and zero actually means zero

EX: Money: Having $0 means zero cash.

Omission Strategy

a method for handling missing data where you completely skip or exclude any row or observation that has a blank spot

Imputation Strategy

a method for handling missing data where you fill in the blanks with an educated guess

Subsetting

carving out a smaller, targeted chunk of a massive dataset so you can focus only on the information you actually need for your current analysis.

EX: imagine searching a clothing website with 10,000 items. If you filter it by "Hoodies" and "Size Medium," you are subsetting the data

what is quantitative data (numerical data)

data that uses numbers and can be measured or counted.

• Your age: 18 years

• Your height: 65 inches

• Number of siblings: 2

• Test score: 87

• Temperature: 72°F

• Easy way to remember:
  Quantitative = Quantity = Numbers