Business Statistics: Chapter 1 - Basic Concept 1

Vocabulary flashcards covering key concepts from the lecture notes on statistics, data types, scales, and statistical methods.

Statistics

The science of collecting, organizing, analyzing, presenting, and interpreting data; aims to explain patterns and correlations in data to identify causes and effects.

Population

The entire set of elements about which conclusions are to be drawn in a study.

Sample

A subset of the population selected for analysis.

Parameter

A numerical summary characteristic of a population.

Statistic

A numerical summary characteristic computed from a sample.

Variable

A characteristic of interest for the elements in a study.

Element

An entity of which data are collected in a study.

Observation

The set of measurements obtained for a particular element.

Data Set

The collection of data values across elements and variables for a study.

Data

Facts and figures collected, analyzed, and summarized for presentation; the data set comprises all collected data.

Measurement Scale

The scheme for numerical representation of the values of the variables that determines which mathematical operations are meaningful.

Nominal Scale

A categorical variable with two or more categories, no intrinsic ordering, categories distinct, non-overlapping, and exhaustive.

Ordinal Scale

A categorical scale with categories that can be ranked or ordered; differences between categories may not be equal.

Interval Scale

A numerical scale where differences are meaningful but zero is arbitrary and does not imply absence of the characteristic.

Ratio Scale

A numerical scale with a true zero point, where zero indicates absence of the characteristic.

Qualitative Data

Data consisting of categorical labels or categories.

Quantitative Data

Data that are numerical and measure quantities.

Discrete Data

Quantitative data that arise from counting and take distinct, separate values.

Continuous Data

Quantitative data that can take on any value within a range or interval.

Cross-Sectional Data

Data collected at about the same point in time across subjects or entities.

Time Series Data

Data collected over multiple time periods to observe trends and patterns.

Primary Data

Data collected directly by the researcher for a specific purpose (e.g., via surveys, experiments, or observations).

Secondary Data

Data collected from existing sources, such as reports or databases.

Manifest Variable

A variable that can be directly measured with a single indicator.

Latent Variable

A variable that cannot be measured directly and is inferred from multiple indicators.

Exogenous Variable

An independent variable that causes changes in other observable or latent variables.

Endogenous Variable

A dependent variable that is influenced by other variables in the model.

Descriptive Statistics

Statistical methods that summarize and describe the features of a data set (e.g., measures of central tendency and variability).

Inferential Statistics

Methods that use sample data to draw conclusions or make predictions about a population, including estimation and hypothesis testing.

Estimation

The process of using sample data to estimate a population parameter.

Hypothesis Testing

A method to test claims about a population using sample data.

Data Processing

Procedures to organize, clean, and analyze collected data.

Data Coding

Assigning codes to data to facilitate processing and analysis.

Population Parameter

A numeric characteristic of a population (e.g., mean μ, variance σ^2).

Sample Statistic

A numeric characteristic computed from a sample (e.g., x̄, s^2).

Data Visualization

Graphical representation of data to communicate information clearly.