Introduction to Business Analytics & Decision Modeling Flashcards

Vocabulary flashcards covering Introduction to Business Analytics, Spreadsheet Modeling, Risk Analysis & Simulation, Statistical Inference, and Applications of Linear Programming.

Business Analytics

The scientific process of transforming data into insights for making better business decisions.

Descriptive Analytics

Analytics techniques that summarize historical data to describe what has happened in the past.

Predictive Analytics

Analytics techniques that use historical data and statistical or machine learning models to forecast future events or quantify outcomes under uncertainty.

Prescriptive Analytics

Analytics techniques that evaluate various options and recommend the best course of action to achieve an objective.

Business Intelligence (BI)

The processes, technologies, and tools used to gather, store, access, and analyze data to aid business decision-making, often synonymous with descriptive analytics.

Statistical Inference

The process of drawing conclusions about an underlying population parameter based on sample data.

Spreadsheet Modeling

The process of designing and implementing quantitative decision models within a spreadsheet format to analyze data and evaluate outcomes.

Decision Model

A quantitative model that represents a real-world decision situation, connecting inputs (data and decision variables) to outputs (performance measures).

Deterministic Model

A decision model in which all input parameters are assumed to be known with certainty and held fixed.

Stochastic Model

A decision model that incorporates uncertainty or randomness, treating inputs as random variables.

Influence Diagram

A graphical representation that outlines the logical structure and relationships among inputs, intermediate calculations, and outputs in a decision model.

Risk Analysis

The process of identifying, quantifying, and evaluating the potential impact of uncertainty on business decision outcomes.

Scenario Analysis

A risk analysis method where specific cases (such as worst case, most likely case, and best case) are defined by altering input parameter values to observe their impact on performance outputs.

Worst Case Scenario

A scenario evaluating performance under the most pessimistic combinations of uncertain input parameters.

Best Case Scenario

A scenario evaluating performance under the most optimistic combinations of uncertain input parameters.

Monte Carlo Simulation

A computer-based quantitative technique that draws repeated random samples from probability distributions to model complex systems under uncertainty.

Hypothesis Testing

A statistical procedure that uses sample evidence to evaluate two competing claims regarding a population parameter.

Null Hypothesis ($H_0$)

A baseline statement assuming no effect, no difference, or status quo, assumed true until sufficient evidence indicates otherwise.

Alternative Hypothesis ($H_a$)

The statement indicating the presence of an effect, difference, or directional change that a researcher seeks to support.

Type I Error

The error committed when a true null hypothesis ($H_0$) is incorrectly rejected.

Type II Error

The error committed when a false null hypothesis ($H_0$) is failed to be rejected.

Errors in Hypothesis Testing

A matrix illustrating decisions (Reject $H_0$ vs. Do not reject $H_0$) against true population conditions ($H_0$ is true vs. $H_0$ is false).

p-value

The probability of obtaining a test statistic at least as extreme as the observed sample value, assuming the null hypothesis ($H_0$) is true.

Level of Significance ($\alpha$)

The maximum threshold probability of making a Type I error that a researcher is willing to tolerate.

Linear Programming (LP)

A mathematical optimization model used to allocate scarce resources efficiently when both the objective function and constraints are linear.

Integer Programming (IP)

A linear programming model in which some or all decision variables are restricted to integer values.

Transportation Problem

A specialized linear programming formulation designed to minimize total distribution costs of shipping goods from multiple origins to multiple destinations.

Assignment Problem

A specific class of transportation linear programming problems that pairs agents to tasks on a one-to-one basis at minimum total cost or maximum efficiency.