Forecasting, ANOVA, and Regression

A set of flashcards covering vocabulary and key concepts related to forecasting, ANOVA, and regression analysis.

Residual

The difference between actual value and predicted value in regression analysis.

Mean Absolute Percentage Error (MAPE)

A metric that expresses forecast error as a percentage.

R² (R-squared)

A measure that indicates how much of the variation in the dependent variable is explained by the model.

Adjusted R²

R² adjusted for the number of predictors, preferred for models with multiple independent variables.

P-value

Statistical measure that helps determine the significance of results; a p-value < 0.05 indicates significance.

Over-forecasting

Predicting higher demand than what actually occurs, leading to negative residuals.

Under-forecasting

Predicting lower demand than what actually occurs, leading to positive residuals.

ANOVA (Analysis of Variance)

A statistical method used to determine if three or more group means are different.

F-statistic

A statistic used in ANOVA to compare the variance between groups to the variance within groups.

Dummy Coding

A method of encoding categorical variables as binary variables for use in regression analysis.

Statistically significant

A result that is unlikely to occur by chance, usually indicated by a p-value < 0.05.

Forecast Error

The difference between actual and forecasted values, calculated as Actual - Forecast.

Simple Moving Average (SMA)

An average calculated over a fixed number of periods, weighting all periods equally.

Weighted Moving Average (WMA)

An average where more recent periods are given more weight than older periods.

Trend

A long-term increase or decrease in a time series data set.

Seasonality

Regular patterns or cycles in time series data, often related to time of year.

Cyclicality

Irregular, long-term fluctuations in a time series data set, often related to economic cycles.

Exponential Smoothing

A forecasting method that uses weighted averages of past observations with more weight on recent data.

Tukey Test

A post-hoc test used in ANOVA to find which specific group means differ.

Null Hypothesis (H₀)

A default assumption that there is no difference or effect, often tested in ANOVA.

Alternative Hypothesis (Hₐ)

The hypothesis that suggests at least one group mean is different in ANOVA.

Coefficient

A value that represents the change in the dependent variable for a one-unit change in an independent variable.

Significance Level

The threshold for determining whether a p-value indicates statistical significance, commonly set at 0.05.

Forecasting

The process of predicting future demand based on past data.

Forecast Accuracy Metrics

Quantitative measures used to assess the accuracy of forecasting methods.

Excess Inventory

Products that remain unsold, leading to increased storage costs and potential waste.

Stockouts

Instances where demand exceeds supply, resulting in lost sales opportunities.

Customer Dissatisfaction

Negative customer experience due to unmet expectations or product unavailability.

Cash Flow Issues

Financial problems that arise when cash is tied up in excess inventory.

Overfitting

A modeling error that occurs when a model is too complex and captures noise instead of underlying patterns.

Observable Patterns

Trends or seasonality seen in the data that can inform forecasting.

Statistical Noise

Random variability in data that does not reflect any real underlying trends.

Inventory Optimization

Adjusting inventory levels to meet forecasted demand effectively.

Sales Force Estimates

Forecasting based on the judgments and predictions of sales personnel.

Quantitative Forecasting

Forecasting methods based on numerical data and statistical techniques.

Qualitative Forecasting

Forecasting methods based on subjective judgment, intuition, or opinions.

Sales Forecasting

The process of predicting future sales based on historical sales data and analysis.

Forecast Implications

The potential consequences of forecasted demand on inventory, cash flow, and customer satisfaction.

Risk Management

The process of identifying, assessing, and minimizing potential losses in forecasting and inventory.

Bias in Forecasting

The systematic deviation of forecasts from the actual values.

Forecast Adaptation

Modifying forecasting methods or models based on changing conditions or new information.

Statistical Significance

A determination that a result is unlikely to occur by random chance, often driven by p-values.

Model Fit

How well a statistical model explains the variation in the data.

Forecasting Models

Mathematical frameworks used to predict future values based on past data.

Inventory Carrying Cost

Total cost associated with holding unsold goods, including storage, insurance, and deterioration.

Discrepancy Analysis

Reviewing the differences between forecasted and actual values to improve accuracy.

Residual Analysis

Evaluating the residuals to assess the performance of a forecasting model.

Sales Prediction Techniques

Various methodologies used to estimate future sales figures.

Quantitative Data Series

Collected numerical data used in forecasting models.

Retail Forecasting Challenges

Difficulties in accurately predicting consumer demand in the retail sector.

Model Selection Criteria

Guidelines for choosing between different statistical models based on performance metrics.

Heteroscedasticity

When the variance of errors in a regression model is not constant across observations.

Correlation Coefficient

A statistical measure that indicates the extent to which two variables fluctuate together.

Long-term Forecasting

Predicting future outcomes over an extended period, often requiring more complex models.

Short-term Forecasting

Predicting future values in the near term, generally based on recent trends.

Regression Analysis

A statistical method for examining the relationship between variables.

Forecast Review Process

A systematic approach to reassess and adjust forecasts as new data become available.

Sales Variability

The fluctuations in sales figures over time due to various factors.

Mean Absolute Deviation (MAD)

An accuracy measure that indicates the average absolute difference between actual and forecasted values.

Forecasting Horizon

The future time frame for which forecasts are made.

Regression Coefficient Interpretation

Understanding how changes in independent variables affect the dependent variable.

Multicollinearity

A situation in regression where two or more predictors are highly correlated, potentially distorting statistical estimates.

Data Validation

The process of ensuring that data used in forecasting is accurate and reliable.

Sensitivity Analysis

The study of how different variables impact a given outcome in forecasting models.

Scenario Planning

Strategizing based on hypothetical situations to prepare for potential future events.

Forecast Stakeholders

Individuals or groups involved in or affected by the forecasting process.