Statistics Sampling Methods and Forecasting

Forecasting using mathematical models and historical data to predict future values.

Subjective forecasting based on expert opinions and judgment.

Data collected over a period of time, exhibiting patterns like trends and seasonality.

Simple forecasting method where the next period's forecast is the same as the last period's actual value.

Predicting the next value by taking the average of all historical data.

Identifying what to forecast, gathering data, selecting a model, generating the forecast, and monitoring accuracy.

How close the forecast is to the actual value.

A persistent upward or downward movement in the data over time.

Repeating pattern of data fluctuations within a year.

The long-term average value of the data.

Predicting the next period's value by averaging the values of the past 'N' periods.

Predicting the next period's value by averaging past values, but assigning different weights to each.

The difference between the actual value and the predicted value.

A measure of the average absolute error in a forecast.

Calculates the average squared errors in a forecast.

A measure of forecast bias, indicating whether the forecast consistently over- or under-estimates.

The complete set of items or individuals we're interested in studying.

A subset of the population used to learn about the population.

A sample where each member of the population has a known chance of being selected.

Selecting sample members from a population in a non-random way.

Selecting participants who are easily accessible.

Selecting participants based on the researcher's judgment.

Selecting participants to match the characteristics of the population.

Recruiting participants by asking them to recommend others.

Every member has an equal chance of being selected.

Selecting participants at fixed intervals from the population.

Dividing the population into groups and randomly sampling from each group.

Dividing population into clusters and randomly selecting clusters to sample from.

Selecting samples in multiple steps or stages.

Collecting information from a part of the full sample combined with a subsample.