Webinar on Forecasting Techniques

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Questions and Answers

What distinguishes simple random sampling from other sampling methods?

It selects members based on convenience.

It is the least reliable form of sampling.

It ensures every member has an equal and known chance of being selected. (correct)

It allows each member an unequal chance of selection.

Which of the following methods involves selecting members based on specific criteria or judgment?

Stratified sampling

Quota sampling

Convenience sampling

Purposive sampling (correct)

What is a primary characteristic of cluster sampling?

It requires complete population data for selection.

It ensures an equal chance of selection across all population members.

It's focused on small, homogeneous groups.

It randomly selects entire groups rather than individuals. (correct)

How does multistage sampling differ from stratified sampling?

It requires multiple random sampling steps. Signup and view all the answers

Which sampling method is considered less costly and labor-intensive by collecting data from a whole sample and then a subsample?

Multiphase sampling Signup and view all the answers

Which of the following statements about forecasts is accurate?

Forecasts are generally more accurate for shorter time periods. Signup and view all the answers

Which type of data is considered categorical?

Brand names of products Signup and view all the answers

Which of the following forecasting models is based on subjective assessment?

Qualitative methods Signup and view all the answers

What is NOT a common characteristic of forecasting?

Forecasts can always be relied upon. Signup and view all the answers

Which component is NOT included in the historic pattern of time series data?

Probability distribution Signup and view all the answers

Which of the following is true about the naïve forecasting method?

It predicts the next period's forecast by using the previous period’s actual value. Signup and view all the answers

What is the focus of quantitative forecasting methods?

Mathematical modeling and statistical analysis Signup and view all the answers

Which step is NOT typically part of the forecasting process?

Communication with external stakeholders Signup and view all the answers

Which method would provide a forecast that is more responsive to recent changes?

Smaller N in moving averages Signup and view all the answers

What formula accurately represents forecast error?

E = A - F Signup and view all the answers

What is the primary characteristic of the Mean Absolute Deviation (MAD)?

It cannot be negative. Signup and view all the answers

In tracking forecast errors, what does a positive tracking signal indicate?

Under-forecasting bias Signup and view all the answers

Which of the following sampling methods gives each member of the population a known non-zero probability of selection?

Systematic sampling Signup and view all the answers

What characteristic of a larger N in moving averages is highlighted?

Greater stability of forecasts Signup and view all the answers

What does the combined weight in a weighted moving average forecast need to total?

One Signup and view all the answers

Which measure penalizes extreme forecast errors more heavily?

Mean Square Error (MSE) Signup and view all the answers

Study Notes

Webinar: Forecasting and Sampling Techniques

Webinar title: SAKSHAM: IL6 Webinar for Logistics, "Forecasting and Sampling Techniques"
Presenter: Priyendu N Giri
Contact: 7077762551
Company: Tata Steel

Basics of Statistics

Types of Data:
- Numerical (Discrete & Continuous)
- Categorical (Nominal & Ordinal)
Statistical Measures:
- Mean, Median, Mode
- Variance, Standard Deviation
- Dependent and Independent Variables
- Correlation

Decisions Requiring Forecasting

Examples:
- Which markets to pursue?
- What products to produce?
- How many people to hire?
- How many units to purchase?
- How many units to produce?
- ...and more...

Common Characteristics of Forecasting

Forecasts are rarely perfect
Forecasts are more accurate for aggregated data than for individual items
Forecasts are more accurate for shorter than longer time periods

Forecasting Steps

Define what needs to be forecast (level of detail, units of analysis, time horizon)
Identify available data and evaluate its suitability
Select and test a forecasting model (considering cost, ease of use, and accuracy)
Generate the forecast
Monitor forecast accuracy over time

Types of Forecasting Models

Qualitative: Forecasts generated subjectively by the forecaster
Quantitative (Statistical): Forecasts generated through mathematical modeling

Qualitative Forecasting Models

Executive opinion: A group of managers provides a forecast
Market research: Surveys and interviews used to identify customer preferences
Delphi method: Eliciting consensus among experts

Statistical Forecasting Models

Time Series Models:
- Assume future follows past patterns
- Single variable models
- Machine learning models
Causal Models:
- Explore cause-and-effect relationships
- Use leading indicators to predict the future
- Use independent/feature variables to forecast dependent/target variables

Composition of Time Series Data

Data = historic pattern + random variation
Historic pattern may include:
- Level (long-term average)
- Trend
- Seasonality
- Cycle

Time Series Patterns

Level or Horizontal Pattern: Data varies around a consistent mean value
Trend Pattern: Data shows a consistent upward or downward movement over time
Seasonal Pattern: Data exhibits a regularly repeating pattern over time
Cycle Pattern: Data displays a repetitive but non-fixed pattern of increases and decreases over a longer period

Level Forecasting Methods

Naïve Forecasting
Simple Mean
Moving Average
Weighted Moving Average
Exponential Smoothing

Time Series Example Problem

Includes data and demonstrates various forecasting methods

Forecast Accuracy

Forecasts are rarely perfect
Need to measure forecast error (E = A - F)
Over forecasts: negative error
Under forecasts: positive error

Tracking Forecast Error

Mean Absolute Deviation (MAD)
Mean Square Error (MSE)

Accuracy and Tracking Signal Example:

Data comparison, Methods A & B
Measurement of MAD and MSE
Tracking Signal
Review of forecast accuracy

Sampling Techniques

Population: Collection of elements we wish to make inferences about
Sampling Units: Non-overlapping collections of elements that cover the entire population
Probability Sampling:
- Each member has a known non-zero probability of selection
- Random sampling, systematic sampling, stratified sampling
Non-probability Sampling:
- Selection is not random; includes convenience sampling, judgment sampling, quota sampling, snowball sampling

Sampling for Population

Study Population: Collection of subjects relevant to the investigation
Target Population: The wider group being studied, which data generalizes to
Sampling Frame: A complete list of all elements in the study population
Sample: A subset of the study population selected for analysis

Simple Random Sampling

Purest form of probability sampling
Each member has an equal chance of selection
Useful when populations are manageable

Systematic Random Sampling

Every nth member is selected
Good when a complete list is available
May not be truly random if underlying order exists

Stratified Random Sampling

Population segmented by strata (groups)
Proportional representation ensures all strata are adequately represented
Varied sampling fractions within strata are possible; important in minority groups

Cluster Sampling

Population divided into clusters
Random sample of clusters is selected
All or some units are then selected from the selected clusters
Useful when a complete list is impractical

Multistage Sampling

Complex cluster sampling; multiple stages of random sampling
Subsamples from clusters are randomly selected at each stage
Useful for larger populations where complete list is missing

Multiphase Sampling

Collecting information from whole & part of a sample at different phases
Useful for costly or complex data collection processes

Convenience Sampling

Selection based on ease and accessibility

Judgmental or Purposive Sampling

Selection based on researcher judgment
Researcher must ensure sample reflects population

Quota Sampling

Aims for proportion in sub-groups; like stratified; but selection non-random

Snowball Sampling

Initial subjects recruit additional subjects
Useful for rare or hidden populations

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Description

Join the SAKSHAM IL6 Webinar for Logistics, where presenter Priyendu N Giri of Tata Steel delves into essential forecasting and sampling techniques. This session focuses on the basics of statistics, common characteristics of forecasting, and the critical decisions that require effective forecasting strategies.