Forecasting and Sampling Techniques PDF

SAKSHAM: IL6 WEBINAR for Logistics “Forecasting and Sampling Techniques” 1 Basics Of Statistics Types Of Data ❖ Numerical ❖Discrete ❖Continuous ❖ Categorical ❖Nominal ❖Ordinal ❖ Mean, Median, Mode ❖ Variance, St...

SAKSHAM: IL6 WEBINAR for Logistics “Forecasting and Sampling Techniques” 1 Basics Of Statistics Types Of Data ❖ Numerical ❖Discrete ❖Continuous ❖ Categorical ❖Nominal ❖Ordinal ❖ Mean, Median, Mode ❖ Variance, Standard Deviation ❖ Dependent and Independent Variables ❖ Correlation 2 Decisions that Need Forecasts ❖Which markets to pursue? ❖What products to produce? ❖How many people to hire? ❖How many units to purchase? ❖How many units to produce? ❖And so on…… 3 Common Characteristics of Forecasting ❖Forecasts are rarely perfect ❖Forecasts are more accurate for aggregated data than for individual items ❖Forecast are more accurate for shorter than longer time periods 4 Forecasting Steps ❖What needs to be forecast? Level of detail, units of analysis & time horizon required ❖What data is available to evaluate? Identify needed data & whether it’s available ❖Select and test the forecasting model Cost, ease of use & accuracy ❖Generate the forecast ❖Monitor forecast accuracy over time 5 Types Of Forecasting Models Qualitative (technological) methods: – Forecasts generated subjectively by the forecaster Quantitative (statistical) methods: – Forecasts generated through mathematical modeling 6 Qualitative Forecasting Models 7 Statistical Forecasting Models 8 Composition of Time Series Data Data = historic pattern + random variation Historic pattern may include: – Level (long-term average) – Trend – Seasonality – Cycle 9 Time Series Patterns 10 Level Forecasting Methods ❖Naïve Forecasting ❖Simple Mean ❖Moving Average ❖Weighted Moving Average ❖Exponential Smoothing 11 Time Series Example Problem 12 Naïve Forecasting Next period forecast = Last Period’s actual: F =A t+1 Simple Average (Mean) Next period’s forecast = average of all historical data At + At 1 + A − +............. Ft +1 = − t 2 n 13 Moving Average Next period’s forecast = simple average of the last N periods At + A − +.........+ A − + Ft+1 = t 1 t N 1 N A smaller N makes the forecast more responsive A larger N makes the forecast more stable 14 Weighted Moving Average Next period’s forecast = weighted average of the last N periods F = C A + C A +.........+ C A where C + C +.........C = 1 15 Time Series Example Solution 16 Forecast Accuracy Forecasts are rarely perfect Need to know how much we should rely on our chosen forecasting method Measuring forecast error: E = A −F Note that over-forecasts = negative errors and under-forecasts = positive errors 17 Tracking Forecast Error Mean Absolute Deviation (MAD): – A good measure of the actual error in a forecast actual − forecast n Mean Square Error (MSE): – Penalizes extreme errors Tracking Signal – Exposes bias (positive or negative) TS = (actual - forecast ) M AD 18 Accuracy and Tracking Signal Example 19 Sampling Techniques Population and Sample ❖ A population is a collection of elements about which we wish to make an inference. ❖ Sampling units are nonoverlapping collections of elements from the population that cover the entire population. 20 Probability Vs Nonprobability ❖ Probability Samples: each member of the population has a known non-zero probability of being selected – Methods include random sampling, systematic sampling, and stratified sampling. ❖ Nonprobability Samples: members are selected from the population in some nonrandom manner – Methods include convenience sampling, judgment sampling, quota sampling, and snowball sampling 21 Sampling for population 22 Simple Random Sampling Simple Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large an equal and known chance of being identify every member of the subjects becomes biased. – You can use software, such as numbers or to draw directly from the columns 23 Systematics Random Sampling 24 Stratified Random Sampling 25 Custer Sampling 26 Strata Vs Cluster 27 Multistage Sampling 28 Multiphase Sampling Part of the information collected from whole sample & part from subsample. In Tb survey in all cases – Phase I X –Ray chest in +ve cases – Phase II Sputum examination in X – Ray +ve cases - Phase III Survey by such procedure is less costly, less laborious & more purposeful 29 Convenience Sampling 30 Judgmental or Purposive Sampling 31 Quota Sampling 32 Snowball Sampling 33  Thank You Presenter’s Name :Priyendu N Giri Contact : 7077762551