Time series: observations of well-defined data at regular intervals (weekly/monthly/yearly). Components: trend (long-term), cyclical (1-7 years), seasonal (yearly), irregular (random). Models: multiplicative (Y=T×C×S×I), additive (Y=T+C+S+I). Moving average smoothing for forecasting.
Sampling methods: probability (simple random, stratified, cluster, multistage) vs non-probability (purposive, convenience). Simple random: equal independent chance, requires population list. Stratified: heterogeneous subpopulations. Cluster: cost-effective. Multistage: widely spread populations.
Forecasting: reliable estimates of uncertain future events using historical data. Types: demand, environmental, technological. Timing: short-range (<3 months), medium-range (3-12 months), long-range (3+ years). Methods: qualitative (personal opinion, Delphi), quantitative (freehand, smoothing, trend).
Sampling distribution: probability distribution of all possible sample statistic values. Mean sampling distribution properties: arithmetic mean equals population mean µ, standard deviation equals σ/√n. Central Limit Theorem: non-normal populations approximate normal (n≥30).
Sampling definition: selecting representative sample from population for analysis and inference. Needs: population movement, cost/time constraints, destructive testing. Bias types: undercoverage, non-response, wording. Sampling vs non-sampling errors. Population parameters (µ, σ²) vs sample statistics (X̄, s²).