Multistage Sampling:
Used for national surveys encompassing various subgroups.
Involves multiple stages: stratifying by region, then income level within regions, and selecting one cluster per income level.
Combines different sampling strategies for complex populations.
Common Sampling Methods:
Simple Random Sample: Basic method where every individual has an equal chance of selection.
Stratified Random Sample: Divides the population into subgroups and samples proportionally from each group.
Other methods may be appropriate under different circumstances.
Sampling Examples:
Class of 200:
20 rows, 10 students; selecting 3 students from each row is stratified sampling since rows may differ based on placement.
Flight Sampling:
Surveying all passengers from one randomly selected flight exemplifies cluster sampling.
Assembly Line Testing:
Every 100th item tested is systematic sampling after a random first selection.
For a survey of 700 British students across unis, TAFEs, and private colleges:
Total institutions: 5 unis + 25 TAFEs + 5 private colleges = 35
Sample size distribution:
100 uni students
500 TAFE students
100 private college students
Sampling should be proportional to the number of each type of institution in the population.
Definition: Variability of a population can be measured to ensure representative sampling.
Methods may involve complex calculations such as standard deviation, rarely employed in practice due to difficulty.
Purpose: After experiment design and data collection, summarizing and visualizing data is crucial.
Key Concepts:
Types of variables: quantitative (continuous measurements) and categorical (group labels).
Introduction of histograms and quartiles as methods for data visualization.
Quantitative Variables: Numerical values (height, weight).
Categorical Variables: Group names without numerical meaning (club, gender).
Some categorical variables can be coded numerically for convenience, but arithmetic operations are still not valid.
Ordinal Variables: Categorical with logical ordering (T-shirt sizes, grades).
Definition: Distribution is the relationship between values a variable can take and the frequency of