01-30-2012

Statistical inference - Method of looking at statistics and drawing conclusions about a population.

Gathering data -

Simple Random Sample (SRS) - Every observational unit has an equal chance of being included. (In an ideal world - Doesn’t always work and not the best for all cases)

Stratified Sampling - Divide the population into strata (e.g. men and women) and then perform a random sample of these strata

Systematic Sampling - Use when you can’t get a random sample, assign a number, then pick every occurrance of that number. (e.g. Roman legions, killing every 10th person).

Cluster Sampling - Creating samples from groups called clusters and randomly selecting there-in (e.g. zip codes, area codes, floors of a building)

Convenient Sampling - Take sample of those immediately available to you (Poor representation of the population)

Target population - Population  we are interested in collecting data on

Bias - Systematic elimination/exclusion of a segment within the target population

Types of Studies:

1. Anecdotal Studies - Like an opinion, not scientific, based on limited experience (“useless”)
2. Survey Studies - Great source of obtaining opinions, not great for truth/fact
3. Observational Studies - “Sit back and see what happens.” No cause and effect relationship
4. Experimental Studies - “Impose something” Requires some form of randomization (eliminates bias) and a control group (receives no treatment) which enable us to create a cause and effect relationship.

01-25-2012

Statistics – The art and science of collecting, analyzing, presenting and interpreting data.

Descriptive statistics – Quick mathematical summary of the data (mean, median, mode, range, variance, standard deviation)

Observational Unit (element) – Object (person or thing) we are collecting data on or about.

1. Variable – Characteristic of an observational unite that could change from one unit to the next.
1. Quantitative Variables – Numeric in nature. Usually based on some type of measurement or number.
2. Categorical (Qualitative) Variables – Grouping into categories (e.g. gender, eye color, hair color)
1. Binary – Subset of categorical where there are only two possible variables (e.g. gender)

2. Population – Refers to all possible observational units. (100% accuracy only comes from a population)
1. Parameter – Numerical characteristic about a population

3. Sample – Small group of the population.
1. Statistic – Numerical characteristic about a sample. Statistics are used to make predictions about a population