1What is Sample Data?
An estimate using a small amount of data sourced from all indivuals of interest. It can be used to make conclusions about a population.
2Random Sampling
Data collected at random. Helps avoid bias.
3Sampling Strategies
1. Stratified: dividing population into groups and sampling each
2. Cluster: choosing specific groups in a population and collecting data between all chosen groups as one study
3. Systemtic: Choosing speicifiv subjects for sampling based on a pattern (ex. every third person in a list is chosen)
4Sampling Biases/Distortions of Data
Convenience Sampling: sample chosen for ease of access (Ex. only surveying friends)
Voluntary Response: Often those with strong opinions will respond
Non-Response: Selected study but some choose not to respond
53 Main Goals of Inference **UPDATE**
6What is the relationshion between Population and Statistics?
Population → Statistics: predicts the statistics of the population
Statistics → Population: makes inferences about the population
7What is an Observational Unit?
What is being used as a measurement of a variable or what is being used to support a study
Ex. People, Companies, Hosptials, etc.
8What is a Variable?
characteristics of what is being studied
Ex. Age, Income, etc.
9What is a Constant?
An element that does not change
Ex. there are always going to be 12 months in a year
10What is an Observation?
A recorded value for a unit
11What is a Data Set?
A collection of data/observations
12What is a Data Matrix? Explain?
How the data is organized
→ Rows = Cases
→ Columns = Variables
* remember: RCCV
13Explain Explanatory and Response variables. Why is it significant?
Explanatory → variables influencing the outcome (explains cause)
Response → the outcome measured
Ex. Smoking could be the explanatory variable, and lung cancer can be the response variable.
142 types of Quantitative Variables
1. Discrete: countable by whole numbers; Ex. number of people, amount of emails, etc.
2. Continuous: measurable in decimals; Ex. time, height, weight, etc.
152 types of Categorical/Qualitative Variables
1. Nominal: unordered categories; Ex. color, blood type
2. Ordinal: ordered categories; Ex. Education levels, clothing sizes
16Observational Vs. Expiremental Studies
Observational: Researcher only records information and doesn't intervene. Shows association but not causation, limiting strong causal conclusions
Ex. Recording students sleep and their text score. Association but no causation bc the students that sleep more might manage their time better.
Expiremental: Researcher intervenes through randomizations, control groups, placebos, and/or blinding.
17What is Raw Data?
data ranked in the order in which it was recorded, not yet organized.
18Organizing and Graphing Categorical/Qualitative Data
Types of variables = category
Number in category = frequency
19What is a Frequency Distribution?
A list of all categories and the number of elements that belong to each category