Chapter 1 - Statistics, Data, and Statistical Thinking Flashcards
Learn your vocabularies. (33 cards)
Statistics
The science of data.
It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical and categorical information.
[1.2] Descriptive Statistics
Utilizes NUMERICAL and graphical methods to explore data, i.e, to look for patterns in a data set, to summarize the info. revealed in a data set, and to present the info. in a convenient form.
Inferential Statistics
Utilizes sample data to make…
ESTIMATES, DECISIONS, PREDICTIONS, OR OTHER GENERALIZATIONS about a larger set of data.
[1.3] Experimental (or observational) unit
Is an object
example: person, thing, transaction, or event) upon which we collect data
Population
A set of units (usually people, objects, transactions, or events) that we are interested in studying.
Variable
A characteristic or property of an individual experimental (or observational) unit.
Measurement
The process we use to assign numbers to variables of individual population units.
Example: Measuring the preference for a food product by asking a consumer to rate the product’s taste on a scale from 1-10.
Census
When we measure a variable for every experimental unit of a population. “EVERYONE”.
Example: You’re doing a survey travel time by asking students at school.
> Asking everyone at school is a CENSUS of the school.
> But asking only 50 students is a SAMPLE of the school.
Sample
A subset of the units of a population.
Statistical inference
An estimate or prediction or some other generalization about a population based on information contained in a sample.
Example: The sample of 100 invoices, the auditor may estimate the total number of invoices containing errors in the population of 15,000 invoices. [Figure 1.2]
Reliability
How good the inference is.
- The only way we can be certain that an inference about a population is correct is to include the entire population (census?) in our sample.
Measure of reliability
A statement (usually quantified) about the degree of uncertainty associated with a statistical inference.
Four Elements of DESCRIPTIVE Statistical Problems
- The POPULATION or SAMPLE of interest
- one or more variables (characteristics of the population or experimental units that are to be investigated)
- Tables, graphs, or numerical summary tools
- Identification of patterns in data
Five Elements of INFERENTIAL Statistical Problems
- The population of interest
- One or more variables (characteristics of the population or experimental units) that are to be investigated
- The sample of population units
- The inference about the population based on information contained in the sample
- A measure of reliability for the inference.
[1.4] Process
A series of actions or operations that transforms inputs to outputs.
- A process produces or generates output over time.
Example: Processes of interest to businesses are those of production or manufacturing.
Black box
A process whose operations or actions are unknown or unspecified.
Sample
Any set of output (object or numbers) produced by a process is also called a sample.
Quantitative data
Measurements that are recorded on a naturally occurring numerical scale.
Example: The temperature (in degrees Celsius) at which each unit in a sample of 20 pieces of heat-resistant plastic begins to melt.
- Unemployment rare (%)
- Score of a sample on the GMAT or MCAT
- Number of females employed in each of a sample of 75 manufacturing companies
Qualitative Data (categorical)
Measurements that CANNOT be measured on a natural numerical scale; only classified into one of a group of categories.
Example:
- Political party affiliation
- Defective status (defective or indefective)
- Size of a car (subcompact, mid-size, full-size)
- A taste tester’s ranking (best, worst, etc.)
Three ways of obtaining data
- Published source
- Designed experiment
- Observational study (e.g., a survey)
[1.6] Published source
A book, journal, newspaper, or Web site.
Designed experiment
Researcher exerts full-control over the characteristics of the experimental units sampled.
Example: A group of experimental units that are assigned the treatment and an untreated (or control) group.
Observational study
Experimental units are OBSERVED in a natural setting. No attempt is made to control the experimental units sampled.
Example: Opinion polls, surveys.
What’s so significant about surveys?
Most common type of observational study, where the researcher samples a group of people to ask questions and record responses.
