Statistics Chapters 1 & 2 Review

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36 Terms
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The science of collecting, analyzing, and drawing conclusions from data.
The lack of consistency or fixed pattern, liability to vary or change.
The entire collection of individuals or objects about which info is desired.
Rule of population and samples
You can take information from a sample to judge the population, but not information from population to judge sample.
Descriptive Stats
The methods of organizing + summarizing data. Often organized w/ a graph, range, average, etc.
Inferential Stats
Involves making generalizations from sample to population.
Any characteristic whose value may change from one individual to another.
Types of Variables
Categorical and Numerical
Numerical variables
Quantitative; observations or measurements that take on numerical values; can be averaged
Discrete numerical
solid number
Continuous numerical
decimal, in-between, can be broken down, not definite
Categorical variables
Qualitative; identifies basic characteristics of the population.
Univariate, Bivariate, Multivariate
data that describes a single characteristic, two characteristics, and more than two (respectively)
Bar chart
Use with CATEGORICAL data; place equal-width rectangular bars above each category label w/ a height determined by its frequency or relative frequency.
Relative frequency
Parts out of a whole (ex: 2/16, 3/16)
the number of occurrences within a given time period, just whole quantities.
Dot plot
use with small numerical data sets
Observational Study
A study in which the researcher observes characteristics of a sample selected from one or more populations. Can be generalized if randomly selected, but cannot show cause-effect relationships because of confounding variables.
Confounding variables
variables that can affect the outcome of your experiment
A study in which the researcher observes how a response variable behaves when one or more explanatory variables (factors) are manipulated. Can show cause-effect relationships, but cannot be generalized if it is not random or if it requires volunteers.
Simple Random Sample (SRS)
A sample of size N is selected from the population in a way that ensures that every different possible sample of the desired size has the same chance of being selected.
Stratified Random Sample
Population is divided into non-overlapping subgroups called strata
Groups that are similar based on some characteristics; where simple random samples are selected from
Cluster sampling
population is divided into non-overlapping subgroups called clusters
Systematic Sampling
One of the first k individuals is selected at random, then every kth individual in the sequence is included in the sample
The tendency for samples to differ from the corresponding population in some systematic way
Selection Bias
Occurs when the way the sample is selected systematically excludes part of the population. (undercoverage)
Convenience sampling
using an easily available or convenient group to form a sample
Response variable
variable that is not controlled by the experimenter and is measured as part of the experiment
Explanatory variable
variables that have values that are controlled by the experimenter
Experimental condition
any particular combination of the explanatory variables (also called treatments)
Experimental units
the smallest unit to which a treatment is applied
Extraneous variable
a variable that is not one of the explanatory variables but is thought to affect the response; needs to be controlled
process by which an extraneous variable's effects are filtered out, similar groups called blocks are created. all treatments must be tried in each block.
Single-blind experiment
subjects do not know which treatment they are in.
something identical to the treatment group but contains no active ingredient