Statistics Chapters 1 & 2 Review

Statistics

The science of collecting, analyzing, and drawing conclusions from data.

Variability

The lack of consistency or fixed pattern, liability to vary or change.

Population

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.

Variable

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)

Frequency

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

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

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

Bias

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

Blocking

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.

placebo

something identical to the treatment group but contains no active ingredient