{"id":6,"date":"2022-05-23T17:37:18","date_gmt":"2022-05-23T21:37:18","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/?p=6"},"modified":"2022-05-24T16:24:11","modified_gmt":"2022-05-24T20:24:11","slug":"glossary","status":"publish","type":"back-matter","link":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/back-matter\/glossary\/","title":{"raw":"Glossary","rendered":"Glossary"},"content":{"raw":"","rendered":"
\n
Associated variables<\/dfn><\/dt>\n
\n

Variables that are associated with one another numerically. Also called dependent variables.<\/p>\n<\/dd>\n

Case<\/dfn><\/dt>\n
\n

An individual in a sample dataset on whom one or more variables have been measured. Also called an observational unit.<\/p>\n<\/dd>\n

Categorical variable<\/dfn><\/dt>\n
\n

A variable with values (levels) that represent categories.<\/p>\n<\/dd>\n

Causal relationship<\/dfn><\/dt>\n
\n

A relationship between variables in which changing the value of one variable has an affect on the value of another variable.<\/p>\n<\/dd>\n

Cohort<\/dfn><\/dt>\n
\n

A group of individuals (cases) who share some characteristic(s) (e.g., all born in the same year).<\/p>\n<\/dd>\n

Continuous variable<\/dfn><\/dt>\n
\n

A numerical variable that can take any numerical value within an interval (e.g., a decimal number between 0 and 1).<\/p>\n<\/dd>\n

Control group<\/dfn><\/dt>\n
\n

Cases in the sample who do not receive a specific treatment being investigated.<\/p>\n<\/dd>\n

Data<\/dfn><\/dt>\n
\n

Measurements of one or more variables on a sample of observations.<\/p>\n<\/dd>\n

Data matrix<\/dfn><\/dt>\n
\n

A common way to organize data in which the matrix rows represent cases (observational units) and the matrix columns represent variables.<\/p>\n<\/dd>\n

Dependent variables<\/dfn><\/dt>\n
\n

Variables that are associated with one another numerically. Also called associated variables<\/em>.\u00a0[The term \"dependent variable\" is sometimes used for a \"response variable,\" but that convention is not used in this course.]<\/em><\/p>\n<\/dd>\n

Discrete variable<\/dfn><\/dt>\n
\n

A numerical variable that can only take numerical values with gaps between them (e.g., 0, 1, 2, ...).<\/p>\n<\/dd>\n

Experimental study<\/dfn><\/dt>\n
\n

A study in which researchers conduct an experiment to investigate the possibility of a causal connection between variables by controlling the values of the explanatory variable(s) for selected cases.<\/p>\n<\/dd>\n

Explanatory variable<\/dfn><\/dt>\n
\n

When we suspect one variable might causally affect another, we label the first variable the
\nexplanatory variable. Also called a predictor variable<\/em>.<\/p>\n<\/dd>\n

Independent variables<\/dfn><\/dt>\n
\n

Two variables that are not associated and have no evident relationship.\u00a0[Do not mix this up with the concept of independent events in probability. Also the term \"independent variable\" is sometimes used for an \"explanatory variable,\" but that convention is not used in this course.]<\/em><\/p>\n<\/dd>\n

Levels<\/dfn><\/dt>\n
\n

The possible values of a categorical variable.<\/p>\n<\/dd>\n

Negative association<\/dfn><\/dt>\n
\n

An association between two numerical variable in which an increase in one variable tends to be associated with a decrease in the other variable.<\/p>\n<\/dd>\n

Nominal variable<\/dfn><\/dt>\n
\n

A categorical variable in which the levels have no meaningful, natural order.<\/p>\n<\/dd>\n

Numerical variable<\/dfn><\/dt>\n
\n

A variable that can take a range of numerical values, and it is meaningful to add, subtract, or take averages with those values. Also called a quantitative variable.<\/p>\n<\/dd>\n

Observational study<\/dfn><\/dt>\n
\n

A study in which researchers collect data in a way that does not directly interfere with how the data arise.<\/p>\n<\/dd>\n

Observational unit<\/dfn><\/dt>\n
\n

An individual in a sample dataset on whom one or more variables have been measured. Also called a case.<\/p>\n<\/dd>\n

Ordinal variable<\/dfn><\/dt>\n
\n

A categorical variable in which the levels have a meaningful, natural order.<\/p>\n<\/dd>\n

Placebo<\/dfn><\/dt>\n
\n

A \"sham\" treatment with no known impact on a response variable (e.g., a sugar pill).<\/p>\n<\/dd>\n

Positive association<\/dfn><\/dt>\n
\n

An association between two numerical variables in which an increase in one variable tends to be associated with an increase in the other variable.<\/p>\n<\/dd>\n

Predictor variable<\/dfn><\/dt>\n
\n

When we suspect one variable might causally affect another, we label the first variable the
\nexplanatory variable. Also called an explanatory variable<\/em>.<\/p>\n<\/dd>\n

Quantitative variable<\/dfn><\/dt>\n
\n

A variable that can take a range of numerical values, and it is meaningful to add, subtract, or take averages with those values. Also called a numerical variable.<\/p>\n<\/dd>\n

Randomized experiment<\/dfn><\/dt>\n
\n

An experiment in which individuals (cases) are randomly assigned to a group (e.g., \"treatment\" or \"control\").<\/p>\n<\/dd>\n

Response variable<\/dfn><\/dt>\n
\n

When we suspect one variable might causally affect another, we label the second variable the
\nresponse variable.<\/p>\n<\/dd>\n

Scatterplot<\/dfn><\/dt>\n
\n

A graph of two numerical variables in which each sample point is plotted at the intersection of the value of one variable on the horizontal axis and the other variable on the vertical axis.<\/p>\n<\/dd>\n

Statistics<\/dfn><\/dt>\n
\n

The study of how best to collect, analyze, and draw conclusions from data.<\/p>\n<\/dd>\n

Summary statistic<\/dfn><\/dt>\n
\n

A single number summarizing the values of a variable for a sample of observations.<\/p>\n<\/dd>\n

Treatment group<\/dfn><\/dt>\n
\n

Cases in the sample who receive a specific treatment being investigated.<\/p>\n<\/dd>\n

Variable<\/dfn><\/dt>\n
\n

A characteristic of an observational unit that has been measured.<\/p>\n<\/dd>\n<\/dl>\n","protected":false},"author":1591,"menu_order":1,"template":"","meta":{"pb_show_title":"","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"back-matter-type":[38],"contributor":[],"license":[],"class_list":["post-6","back-matter","type-back-matter","status-publish","hentry","back-matter-type-glossary"],"_links":{"self":[{"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/pressbooks\/v2\/back-matter\/6","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/pressbooks\/v2\/back-matter"}],"about":[{"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/wp\/v2\/types\/back-matter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/wp\/v2\/users\/1591"}],"version-history":[{"count":1,"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/pressbooks\/v2\/back-matter\/6\/revisions"}],"predecessor-version":[{"id":90,"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/pressbooks\/v2\/back-matter\/6\/revisions\/90"}],"metadata":[{"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/pressbooks\/v2\/back-matter\/6\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/wp\/v2\/media?parent=6"}],"wp:term":[{"taxonomy":"back-matter-type","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/pressbooks\/v2\/back-matter-type?post=6"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/wp\/v2\/contributor?post=6"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/introductiontoprobabilityandstatistics\/wp-json\/wp\/v2\/license?post=6"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}