Univariate analysis: Difference between revisions

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			#REDIRECT ]
	'''Univariate analysis''' is perhaps the simplest form of ]. Like other forms of statistics, it can be ] or ]. The key fact is that only one variable is involved.

			{{rcatsh\|
	== Descriptive methods ==
			{{r sec}}

			{{r hist}}
	Descriptive statistics describe a sample or population. They can be part of ].<ref name = "Everitt">{{cite book \| last = Everitt \| first = Brian \| title = The Cambridge Dictionary of Statistics \| publisher = Cambridge University Press \| location = Cambridge, UK New York \| year = 1998 \| isbn = 0521593468 }}</ref>
			{{r wikidata}}

			}}
	The appropriate statistic depends on the ]. For nominal variables, a ] and a listing of the ] is sufficient. For ordinal variables the ] can be calculated as a measure of ] and the ] (and variations of it) as a measure of dispersion. For interval level variables, the ] (average) and ] are added to the toolbox and, for ratio level variables, we add the ] and ] as measures of central tendency and the ] as a measure of dispersion.

	For interval and ratio level data, further descriptors include the variable's ] and ].

	== Inferential methods ==

	Inferential methods allow us to infer from a sample to a population.<ref name = "Everitt" /> For a nominal variable a one-way chi-square (goodness of fit) test can help determine if our sample matches that of some population.<ref>http://www.vassarstats.net/csfit.html</ref> For interval and ratio level data, a ] can let us infer whether the mean in our sample matches some proposed number (typically 0). Other available tests of location include the one-sample ] and ].

	==See also==
	* ]
	* ]
	* ]

	==References==
	{{reflist}}

	]

Latest revision as of 01:17, 13 May 2024

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