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| '''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. |
'''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. | ||
| == Descriptive methods == | == Descriptive methods == | ||
| Descriptive statistics describe a sample or population. They can be part of ]. |
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> | ||
| 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. | 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. | ||
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| == Inferential methods == | == Inferential methods == | ||
| Inferential methods allow us to infer from a sample to a population |
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== | ==See also== | ||
Revision as of 04:49, 22 July 2015
Univariate analysis is perhaps the simplest form of statistical analysis. Like other forms of statistics, it can be inferential or descriptive. The key fact is that only one variable is involved.
Descriptive methods
Descriptive statistics describe a sample or population. They can be part of exploratory data analysis.
The appropriate statistic depends on the level of measurement. For nominal variables, a frequency table and a listing of the mode(s) is sufficient. For ordinal variables the median can be calculated as a measure of central tendency and the range (and variations of it) as a measure of dispersion. For interval level variables, the arithmetic mean (average) and standard deviation are added to the toolbox and, for ratio level variables, we add the geometric mean and harmonic mean as measures of central tendency and the coefficient of variation as a measure of dispersion.
For interval and ratio level data, further descriptors include the variable's skewness and kurtosis.
Inferential methods
Inferential methods allow us to infer from a sample to a population. For a nominal variable a one-way chi-square (goodness of fit) test can help determine if our sample matches that of some population. For interval and ratio level data, a one-sample t-test 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 sign test and Wilcoxon signed rank test.
See also
References
- ^ Everitt, Brian (1998). The Cambridge Dictionary of Statistics. Cambridge, UK New York: Cambridge University Press. ISBN 0521593468.
- http://www.vassarstats.net/csfit.html