## Advanced Statistics - Biology 6030 |

## Bowling Green State University, Fall 2019 |

**Atribute:**nominal, categorical (e.g., country of origin for graduate students in the biological sciences). Logical operators (e.g., equal, not equal) apply but mathematical operators do not. Numbers can be used to represent frequency of atributes**Rank:**ordinal, discrete: variable that exists along a continuum along which only certain values are possible (e.g., number of graduate students in the biological sciences). Comparison operators hold (e.g., <, >, =) but some mathematical ones (e.g., *, /) dont**Measurement:**measures a continuously subdivisible parameter (e.g., height measure of graduate students in the biological sciences). All logical, comparative and mathematical operators apply**Interval**, continuous, cardinal: Scores stand in a quantitative relationship to one another, where adjacent scores are separated by an equal interval.**Ratio**: like interval but with a true zero value (e.g., height, speed)

- Stats for
**characterizing collections**of something - Descriptive statistics **Variables**or**constants**: instances- A
**random variable**is a numerical outcome of an experiment in which we measure an entity **Accuracy**,**precision, resolution**and**significant digits**: A result is considered accurate if it is consistent with the true or accepted value for that result. The precision of a result is an indication of how sharply it is defined. Accuracy is a measure of reliability, and is the difference between the True Value of a measured quantity and the Most Probable Value which has been derived from a series of measures. Precision is a measure of repeatability, i.e. the degree of agreement between individual measurements of a set of measurements, all of the same quantity. Resolution. This is the smallest interval measurable by an instrument.**Independence**of datapoints,**Pseudoreplication**,**Degrees of freedom**- match
**Distributions**and**VariableTypes** **Parametric statistics**make assumptions about distributions. They are more powerful as long as the asumtptions are not violated- Central Tendency: Mean
- Dispersion: variance, standard deviation, standard error
- Shape: Kurtosis, Skewness

**Non-parametric statistics**do not make assumptions about the distribution.- Central tendency: median
- Dispersion: minimum, maximum, range, quartiles, interquartile range, quantiles, percentiles

**Sum**:= x_{1}+ x_{2}+ x_{3}+ x_{4}+ ... + x_{n}**Product**:= x_{1}* x_{2}* x_{3}* x_{4}* ... * x_{n}**Factorial**: n**! =**n * n-1 * n-2 * n-3 ...

last modified: 3/12/08

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