Stats

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Population

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The entire collection of individuals or objects you want to learn about

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Sample

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Part of the population that is selected for study

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Observational study

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Person carrying out the study does not control who or what is in the population

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Population characteristic or parameter

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Number that describes an entire population

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Sample size

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Number that describes a sample

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Simple random sample

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Sample collected from a population in such a way that every different possible sample has n has an equal chance of being selected

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Selection bias

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When the sample systematically and excludes some part of the population of Interest

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Measurement bias

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When method of observation tends to produce values that differ from the True Values

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Non response bias

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Responses are not obtained from all individuals selected

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Variable

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Any characteristic who is Valium a chain from one individual to another

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Data

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Result for making observations either on a single variable or simultaneously on two or more variables

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Univeriable

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A data set consisting of observations on a single characteristic

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Numerical data is

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Quantitative

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Categorical data is

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Qualitative

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Discrete variable

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If the possible values correspond to isolated points on a number line example whole numbers

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Continuous variables

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If the value can be measured in many different ways that it can go on forever

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Frequency

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Number of times a category appears in a data set

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Relative frequency equation

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Frequency ÷ # of observations

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Scatterplot

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Shows relationship between two or more variables

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Time-series plot

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Graph of data collected over time that can help you see interesting Trends or patterns

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Measure of center

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Describe where the data distribution is located along a number line. A measure of center provides info about what is typical

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Measure of spread

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Describe how much variability there is in a data distribution. A measure of spread provides information about how much individual values tend to differ from one another

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If the data distribution is approximately symmetric use

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Mean and standard deviation

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If the shape of the data distribution is skewed or has outliers use

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median and interquartile range

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Sample mean=

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Sum of x ÷ # of values

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Population mean

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Average of every member of the population only if you have a census of the population

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Sample variance=

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Sum of all variables × (variable-mean)÷ # of variable - 1

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Sample standard deviation=

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Avg distance from the mean

Square root of s squared

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Population variance=

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Sum of variables × (variable-mean) squared ÷ size of population

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Outlier

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Any data point that is less than the lower quartile - 1.5×IQR. Any data point greater than upper quartile + 1.5×IQR

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Z-score

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How many standard deviations the data value is from the mean

Variable - mean ÷ standard deviation