2024-02-03T00:53:49+03:00[Europe/Moscow] en true <p>Variable</p>, <p>Discreet variable. What does it measure? </p>, <p>Continuous variable. What can it measure? </p>, <p>Nominal-level variable</p>, <p>Ordinal-level variable</p>, <p>Interval/ratio-level variable</p>, <p>Descriptive data</p>, <p>Inferential data</p>, <p>Frequency (f)</p>, <p>Frequency distribution</p>, <p>Cumulative frequency distribution</p>, <p>Cumulative percent distribution </p>, <p>Central tendency </p>, <p>Mode</p>, <p>Median</p>, <p>Mean</p>, <p>Percentile </p>, <p>Positively skewed distribution</p>, <p>Negatively skewed distribution</p>, <p>Measures of variability </p>, <p>Range</p>, <p>Interquartile range (IQR)</p>, <p>Variance </p>, <p>Standard deviation</p>, <p>Independent Variable (IV)</p>, <p>Dependent Variable (DV)</p>, <p>Population</p>, <p>Sample </p>, <p>Raw Frequency </p>, <p>Relative frequency</p> flashcards

Definitions

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  • Variable

    Basic unit of statistics; must take on more than one value and be measurable

  • Discreet variable. What does it measure?

    Variables that have a minimum unit of measurement that CANNOT be subdivided

  • Continuous variable. What can it measure?

    Variables that can take on all numerical values in an interval

  • Nominal-level variable

    It's values CANNOT be ranked in any meaningful way

  • Ordinal-level variable

    It's values CAN be ranked but the difference between the categories cannot be measured

  • Interval/ratio-level variable

    It's values CAN be ranked and the differences CAN be measured definitively

  • Descriptive data

    Complete data on a population, NO estimations or guessing needed

  • Inferential data

    Incomplete data on a population, inferred onto a population to make an ESTIMATED guess

  • Frequency (f)

    The number of times a given observation appears in a data set.

  • Frequency distribution

    A table that organizes how many observations fall into each category or interval of a variable

  • Cumulative frequency distribution

    The number of observations that are at or below each category or interval of a given variable

  • Cumulative percent distribution

    The percentage of observations that are at or below each category or interval of a given variable

  • Central tendency

    The most common value or middle value in the distribution of observations of a given variable

  • Mode

    The category with the greatest frequency or percentage of observations, not the frequency value itself

  • Median

    The middle most value in the distribution of observations of a given variable

  • Mean

    The most 'average' or likely observation in the distribution of a given variable

  • Percentile

    A specific type of measurement that represents what percentage of the distribution falls BELOW a specific score

  • Positively skewed distribution

    A distribution with a handful of extremely large values. The mean > median

  • Negatively skewed distribution

    A distribution with a handful of extremely low values. The mean < median

  • Measures of variability

    The diversity or dispersion in the distribution of observations of a given variable

  • Range

    The difference between the lowest and highest scores in a data set.

  • Interquartile range (IQR)

    The difference between the 25th and 75th percentile

  • Variance

    The difference between each observation and the mean of distribution of the observations

  • Standard deviation

    The square root of the variance, reverts data back to it's original metric

  • Independent Variable (IV)

    The CAUSE and variable that can be changed

  • Dependent Variable (DV)

    The EFFECT that's dependent on the cause

  • Population

    Every single member of a group of interest

  • Sample

    Small subset of an entire group of interest

  • Raw Frequency

    All given information in a set of data, plain number of observations in each category

  • Relative frequency

    Percentage representing the number of observations in a category relative to other categories