Graphical Descriptive Methods

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Lea_
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312031
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Graphical Descriptive Methods
Updated:
2015-11-25 01:13:29
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QBM117
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Week One
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  1. Descriptive Statistics
    Collecting, organising, summarising and presenting numerical data, to enable meaningful interpretation and to support decision making.

    • Graphs
    • Tables
    • Summary Measures
  2. Inferential Statistics
    Drawing conclusions about a population based on the sample information.
  3. Statistical Inference
    Using the sample statistic to estimate the parameter of interest.
  4. Population
    Is the entire collection of items about which in which information is required.
  5. Sample
    A subset of the population that we collect data from.
  6. Variable
    A characteristic of a population or sample that is of interest to us.
  7. Data
    The actual (or observed) values of variables.

    • Quantitative data - numerical observations.
    • Qualitative data - categorical.
  8. Levels of Measurement (LOM)
    Data can also be described in terms of the level of measurement attained.

    • Nominal
    • Ordinal
    • Interval
    • Ratio
  9. Qualitative Data (LOM)
    Nominal scale - classifies data into distinct categories in which no ordering is implied

    Ordinal - classifies data into distinct categories in which ordering is implied
  10. Quantitative Data (LOM)
    Interval Scale - an ordered scale in which the difference between measurements is a meaningful quantity that does not include a true zero point.

    Ratio Scale - an ordered scale in which the difference between two points includes a true zero point.
  11. Parameter
    Is a number that describes a population.

    • Population mean - μ
    • Population standard deviation - σ
    • Population proportion - ρ

    A parameter is a fixed number.
  12. Statistic
    Is a number that describes a sample.

    • Sample mean - x (- over)
    • Sample standard deviation - s
    • Sample proportion - p (^ over)

    A statistic is a variable whose value varies from sample to sample.
  13. Histogram
    A graphical summary of a set of data showing the number (frequency) of observations in each of several non-overlapping classes.

    • - Select the number of classes.
    • - Select an appropriate width for each class.
    • - Make sure that classes are non-overlapping and contain all observations.
  14. Relative Frequency Histogram
    Replace the class frequency on the axis by the class relative frequency.

    Class relative frequency = class frequency / total number of observations
  15. Relative Frequency
    Useful when comparing two or more populations (samples) especially when the number of observations in the samples are different (males to female).

    The vertical scale of the relative frequency is common allowing easy comparisons across different populations.
  16. Symmetry Histogram
    Said to be symmetric if, when we draw a vertical line down the centre, the two sides are identical in shape and size.
  17. Skewed Histogram
    Positively skewed - A long tail extending to the right, indicates few larger values and more smaller values.

    Negatively skewed - A long tail extending to the left, indicates few smaller values and more larger values.
  18. Number of Modes (Modal classes) Histogram
    Unimodal - Histogram with one peak.

    Bimodal - Histogram with two peaks, not necessarily the same height.

    Multimodal - Histogram has several peaks.
  19. Bell Shape Histogram
    Special type of symmetric unimodal histogram.
  20. Cumulative Frequency Distribution
    The number or proportion of observations less than or equal to some value.
  21. Ogive
    A graph of the cumulative relative frequency.

    The ogive is closed at the lower end by extending a straight line to the lower limit of the first class.

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