It quantitavely summarize a data set
Notes: example: pop: all 4th year students, 50 samples à derive quantitative measures (descriptive stat) to describe your samples
Descriptive Univariate Statistics – examination across of one variable at a time
- Frequency Distributions
- Graphic Representations or Graphing
- Distributional Shape
- Measures of Central Tendency
- Measures of Variability
Univariate and Bivariate
Univariate – 1 and only one dependent variable
Bivariate – more than one dependent variable
Frequency Distribution
-how many subjects were similar when measured on the dependent variable
- Simple or Ungrouped: No apparent grouping found in the presentation of data
- Grouped: Data are grouped by ranges
- Cumulative: The cumulative effects of data are shown as percentages
Graphic Representation or Graphing
Uses of Graphs
- Histogram – measured by the dependent variable, how many times does any given event occur
- Bar Graph – measured by the dependent variable, into what category or categories does the data fall
- Pie Chart – measured by the dependent variable, what percentage is assigned to a given subgroup.
Distributional Shape
- Normal Distribution – bell shaped, human height
- Skewed Distribution – positively skewed, negatively skewed
- Stat: Skew = near 0 == normal
- Positively skewed – few large values will tend to pull the average (mean) up – sales in dept store.
- Negatively Skewed – few small values will tend to pull the average (mean) down – test scores
- Bi-modal Distribution
Near to zero – more close to normal distribution
Measures of Central Tendency
- Mode – most frequent occurring data value
- Median – The data value that occurs at the midpoint of the distribution
- Mean – arithmetic average
Variablility –
- Defined as the measure of how scores differ from one another in a given distribution of scores.
- Range – difference between the low and the high scores in a given data set
- Variance and Standard Deviation (SD or )
Variance = SD2
Standard Deviation = squareroot (variance)
