GGR 270 – Lecture 4 – October 6, 2010

Normal Distribution – Z Scores

Standard scores are referred to as Z Scores

Indicate how many Standard Deviations separate a particular value from the mean

Z Scores can be positive or negative depending if they are > or < the mean

Z score of the mean is O and the Standard Deviation is positive or negative

Table of normal values provides probability info on a standardized scale

But, we can also calculate Z scores

Formula involves comparing values to the mean value, and dividing by the Standard

Deviation

Expressed as:

Result is interpreted as the ‘number of standard deviations an observation lays above

or below the mean’.

Skewness

Measures the degree of symmetry in a frequency distribution

Determines how evenly the values are distributed either side of the mean

Expressed as:

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Coefficient of variation

Allows for comparison of variability of spatial samples

Tests which sample has the greatest variability

Standard deviation or variance are absolute measures, so they are influenced by the

size of the values in the dataset

To allow a comparison of variation across 2 or more geographic samples, can use a

relative measure of dispersion called coefficient of variation

Expressed as:

Describing Bivariate data

Graphs

Comparative pie-charts

Stacked bar chart

Correlation

Allows us to observe statistically the relationship between 2 variables

Looking at the strength and direction of the relationship between 2 variables

Most common graphing technique is the scatter plot

Direction of the Bivariate relationship

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