Week 4 Study Notes

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13 Dec 2010
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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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