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Stats test guidance

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Learn how to make maps with real data

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Create and share an evacuation map to prepare for an incoming hurricane.

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Finding location co-ordinates for GIS Mapping

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Latitude and Longitude Finder on Map Get Coordinates

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Lat long is a geographic tool which helps you to get latitude and longitude of a place or city and find gps coordinates on map, convert lat long, gps, dms and utm.

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Step by step from data to map!

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Guidance for GIS

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Trends and patterns can be difficult to recognise, especially when a large number of data points are presented on a map. To help make sense of the information that you have collected, GIS software offers a range of tools to summarise raw data, carry out calculations and find spheres of influence.

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FSC data analysis guidance

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1. Statistical tests

Spearman's Rank Correlation Test

Spearman's Rank Correlation is a statistical test to test whether there is a significant relationship between two sets of data.

The Spearman's Rank Correlation test can only be used if there are at least 10 (ideally at least 15-15) pairs of data.

There are 3 steps to take when using the Spearman's Rank Correlation Test

Step 1. State the null hypothesis

There is no significant relationship between _______ and _______

Step 2. Calculate the Spearman's Rank Correlation Coefficient

`r_s = 1-(6∑D^2) / (n(n^2-1))`

`r_s` = Spearman's Rank correlation coefficient

`D` = differences between ranks

`n` = number of pairs of measurements

Step 3. Test the significance of the result

Compare the value of `r_s` that you have calculated against the critical value for `r_s` at a confidence level of 95% / significance value of p = 0.05.

If `r_s` is equal to or above the critical value (p=0.05) the REJECT the null hypothesis. There is a SIGNIFICANT relationship between the 2 variables.

A positive sign for `r_s` indicates a significant positive relationship and a negative sign indicates a significant negative relationship.

If `r_s` (ignoring any sign) is less than the critical value, ACCEPT the null hypothesis. There is NO SIGNIFICANT relationship between the 2 variables.

Chi-squared test

Chi squared in a statistical test that is used either to test whether there is a significant difference, goodness of fit or an association between observed and expected values.

`chi^2 = ∑ (O-E)^2 / E`

The chi squared test can only be used if

the data are in the form of frequencies in a number of categories (i.e. nominal data).

there are more than 20 observations in total

the observations are independent: one observation does not affect another

There are 3 steps to take when using the chi squared test

Step 1. State the null hypothesis

There is no significant association between _______ and _______

Step 2. Calculate the chi squared statistic

`chi^2 = ∑ (O-E)^2 / E`

`chi^2` = chi squared statistic

`O` = Observed values

`E` = Expected values

Step 3. Test the significance of the result

Compare your calculated value of `chi^2` against the critical value for `chi^2` at a confidence level of 95% / significance value of P = 0.05, and appropriate degrees of freedom.

`"Degrees of freedom" = ("number of rows" - 1) xx ("number of columns" - 1)`

If Chi Squared is equal to or greater than the critical value REJECT the null hypothesis. There is a SIGNIFICANT difference between the observed and expected values.

If Chi Squared is less than the critical value, ACCEPT the null hypothesis. There is NO SIGNIFICANT difference between the observed and expected values.

Mann Whitney U test

Mann Whitney U is a statistical test that is used either to test whether there is a significant difference between the medians of two sets of data.

The Mann Whitney U test can only be used if there are at least 6 pairs of data. It does not require a normal distribution.

There are 3 steps to take when using the Mann Whitney U test

Step 1. State the null hypothesis

There is no significant difference between _______ and _______

Step 2. Calculate the Mann Whitney U statistic

`U_1= n_1 xx n_2 + 0.5 n_2 (n_2 + 1) - ∑ R_2`

`U_2 = n_1 xx n_2 + 0.5 n_1 (n_1 + 1) - ∑ R_1`

`n_1` is the number of values of `x_1`

`n_2` is the number of values of `x_2`

`R_1` is the ranks given to `x_1`

`R_2` is the ranks given to `x_2`

Step 3. Test the significance of the result

Compare the value of U against the critical value for U at a confidence level of 95% / significance value of P = 0.05.

If U is equal to or smaller than the critical value (p=0.05) the REJECT the null hypothesis. There is a SIGNIFICANT difference between the 2 data sets.

If U is greater than the critical value, then ACCEPT the null hypothesis. There is NOT a significant difference between the 2 data sets.

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Spearman's Rank Correlation is a statistical test to test whether there is a significant relationship between two sets of data. The Spearman's Rank Correlation test can only be used if there are at least 10 (ideally at least 15-15) pairs of data.

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Ready-made worksheets for data analysis

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Summary of the SMP for easy understanding!

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Shoreline Management Plan for West of Wales Area D