Measures of central tendancy- Mean, median and mod
- Simple and easy
- Mode can be used with non numerical data
- Median- very large and very small numbers do not affect result
- Mean- useful in making measurements more accurate
- Cant use discontinuous data
- Median and mode do not account for whole set of data
- Mean is easily disorted by very large/small anomalies
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- Interquatile range is the spread of values around the median
- Find out the LQ and the HQ and the difference is the IQR
- Not affected by the outliers
- Not all data considered
- Complicated to work out
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- the average amount by which the values in a data set vary from the mean
- Calculate the mean and minus it from X
- Square each of the answers and add up total
- Then divide by n and square root
- Low standard deviation means little range and therefore reliable mean
- More reliable measure of dispersal as it uses all the data
- Can be greatly affected by outliers
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- Formulate a null hypothesis.
- Individually rank the values of each variable. 1 = highest value.
- Find the difference between the two.
- Square the differences and sum the values.
- Input into the formula.
- Indicates the statistical significance of a result - rules out chance.
- Gives numerical value to the strength and direction of a correlation.
- Does not show if there is a casual link
- Too many tied ranks affect the validity of the test.
- Subject to human error.
- Only appropriate for data with 10-30 values with 2 variables that are believed to be related
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Mann Whitney U
- Select null hypothesis.
- Rank the data sets across the two columns. 1 = lowest value.
- Treat as two seperate columns. Add ranks in first column to get your R1 value then add ranks in the second column to get your R2 value.
- Input int the formula.
- Choose the smaller U value of either U1 or U2.
- Compare to the critical values table: less than the critical value means you should reject the null hypothesis at 95% confident. Greater than the critical value - accpet the nul.
- Used to show if there is a statistical difference between two sets of data e.g. size of rocks in upper course and lower course.
- Does not explain cause and effect.
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- Identify null hypothesis - no significant difference between observed an expected.
- Subtract observed frequencies from expected and square the result.
- Divide this by the expected value for that group.
- Compare with degrees of freedom: on the critical values chart, the degree will be one less than the total number of observed values.
- To assess the degree of difference between observed and theoretical data e.g. number of pebbles along a river.
- Statistical significance of results can be tested.
- Doesn't explain why there is a pattern.
- Does not give the strenght of the relationship.
- Percentages cannot be used.
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