1
Q
What is the first statistic?
A
- Standard Deviation
2
Q
What is Standard Deviation?
A
- It is one of the most important descriptive statistics as it gives a more accurate figure than a range or interquartile range
- This is because it considers all figures and is not affected by extreme values.
- It also shows the extent of the variations from the mean.
3
Q
What does the Standard Deviation do differently from the range?
A
- It measures the spread of data from the mean, while the range measures the two extreme values in the data set.
4
Q
What is the second statistic?
A
- Chi Squared
5
Q
What is Chi Squared?
A
- This can test the associations between variables.
- It is one of the most widely used and versatile tests of association.
6
Q
What can Chi Squared be compared with?
A
- Significance tables
- This is to confirm whether the difference between the observed data and the expected data is a chance effect or a statistical significance.
7
Q
What should the Chi Squared data have?
A
- Sample should include 20 observations in each area.
- The data should be in the form of frequencies.
- The data have must precise numerical value, for example you cannot use %’s.
- The data must be organised into categories or groups.
- The expected frequency in one category should be greater than 5 or else the statistic won’t work.
8
Q
What is the third statistic?
A
- Nearest Neighbour Analysis
9
Q
What is Nearest Neighbour Analysis? (NNA)
A
- It can be used to identify a tendency towards clustering or dispersion of data-sets.
10
Q
What is the 3 types the data could be?
A
- Clustered = 0, the dots are close to the same point
- Random = 1.0, there is no pattern
- Regular = 2.15, perfectly uniformed pattern.
11
Q
What is are the disadvantages of NNA?
A
- It cannot always distinguish between single and multi-clustered distribution.
- An index of 1.0 doesn’t always mean a random distribution as it may be related to a second unmapped factor e.g villages may give a 1.0 but they may be situated around springs.
12
Q
What is the fourth statistic?
A
- Spearman’s Rank Correlation Coefficient
13
Q
What is Spearman’s?
A
- The test is quick and can be easily calculated in comparison to Pearson’s.
- The test uses data which can be ranked but this means that it loses some of its accuracy as it is not using the actual values.
14
Q
What are the advantages + disadvantages of Spearman’s?
A
- It can only test for linear relationships so a scatter graph could be drawn to see if this is the case.
- It requires a sample size of at least 7 observations, however, the longer the sample size, the more reliable the result.
15
Q
What is the fifth statistic?
A
- Pearson’s Product Movement Correlation Coefficent
16
Q
What is Pearson’s?
A
- It is the most widely used correlation method.
- It tests the strength of correlation between two variables.
- It is a more powerful method than Spearman’s because it uses the actual values instead of ranks to examine the relationship between two variables.
17
Q
What can we do with Pearson’s and what do we need?
A
- Ideally need 12-15 data values in order to be an efficient statistic.
- Can draw scatter graph of the results to visually confirm there is a correlation between two data sets.
18
Q
What is the final statistic?
A
- Linear Regression
19
Q
What is Linear Regression?
A
- This is the most commonly used method of predictive analysis to determine the linear relationship between one independent variable and one dependent variable.
20
Q
What does Linear Regression allow us to calculate?
A
- It allows us to understand how much the dependent value will change when we change the independent variable.
- The regression equation allows us to calculate the best-fit line on the scatter graph mathematically and by doing this, it’s more certain than plotting it by eye.
- Allows us to predict trends + future values.
Advanced Higher Geography
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