Interpreting Geographical Data

Year one statistics exam

  • Summation sign
  • Rounding
  • Central tendency
  • Variability
  • Boxplots
  • Standard deviations
  • Normal distribution
  • Sampling
  • Reliability and standard errors
  • Confidence intervals and t-distribution
  • Colomn, charts and tables
  • Hypothesis testing and one sample t-test
  • Two sample t-test
  • F-test
  • Anova I
  • Anova II
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  • Created by: Sophie
  • Created on: 04-01-15 13:38

1. Definition of error

  • All different values
  • When cannot measure a value perfectly so automatically introduces uncertainty
  • A typical value
  • A value that lies within the values
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Other questions in this quiz

2. What is reliability?

  • The measure of how similar the sample mean is as an estimate to the population mean
  • The measure of how different the sample mean is as an estimate to the population mean
  • The measure of how the parameters of sample and population are the same

3. Which is not a feature of different samples?

  • There is not a significant difference between the mean and the small sample
  • 100 samples to 10 samples
  • So the smaller sample has committed a Type 1 error
  • There is a high significant value between the mean with the bigger samples

4. Which is not a feature of the analysis of variance?

  • he answer it gives you is about whether the means are the same or not.
  • ANOVA compares all the means with each other
  • What cant can accounted for by the difference- UNEXPLAINED VARIANCE/ WITHIN
  • Null Hypothesis- it is that all the means are the same.
  • It uses one overall variance. This is what allows there to be a single error rate, rather than many.
  • What can be accounted for by the difference- EXPLAINED VARIANCE/ BETWEEN
  • ANOVA makes similarities between the explained and unexplained variances.
  • Alternative Hypothesis- at least one of the means differs significantly from at least one of the others.

5. Which is not a feature of the F statistic graph?

  • The shape of the curve is defined by the degrees of freedom. The F distribution takes two d.f. values
  • F Test compares two means to see whether one is significantly different that the other
  • F-TEST compares two variances to see whether one is significantly bigger than the other
  • F distribution is bounded at 0, with all F-values having to be positive. Both Z and t can be either positive or negative, and the normal and t distributions go to infinity in both directions, this is only true
  • When both d.f. values are small it is very right-skewed. When both are large it looks a bit like the normal distribution. When one is small and the other large you get intermediate forms.
  • The right-skew in the F-distribution remains, even for very large samples. Both the t and normal distributions are symmetrical.


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