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. What happens when there is a different but we failed to detect it, Type II error?

  • Absence is not evidence
  • Absence of evidence is not the same as evidence of absence.
  • Absence is evidence
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Other questions in this quiz

2. What is interval data?

  • Where there are equal intervals of data on a continuous numerical scale, eg: farenheit
  • Where you allocate a score to a category and it indicates a group of data
  • Where there are equal intervals between the data and an absolute zero, eg: time
  • Where you rank items that you measure depending on which has a more or less of an influence that we want to measure. Intervals are not necessarily equal and there is not true zero point

3. ?Which statement is incorrect about Levene's Test?

  • It does the same thing (compares variances) but is more robust because it does not require normality of the data being tested.
  • Levene’s test is commonly used for the purpose of comparing variances in t-tests. For example, SPSS automatically does this test when you do a two-sample t-test.
  • However, Levene's test can be much less powerful at detecting differences in variance.
  • There is a variant of the T-test
  • There is a variant of the F-test

4. Which statement is incorrect about reporting CI?

  • The mean was 6.75 mm at 95% CI and N=100
  • All sample statistics have confidence intervals and a statistic such as a sample mean is only meaningful when reported with its CI (or SD or SE, depending on context).
  • 6.75 mm is less than mean length of rice grains in the sample is less than 7.15 mm (95% CI, N = 100).
  • The mean length of rice grains in the sample was 6.95 mm (95% CI: lower limit = 6.75 mm, upper limit = 7.15 mm, N = 100).
  • Without this information we cannot sensibly interpret the statistic. Note that it is good practice to report the sample size, for similar reasons.
  • The mean length of rice grains in the sample was 6.95 ± 0.20 mm (95% CI, N = 100).


  • 1) Coefficient of determination = (150.57 – 118.14) ÷ 150.57 = 0.21 aka 21%. SST-SSE=SSM
  • 2) In general, we can account for anywhere between 0% and 100% of the variation with a model.
  • Proportion explained
  • SSM/SST (sum of model squares divided by the overall mean)


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