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. How do we calculate the F test?

  • You have two double the P value as we need to allow for the fact that either of the variances could have been bigger. "=FDIST(0.53/0.197,99,99)*2"
  • F = variance 1 ÷ variance 2. That is, F is simply the ratio of the variances. For this reason, the F-test is often called the ‘variance ratio test’. You use the degrees of freedom samples of the numerator and denominator
  • F= variance 1 x variance 2
  • F = mean 1 / mean 2.
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Other questions in this quiz

2. Which is not a characteristic of an ANOVA I within country extreme example?

  • When we can account of overall means as each country means, the country means are not significantly different, but are instead the same.
  • When we can account for all the variance by using country means instead of overall means, the country means are significantly different.
  • All the variation (this time SST=1) is within-country variation (SSE=1) and there is no between-country variation (SSM=0).

3. Which statement is incorrect about error bars on Excel?

  • Excel only knows the value of the mean and not the other data sets
  • It will either add the wrong error bars or add none at all as does not obtain the whole dataset
  • You have to do is calculate the SD, SE or CI yourself and then enter it in this ‘custom’ option, specifying your correct value as both the upward-extending bar (‘positive’) and the downward one (‘negative’).
  • You can use the option of standard error or standard deviation in Excel
  • Make colomn first and then add the error bars yourself

4. Definition of central tendancy

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

5. Which statement is incorrect about the FITTED VALUE?

  • Our model uses this information, as best it can, to produce a prediction (fitted value) for any rice grain
  • These fitted values can be compared with the actual (or OBSERVED) values.
  • The best our model can do is predict that any given rice grain’s length will not be the mean grain length of rice from the same country – as judged by the grains in our sample.
  • These values come from the best fit procedure, the mean is the single value that best describes the data, it fits the data better than any other single value


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