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33 Cards in this Set
- Front
- Back
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name the two ways of summarising data |
skewness and kurtosis |
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binomial distribution |
theprobability distribution of a binomial variable is called binomial distribution |
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inferential statistics |
used to reach conclusions that extend beyond the immediate data alone. |
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descriptive statistics |
to describe what is going on in our data |
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what does the standard deviation and variance measure? |
the width of the data |
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what does the standard deviation and the variance not measure? |
the shape of the frequency distribution |
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what is it called when data is clumped in one area on a graph? |
skewed data asymmetrical |
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what does the variance measure? |
measures the distance from the mean |
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what does the variance not describe ? |
the curve of the data (the skewness) |
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what the mean fails to describe ? |
the distribution, doesn't tell you where your data is on the graph, where it clumps on the scale of ranges. |
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define skewness |
a measurement of the symmetry of the distribution |
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when is said to be positive skewness |
when the mean is greater than the median |
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when is there said to be negative skewness |
when the mean is lower than the median |
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when is there no skewness |
when data is perfectly distributed |
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3 reasons why skewness is useful? |
testing assumptions about the data, whether it has a normal distribution, whether other statistical tests are valid |
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what 2 things do both skewness and kurtosis do? |
show how symmetrical at the data and test assumptions about data |
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what is called when data is symmetrical? |
normal distribution |
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what is kurtosis? |
how peaky the model value (the value that appears most frequently) to the rest of the population. |
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what causes spurious precision? |
when there are lots of decimal points |
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what is spurious precision also known as? |
false precision |
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inferential statistics are built on what? |
probability theory |
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why is the probability useful when describing statistics? |
allow us to characterise and make confident statements about populations, to test hypothesis statically. e.g. the probability of flooding |
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the risk and likelihood of things happening can be determine through working out the |
probability |
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what are independent probabilities |
wo events are independent if the occurrence of one does not affect the probability of the other. |
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The dependent variable |
The dependent variables represent the output or outcome whose variation is being studied. |
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The independent variable |
The independent variables represent inputs or causes, i.e. potential reasons for variation. |
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mutual independence |
knowing outcome does not help predict the next outcome |
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conditional probabilities |
where actions o affect outcomes, where these actions are not independent |
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is the probability of flooding conditional? why? |
yes, because what happened before changes the probability of the event occurring. |
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how do you determine the recurrence periods in years? |
x m3/s = (number of peaks in list) + 1 (divided by) ranked position of that particular discharge. |
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what is the next step after the equation to determine the river discharge recurrence intervals? (the probability of the discharge occurring) |
see how frequently one specific discharge occurs over a 100 year period. (this produces the probability of a discharge occurring. |
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the terminology used for probability has changed from likelihood terminology to... |
confidence terminology |
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3 examples of famous probability mistakes gone wrong |
cancer clusters, gamblers fallacy, the monty hall problem |
