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28 Cards in this Set
- Front
- Back
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what types of variables are there
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NOMINAL/categroical
ORDINAL/ranking INTERVAL RATIO |
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Nominal and measurement properties
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discrete
no magnitude, equal interval, or zero ex: fruit |
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Ordinal and measurement properties
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discrete
has magnitude meaningful order |
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Interval and measurement properties
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continuous
has magnitude and equal intervals |
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raio and measurement properties
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has magnitude, equal intervals, and absolute zero
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properties of measurement
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magnitude: one obeservation of x is greater than, less than, or equal to another
equal intervals: the difference btwn one and two is the same as four and five absolute zero: value that indicates that none of x exists |
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what are the two branches of statistics
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descriptive and inferential statistics
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what question does descriptive statistics answer? definition?
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what does the data look like
def: organize, summarize, and communicate a group of numerical obesrvations |
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what question does inferential statistics answer? definition?
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what can we conclude about the population given the results of the sample
def: use sample data to make general estimates of larger population |
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what are three measures of center
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mean
median mode |
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what does the mean tell us
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the arithmetic average
-heavily influenced by extreme values -outliers pull mean to either side |
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what doe sthe median tell us
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the middle of the distribution of data when scores are arranged in ascending order
-not influenced by extreme scores and thus is sometimes a better measure of center |
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what does the mode tell us?
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the value is observed the most
-there can be multiple modes or zero modes |
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what are the three measures of spread/dispersion/variability?
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range
variance standard deviation |
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range
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lowest score to highest score
The range tells you the spread. If the range is small the data is bunched together, and if the range is larger then the data is spread apart more. |
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variance
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average of the squared deviations from the sample mean
how spread out the numbers are from the mean |
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standard deviation
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typical but not average
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when is a frequency table useful? whayt kind of info does it display?
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A frequency table is a way of summarising a set of data. It is a record of how often each value (or set of values) of the variable in question occurs. It may be enhanced by the addition of percentages that fall into each category.
A frequency table is used to summarise categorical, nominal, and ordinal data. |
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how can a freq table be made into a graph
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histogram, frequency polygon
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what is a grouped freq table
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The grouped frequency table is a statistic method to organize and simplify a large set of data in to smaller "groups." When a data consists of hundreds of values, it is preferable to group them in a smaller chunks to make it more understandable.
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purpose of grouped freq table
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The main purpose of the grouped frequency table is to find out how often each value occurred within each group of the entire data.
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what is expected relative frequency
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the expected number of occurrences.
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what type of graph is best
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It depends on the nature of your variables.
To describe a single variable on an interval/ratio scale – use a histogram or a frequency polygon. To describe the relationship between two interval/ratio variables – use a scatter plot or a line graph. To describe...between one (or two) nominal independent variables and an interval/ratio dependent variable – use a bar graph or a Pareto chart. |
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rules of graphing
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Minimize the data-to-ink ratio.
We want to display the most data possible with the least ink possible. When possible, use a range-frame, where the axes of your graphs only extend as far as the minimum and maximum scores of the variables. |
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null and alternative hypotheses
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Ho= The Null Hypothesis – This is the hypothesis that is tested, and it posits that there is no difference.
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alternative hypotheses
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H1= The Alternative Hypothesis – This is the one that predicts a difference.
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type I error
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false positives: this is when you reject the null hypothesis when it's actually true
-saying drug x reduce bp when it doens't |
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type II error
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false negative: this is when you fail to reject a null hypothesis when it's actually false
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