plots each observation against the time at which it was measured
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5# Summary
minimum, Q1, median, Q3, maximum
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IQR
the distance between the first and third quartile
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IQR =
Q3 - Q1
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calculate lower outlier
Q1 - 1.5(IQR)
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calculate higher outlier
Q3 + 1.5(IQR)
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second method to calculate outliers
if value is located 2 or more SD’s above or below the mean
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standard deviation
average distance from the mean of all the values in a data set
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SD for a population
SD = sqrt \[ Σ ( x - μ )² / N \]
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SD for a sample
SD = sqrt \[ Σ(x - x̄)² / (n - 1) \]
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frequency table
gives the number of cases falling into each category
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relative frequency table
gives the proportion of cases falling into each category
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percentages, relative frequencies, and rates all provide the same information as
proportions
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bar charts(graphs) are used to display
frequencies for categorical data
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discrete variable
can take a countable number of values, could be finite or countably infinite
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continuous variable
can take on infinitely many values but values cannot be counted no matter how small the interval there is, there is always a value in between
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univariate
one main peak
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bimodal
two prominent peaks
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uniform
height is the same
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gap
region between two values with on data observed
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cluster
concentration of data separated by gaps
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statistic
numerical summary of sample data
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p^th percentile
value that has p% of data less than or equal to it
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variability
how spread out scores are in a distribution
\ The degree to which data points differ from each other or from the mean value. It can be measured by calculating the variance or standard deviation of a dataset.
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3 measures of variability
range, IQR, SD
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the empirical rule
This rule states that for a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, 95% falls within two standard deviations, and 99.7% falls within three standard deviations.