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29 Cards in this Set
- Front
- Back
- 3rd side (hint)
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individual
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object described in a data set
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ex: people
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multiple individuals who aren't people
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cases
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variable
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any characteristic of a variable
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ex: a person's age, height, etc.
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quantitative value
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numerical
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ex: age
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categorical value
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category
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ex: blood type
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distribution
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tells us what values occur and how often those value occur for different variables
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ways to graph categorical data (1/2)
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bar graph
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each category is represented by a bar; x-axis: categories, y-axis: count (frequency) or percentage
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ways to graph categorical data (2/2)
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pie chart
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each side represents the percentage of the total; must add up to 1 (100%)
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ways to graph quantitative data (1/3)
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histograms - used to summarize data for a single variable
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x-axis: break the range of values into bins; y-axis: show the numerical value for each bin
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ways to graph quantitative data (2/3)
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stemplots - used to summarize data for a single variable
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stem: all but the final (right-most) digit; leaf: final digit
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trimming
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used for stemplots when there is an excess of data
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ways to graph quantitative data (3/3)
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timeplot - when data is ordered in a natural way
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ex: time; time on x-axis, numerical value on y-axis
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mean (x̄)
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average
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not resistant to outliers
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median (M)
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middle number
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if n is odd, M=middle #; if n is even, average 2 middle #s
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quartiles - Q₁
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25% of data lies below it, median of min & M
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quartiles - Q₃
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75% of data lies below it, median of max & M
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5 # summary
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min, Q₁, M, Q₃, max
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IQR - interquartile range
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Q₃ - Q₁
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IQR rule
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a point is an outlier if it is below Q₁-1.5(IQR) or above Q₃+1.5(IQR)
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standard deviation (s)
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describes variation around mean
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use it when mean is the appropriate measure of center
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variance
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s²
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density curve
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mathematical model of distribution
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nonnegative; area under curve is 1; can be any shape
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median (density curve)
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point that divides area in half
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mean (density curve)
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balance point
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normal distribution (bell curve)
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symmetric, bell shaped curve; mean: μ, sd: σ
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ex: N(μ,σ)
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68-95-99.7 rule
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68% of data lie within 1 σ of mean, 95% - 2 σ, 99.7% - 3 σ
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standard normal deviation N(0,1)
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mean=0, sd=1
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transform any normal curve N(μ,σ) into N(0,1)
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z-score
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measures the # of sd's that a value x is from the mean
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z=(x-μ)/σ
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normal quantile plot
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plot z-scores on x-axis; data on y-axis
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straight line: data is normal; curved up: right skewed; curved down: left skewed
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