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18 Cards in this Set
- Front
- Back
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Scatterplot
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Picture of bivariate numerical data in which each observation (x, y) is represented as a point located w.r.t. a horizontal x axis and a vertical y axis
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Residuals
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Obtained by subtracting each predicted value from the corresponding observed y value
These are the vertical deviations from the least-squares line (best fit) |
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Principle of Best Fit
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Method used to select a line that summarizes an approx. linear relationship between x and y
Best-fit line is the line that minimizes the sum of the squared vertical deviations for the points in the scatterplot |
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Slope of best-fit line
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m = (y - b) / x
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Intercept of best-fit line
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b = y - mx
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Residual plot
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Scatterplot of the (x, residual) pairs. Isolated points or a pattern of points in a residual plot are indicative of potential problems
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Residual Sum of Squares (SSResid)
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The sum of the squared residuals is a measure of y variation that cannot be attributed to an approximate linear relationship (unexplained variation)
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Equation for SSResid
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SSResid = Sum (y - y-hat)^2
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Total Sum of Squares (SSTo)
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Sum of squared deviations from the sample mean, a measure of total variation in the observed y values
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Equation for SSTo)
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SSTo = Sum (y - y-bar)^2
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Coefficient of Determination (r^2)
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The proportion of variation in observed y's that can be attributed to an approximate linear relationship
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Equation for r^2
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r^2 = 1 - (SSResid/SSTo)
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Transformation
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A simple function of the x and/or y variable, which is then used in a regression
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Chance Experiment
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Any experiment for which there is UNCERTAINTY concerning the resulting outcome
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Sample Space
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The collection of all possible outcomes from a chance experiment
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Event
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Any collection of possible outcomes from a chance experiment
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Simple Event
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Any event that consists of a single outcome
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Disjoint (mutually exclusive) events
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Events that have no outcomes in common
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