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23 Cards in this Set
- Front
- Back
inflection points
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the points at which the curve changes from falling more steeply to falling less steeply. They are located at a distance equal to the standard deviation on either side of the mean
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standardizing
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changing to units of size σ about μ as a center
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Z-score
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standardized value that tells hows many standard deviations the original observation falls away from the mean and in which direction z= (x-μ)/σ
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standard normal distribution
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N(0,1)- a normal distribution with a mean of 0 and a standard deviation of 1
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Z table
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the value in the table shows the proportion of observations to the left of the z score
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normal probability plot
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used to assess normality. if the data is approximately normal, the normal probability plot will be roughly linear.
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response variable
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dependent variable -y- measures the outcome of a study
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explanatory variable
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independent variable -x- attempts to explain the observed outcomes
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scatterplot
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shows the relationship between two quantitative variables
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correlation (r)
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measures the direction and strength of a linear relationship
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least-squares regression
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a method for finding a line that summarizes the relationship between two quantitative variables
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coefficient of determination (r^2)
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the fraction of variation in the values of y that is explained by the least-squares regression of y on x
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residual
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the difference between an observed value of the response variable and the value predicted by the regression line
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residual plot
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a scatterplot of the regression residuals against the explanatory variable
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outlier
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an observation that lies outside the overall pattern of teh other observations
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influential observation
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an observation whose removal markedly changes the result of the calculation. Points that are outliers in the x direction of a scatterplot are often influential for the LSRL
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transforming the data
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applying a function (i.e. log or square root) to a quantitative transforming variable
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extrapolation
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the use of a regression line for prediction far outside the domain of values of the explanatory variable
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lurking variables
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a variable that is not among the explanatory or response variables in a study and yet may influence the interpretation of relationships among those variables
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confounding
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two variables, either explanatory or lurking, are confounded when their effects on a response variable cannot be distinguished from each other
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two way table
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describes two categorical variables
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marginal distributions
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totals at the right and bottom of a two way table
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simpson's paradox
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reversal of the direction of a comparision or an association when data from several groups are combined to form a single group
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