Stack #54615
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| independent variable x is called | the explanatory variable
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| depedent variable y is called | the response variable
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| scatterplots are analyzed according to | direction, form, outliers, and strength
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| direction of the scatterplot is | whether there is apositive assocaition or negative or neither
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| form | clusters of points, linear pattern, etc
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| strength of the relationship | how close to a straight line do these poitns appear
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| outlioers | points that do not follow the geneartal pattern of th data
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| the correlation coefficient | measuers the direction and strength of the linear relationship between two quantitative variables
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| formula for r | 1/n-1 sigma (xi-x)/sx (yi-y)sy
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| correlation coefficient is always | between -1 and 1
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| if r is positive then x and y | have a positive assocaition
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| if r=1 then x and y | have a perfect positive correlation
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| if r is negative than | x and y have a negative association
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| if r= -1 | then x and y have a perect negative correlation
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| the c;oser r is to either 1 or -1 | the strnoger the relationship fo the variabels
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| r=0 | no correlation
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| the formula for the correlation coefficient is | extremely sensitive to outliers
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| the correlation coefficient has | no units
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| the correlation coefficient is the same regardless of | which variavle you consdier to be the explantory and which you consider to be the response
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| formula for least squares regression line | yhat=bnaught+b1x
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| b1 equation | b1=rsy/sx
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| b0 is | the y=intercept of the line
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| b0 equatrion | ybar-bixbar
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| residual equation | y-yhat
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| a point on every regression line is | xbar, ybar
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| rsquared is called | the coefficient of determination
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| r2 measures | the variation in y that is explained by y's linear assocaitoon with x
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| residual plots graphs on the vertial axis and either the explanatory response or predicted | response values on the horizontal axis
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| residuals from a LSQR have a mean of | 0
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| influential | an observation si influential if removing it would markedly change the position of the regression line
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| logarithmic transformation | if the ordered pair (x,y) in a data set display a graph with an approximately exponetial shape then the graph of the ordered pairs (x, logy) will disaplay a graph with an approximately linaer shape
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| if a function resembles a power functionthen | it is reasonable that the point (0,) lies on its graph
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| extrapolation | is the use of a regression line for predictin outside of the values of the explanatory variable x tht you used to ontain the line
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| interplotation | is the use of regressionj line for prediction wsindie of the range of the values x
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| association does not imply causation | in other words, a strong correlation between two varaibles does not mean that a cause and effect relatioship exists
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| a lurking varaible | is a variable that has an important effect on the relationship among the varaibels in a study but is not included among the varaibles
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| a confounding variable | is a lurking variable that affects onylt he response variabvle but creates a situation where it is impossible to determine whether the affect on the response variable is casued by the expkanatory variable, the confounding lurking variable, or neither
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