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Stack #54615
| Question | Answer |
|---|---|
| independent variable x is called | the explanatory variable |
| depedent variable y is called | the response variable |
| scatterplots are analyzed according to | direction, form, outliers, and strength |
| direction of the scatterplot is | whether there is apositive assocaition or negative or neither |
| form | clusters of points, linear pattern, etc |
| strength of the relationship | how close to a straight line do these poitns appear |
| outlioers | points that do not follow the geneartal pattern of th data |
| the correlation coefficient | measuers the direction and strength of the linear relationship between two quantitative variables |
| formula for r | 1/n-1 sigma (xi-x)/sx (yi-y)sy |
| correlation coefficient is always | between -1 and 1 |
| if r is positive then x and y | have a positive assocaition |
| if r=1 then x and y | have a perfect positive correlation |
| if r is negative than | x and y have a negative association |
| if r= -1 | then x and y have a perect negative correlation |
| the c;oser r is to either 1 or -1 | the strnoger the relationship fo the variabels |
| r=0 | no correlation |
| the formula for the correlation coefficient is | extremely sensitive to outliers |
| the correlation coefficient has | no units |
| the correlation coefficient is the same regardless of | which variavle you consdier to be the explantory and which you consider to be the response |
| formula for least squares regression line | yhat=bnaught+b1x |
| b1 equation | b1=rsy/sx |
| b0 is | the y=intercept of the line |
| b0 equatrion | ybar-bixbar |
| residual equation | y-yhat |
| a point on every regression line is | xbar, ybar |
| rsquared is called | the coefficient of determination |
| r2 measures | the variation in y that is explained by y's linear assocaitoon with x |
| residual plots graphs on the vertial axis and either the explanatory response or predicted | response values on the horizontal axis |
| residuals from a LSQR have a mean of | 0 |
| influential | an observation si influential if removing it would markedly change the position of the regression line |
| 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 |
| if a function resembles a power functionthen | it is reasonable that the point (0,) lies on its graph |
| 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 |
| interplotation | is the use of regressionj line for prediction wsindie of the range of the values x |
| association does not imply causation | in other words, a strong correlation between two varaibles does not mean that a cause and effect relatioship exists |
| 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 |
| 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 |
Created by:
lilee256