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formula quiz 3-4
Stack #75166
| Question | Answer |
|---|---|
| independent variable x is | called the explanatory variable |
| dependent varaible y is called | the response variable |
| scatterplots are usually analyzed according to | direction, form, strenth, and outliers |
| direction is | whether there isa positive assocaition, engative association or neither |
| form | clusters of points, linear patter |
| strength of the relationship | how close to a straight line do these points appear to lie |
| outliers | points that do not follow the gernal pattern of the data |
| correlation coeffiicent | measures the direction and strength of the linear relationship between two quanittiative varaibles |
| formula for r | i/n-1 times sigma xi-x ove sx times yi-y over sy |
| correlation coefficient r is always a number between and including | -1 and 1 |
| if r is positive then x and y are said to have a | positive assocaiting |
| if r=1 then x and y have a | perfect positive correlatrion |
| if r is negative then x and y have a | negative assocaition |
| if r= -1 then x and y have a | perfect negative correlationsghip |
| the closer r is to either 1 or -1 | the stronger the relationship is between the two variables |
| if r=o | there is no correlation between the two variables |
| teh correlation coefficent only measures | the existence and strength of linear relationships |
| the formula for the correlation coefficient is extremely | sensitive to outliers |
| the correalation coefficient has | no units |
| the corrleation coefficient is the same | regardless of which variable you consider to be the explanatory and which you consider to be the response |
| formula for lsqr | y hat equals naugth plus b1x |
| b1 is the | slope of the line |
| b1 formula is | r sy/sx |
| naught is | the y intercept of the line |
| banaught formula is | ybar minus b1xbar |
| teh diference btween y and yhat is caled | an error or a residual |
| a residual is | the observed value of y minus the prediceted value of y |
| the point xbar and ybar is a pooint | on every regression line |
| fsquared is called the | coefficient of determination and meaures the variation in y that is explained by y's linear assocaition with x |
| a residual plot | graphs the residuals on the vertical axis adn either the explanatory response or rpredicted response values on the horizontal acis. |
| residuals from a LSQR alwasy ahve a mean of | 0 |
| the horizontal axis | of a residual plot correspond to the regression line, which means trhat a residual point plotted onm the horizontal axis has a residual value of 0 |
| the correlation coefficient and the lsqr for a set of data | can be strongly influence by an outlying observation |
| an observation is influiential if | removing it would markedly change the position fo the rergression line |
| if the ordered pairs x,y ina d ata set dispalay a graph with an approxiamately exponetial shape then the graph of the ordred paris | x, log y will display a graph owith an approximately linear shape |
| if the ordered pairs x, y ina d ata set display a graph tyhat is approximately a power function then the graph of the ordered pairs | log x, log y will display a graph with an approxialmatley linear shape |
| if a function resembles a power function then it is reasonable hat the point | 0,0 should lieo n its graph |
| extrapolation is the | use of a regression line for prediction ioutside the range of balues of the explanatory variable x that yo used to obtain the line such predictions cannot be trusted |
| interpolation is the use | ogf a repgression line for a rpediction inside the range of the values of x, making it a more trustworthy procedure |
| association does not imply casuation | in othjer wrods, a strong correlation between two varaibles does not mean that a cause adn effect relationsip exists between them |
| a lurking variable is a variable that has | an important effecton the relationshi among the varibles in a study but is not included among the varaibles |
| a confounding varaible is a | lurking vatriable that affects only he response variable but creates a situation here it is impossible to dete4rmine whether ther affect on the respmse variable is caused by the explanatory variable, the confusing lurking variable, or neither |
Created by:
lilee256