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Lecture

STAB22-LEC08-(8,9).docx

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Department
Statistics
Course
STAB22H3
Professor
Ken Butler
Semester
Fall

Description
STAB22 LEC08 He covered C8 C9 but has not finished with C9 yet I do not use chapter headings for this note because he kind of mixed up the two chapters in his presentation of lecture slides 1 Car datayvarMPG miles per gallon xvarWeightexpectation heavier cars should have lower or worser MPG bc they use up more gas to drive in a given mile than does a lighter car First of all Is association straightfirst of all association is ve showing a downward trend just by looking at original scatterplot it looks like it is straight this is backed up by the correlation of 0903 96 REGRESSION FOR PREDICTING MPG FROM WEIGHTresults overall seem to show that results are good fitting for a straight lineone example would be R2 which came out as a high of about 82 R208155 97 Recall R2 meaningtells you to what extent is the reason that the yvariable varies because it dep on the xvariableis it majorly bc of the xvar that the yvar varies this would correspond to a high R2 oris the yvar not exhibiting variation primarily bc it dep on the xvar this would correspond to low R2 Recall extrapolation did a prediction for car weight that was beyond our data set the MPG for a car that weighs 6 tons note that our data only goes up to about 5 tons the result we got was a ve MPG which is entirely unreasonable it is not possible for a car to have a NEGATIVE gas mileageif we are trying to predict MPG beyond our data set or within we are assuming that straight line trend continues indefinitely but it will eventualy hit 0 and then go into the ve zone where the data in this context will be nonsensicalex does It make sense for a heavy car to have a negative gas mileage we can tell that things will not continue in a straight line decreasing pattern beyond about 55 tons based on the nonsensical ans we retrieved from plugging in 6 tons into our linear eqn 98CHECKING RESIDUAL PLOT Plot the residuals vs either the x or y var
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