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stats ch6
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Terms in this set (16)
quantitative variables
variables with numbers as values
what is the goal of a quantitative variable
examine the relationship ( if there is one ) between our two quantitative variables
there are two quantitative variables
response (y axis)--> measures out come of the study and what we are interested in learning about
explanatory (x
axis) --> use to explain the values of the response variable
ex: driving distance and money won
response variable--> money won
explanatory variable --> driving distance
what do we need to interpret a scatterplot ?
form ? linear curved overall pattern
strength? how close or far the dots are from the line
outliers ? are there points outside the general pattern
direction? positive or negative
correlation coefficient what does it measure and what does it tell us ?
measure of the strength of the linear relationship between two quantitative variables
it also tells us the direction of the relationship
correlation coefficient is r
what are the properties of r ?
r has no units it is just a number
measures the linear relationship between two quantitative variables
can be affected by outliers
r can only be a value between what to numbers?
-1 and 1
r=1 means ?
perfect straight increasing line
r=-1
perfect straight line decreasing
r=0
there is no linear relationship
what happens as r moves away from zero ?
the linear relationship between the two variables gets stronger
what are the 3 main adjectives used to describe strength of relationship ?
strong moderate weak
these depend on the numeric value of r
association is not ...?
causation
only because two variables are associated (relationship between 2 variables )does not mean ..?
one causes the other there could be other lurking variables that are the cause
what is a lurking variable?
there could be other variables that we have not looked at that could be the driving force in what is going on
variables other than the two in the two in scatterplot can affect the relationship between the two variables
the other variables are called the lurking variables
how we can show better causation ?
we can control all outside variables and change only the values of the explanatory variables then you can conclude that changes in the explanatory variable causes changes in the response variable
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