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Lecture 13

STA 205 Lecture Notes - Lecture 13: Dependent And Independent Variables, Point Estimation, Prediction Interval


Department
Statistics
Course Code
STA 205
Professor
Brooke Buckley
Lecture
13

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develop alinear model that
can be used for prediction
and estimate
response the variable we
want to model ly
dependent
perdictor the variable
hypothesised to have an
effect on the response x
independent

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Packet 13: Correlation & Regression
After completing this material, you should be able to:
identify the response and predictor variables
interpret a scatterplot in context
obtain the correlation from StatCrunch and interpret the strength of the linear relationship
use StatCrunch output to obtain the line of best fit and interpret the coefficients (where appropriate)
use a hypothesis test to determine if there is a significant linear relationship between two variables
use the regression equation for prediction
interpret the appropriate interval (confidence interval or prediction interval) for a given scenario.
Goal:
Because we now have two quantitative variables collected, we need a way to distinguish between them:
response variable:
predictor variable:
Example: A consumer organization would like to develop a model to predict a car’s gas mileage (in miles per gallon) based
upon its weight (in thousands of pounds). In order to do this, a random sample of 50 cars was selected with weights
ranging between 1900 pounds and 4000 pounds.
What two variables were recorded for this sample of 50 cars?
To graphically inspect the relationship between these two variables, we can create a scatterplot. When given a
scatterplot, the following three characteristics should be
discussed:
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Aweight lbs if gas mileage gal
predictor response
direction of the
relationship

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1direction of the relationship
positive as ary9
negative as xRYd
2type of relationship
pattern
linear
non linear
3unusual observations
that Do Not follow the
trend
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