C784 Module 6: Correlation & Regression
lurking variable - ANS A variable that is not included in an analysis but that is related to two (or
more) other associated variables which were analyzed.
simple linear regression - ANS the prediction of one response variable's value from one
explanatory variable's value
Simpson's Paradox - ANS A counterintuitive situation in which a trend in different groups of
data disappears or reverses when the groups are combined.
degree - ANS The largest exponent in a mathematical expression or equation.
causation - ANS A relationship of cause and effect between two or more variables.
linear interpolation - ANS Estimation using the linear regression equation is between known
data points.
association - ANS A pattern or relationship between two variables.
coordinate plane - ANS A tool for graphing consisting of a horizontal x-axis and a vertical
y-axis.
regression equation - ANS An equation used to model the relationship between two quantitative
dependent and independent variables.
scatterplot - ANS A graph that uses dots on a coordinate plane to show the relationship
between variables.
Regression Analysis - ANS a statistical tool that quantifies the relationship betwn a response
variable and one or more explanatory variables
least squares - ANS A technique for finding the regression line.
slope-intercept form - ANS A common format for the equation of a line: y = mx + b, where m is
the slope and b is the y-intercept.
regression line - ANS The line of best fit to show the relationship between variables, the one
that minimizes distance from each data point to the line.
A linear regression equation takes the following form:
, y = mx^2 + b. True or False? - ANS false.
This is not the form that a linear regression equation takes.
Linear regression is always of degree 1, so the exponent of 2 associated with the x makes this a
nonlinear equation.
A linear regression "best-fit-line" can be estimated using least squares. True or False? - ANS
true.
Least squares estimation is the most common technique used to estimate the best-fit-line in
linear regression.
Linear extrapolation is always a reliable method of prediction. True or False? - ANS false.
Extrapolation assumes that the linear pattern of the data will continue outside of the range of
data points. This may not always be the case and therefore may not always be a reliable
method of prediction.
Linear interpolation is a technique used to make a prediction that falls between known data
points. True or False? - ANS true.
Linear interpolation is a technique used to make a prediction that falls between known data
points, using the linear regression equation.
Least squares estimation is a technique for predicting future data values. True or False? - ANS
false. Least squares estimation is a technique used to estimate the best-fit-line in linear
regression.
EXTRAPOLATE - ANS Using information from a data set to make predictions about data
outside of the original set.
POPULATION - ANS An entire pool from which a sample is drawn.
SAMPLE SIZE - ANS Statistics: the number of individuals measured or observed in a study.
Probability: number of possible outcomes in a trial or experiment.
Extrapolation is always inappropriate. True or False? - ANS false.
There are applications of extrapolation, and times in which it is necessary. Be mindful of the
situation and try to avoid inappropriate extrapolation by considering the context.
Which of the following statements is most appropriate with regards to representative samples?
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