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WGU C784 MODULE 6 CORRELATION & REGRESSION EXAM QUESTIONS WITH VERIFIED ANSWER,100%CORRECT

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WGU C784 MODULE 6 CORRELATION & REGRESSION EXAM QUESTIONS WITH VERIFIED ANSWER A variable that is not included in an analysis but that is related to two (or more) other associated variables which were analyzed. - CORRECT ANSWER lurking variable the prediction of one response variable's value from one explanatory variable's value - CORRECT ANSWER simple linear regression A counterintuitive situation in which a trend in different groups of data disappears or reverses when the groups are combined. - CORRECT ANSWER Simpson's Paradox The largest exponent in a mathematical expression or equation. - CORRECT ANSWER degree A relationship of cause and effect between two or more variables. - CORRECT ANSWER causation Estimation using the linear regression equation is between known data points. - CORRECT ANSWER linear interpolation A pattern or relationship between two variables. - CORRECT ANSWER association A tool for graphing consisting of a horizontal x-axis and a vertical y-axis. - CORRECT ANSWER coordinate plane An equation used to model the relationship between two quantitative dependent and independent variables. - CORRECT ANSWER regression equation

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WGU C784 MODULE 6 CORRELATION &
REGRESSION EXAM QUESTIONS WITH
VERIFIED ANSWER
A variable that is not included in an analysis but that is related to two (or more)
other associated variables which were analyzed. - CORRECT ANSWER lurking
variable


the prediction of one response variable's value from one explanatory variable's
value - CORRECT ANSWER simple linear regression


A counterintuitive situation in which a trend in different groups of data disappears
or reverses when the groups are combined. - CORRECT ANSWER Simpson's
Paradox


The largest exponent in a mathematical expression or equation. - CORRECT
ANSWER degree


A relationship of cause and effect between two or more variables. - CORRECT
ANSWER causation


Estimation using the linear regression equation is between known data points. -
CORRECT ANSWER linear interpolation


A pattern or relationship between two variables. - CORRECT ANSWER association

,A tool for graphing consisting of a horizontal x-axis and a vertical y-axis. - CORRECT
ANSWER coordinate plane


An equation used to model the relationship between two quantitative dependent
and independent variables. - CORRECT ANSWER regression equation


A graph that uses dots on a coordinate plane to show the relationship between
variables. - CORRECT ANSWER scatterplot


a statistical tool that quantifies the relationship baton a response variable and one
or more explanatory variables - CORRECT ANSWER Regression Analysis


A technique for finding the regression line. - CORRECT ANSWER least squares


A common format for the equation of a line: y = mx + b, where m is the slope and
b is the y-intercept. - CORRECT ANSWER slope-intercept form


The line of best fit to show the relationship between variables, the one that
minimizes distance from each data point to the line. - CORRECT ANSWER
regression line


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 non-linear equation. - CORRECT ANSWER A linear regression
equation takes the following form:


y = mx^2 + b. True or False?


true.


Least squares estimation is the most common technique used to estimate the
best-fit-line in linear regression. - CORRECT ANSWER A linear regression "best-fit-
line" can be estimated using least squares. True or False?


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. - CORRECT ANSWER Linear
extrapolation is always a reliable method of prediction. True or False?


true.


Linear interpolation is a technique used to make a prediction that falls between
known data points, using the linear regression equation. - CORRECT ANSWER
Linear interpolation is a technique used to make a prediction that falls between
known data points. True or False?

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