If λ=1 - ANS we do not transform
non-deterministic - ANS Regression analysis is one of the simplest ways we have in statistics to investigate the relationship between two or more variables in a ___ way
random - ANS The response variable is a ___ variable, because it varies with changes...
non-deterministic - ANS Regression analysis is one of the simplest
ways we have in statistics to investigate the relationship between two
or more variables in a ___ way
random - ANS The response variable is a ___ variable, because it
varies with changes in the predicting variable, or with other changes in
K
the environment
C
fixed - ANS The predicting variable is a ___ variable. It is set fixed,
before the response is measured.
LO
simple linear regression - ANS regression analysis involving one
independent variable and one dependent variable in which the
relationship between the variables is approximated by a straight line
YC
Multiple Linear Regression - ANS A statistical method used to model
the relationship between one dependent (or response) variable and
two or more independent (or explanatory) variables by fitting a linear
D
equation to observed data
U
polynomial regression - ANS a regression model which does not
assume a linear relationship; a curvilinear correlation coefficient is
ST
computed (we can think of X and X-squared as two different predicting
variables)
three objectives in regression - ANS 1) Prediction
2) Modeling
3) Testing hypothesis
, Prediction - ANS We want to see how the response variable behaves
in different settings. For example, for a different location, if we think
about a geographic prediction, or in time, if we think about temporal
prediction
Modeling - ANS modeling the relationship between the response
variable and the explanatory variables, or predicting variables
K
Testing hypotheses - ANS of association relationships
C
useful representation of reality - ANS We do not believe that the
linear model represents a true representation of reality. Rather, we
LO
think that, perhaps, it provides a ___
β0 - ANS intercept parameter (the value at which the line intersects
the y-axis)
YC
β1 - ANS slope parameter (slope of the line we are trying to fit)
epsilon (ε) - ANS is the deviance of the data from the linear model
D
to find β0 and β1 - ANS to find the line that describes a linear
U
relationship, such that we fit this model.
ST
simple linear regression data structure - ANS pairs of data consisting
of a value for the response variable,and a value for the predicting
variable. And we have n such pairs
modeling framework for the simple linear regression: - ANS 1)
identifying data structure
2) clearly stating the model assumptions
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