ISYE 6414 - Midterm 1 ASSESSMENT TEST 2 Prep: Questions and Correct Answers Given
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Course
ISYE 6414
Institution
ISYE 6414
ISYE 6414 - Midterm 1 ASSESSMENT TEST 2 Prep: Questions and Correct Answers Given
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
r...
M
AO
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ISYE 6414 - Midterm 1
ASSESSMENT TEST 2 Prep:
JP
Questions and Correct
Answers Given
,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 in the predicting variable, or with other changes in the environment
fixed - ANS The predicting variable is a ___ variable. It is set fixed, before the
response is measured.
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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
Multiple Linear Regression - ANS A statistical method used to model the relationship
AO
between one dependent (or response) variable and two or more independent (or
explanatory) variables by fitting a linear equation to observed data
polynomial regression - ANS a regression model which does not assume a linear
relationship; a curvilinear correlation coefficient is computed (we can think of X and
X-squared as two different predicting variables)
three objectives in regression - ANS 1) Prediction
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2) Modeling
3) Testing hypothesis
Prediction - ANS We want to see how the response variable behaves in different
JP
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
Testing hypotheses - ANS of association relationships
useful representation of reality - ANS We do not believe that the linear model
represents a true representation of reality. Rather, we think that, perhaps, it provides a
___
, β0 - ANS intercept parameter (the value at which the line intersects the y-axis)
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
to find β0 and β1 - ANS to find the line that describes a linear relationship, such that
we fit this model.
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
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modeling framework for the simple linear regression: - ANS 1) identifying data
structure
2) clearly stating the model assumptions
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linear regression assumptions - ANS 1) linearity
2) constant variance assumption
3) independence assumption
linearity assumption - ANS mean zero assumption, means that the expected value of
the errors is zero.
A violation of this assumption will lead to difficulties in estimating β0, and means that
your model does not include a necessary systematic component.
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constant variance assumption - ANS which means that the variance (σ^2) of the error
terms or deviances is constant for the given population. A violation of this assumption
means that the estimates are not as efficient as they could be in estimating the true
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parameters
Independence Assumption - ANS which means that the deviances are independent
random variables.
Violation of this assumption can lead to misleading assessments of the strength of the
regression.
normality assumption - ANS errors (ε) are normally distributed. This is needed for
statistical inference, for example, confidence or prediction intervals, and hypothesis
testing. If this assumption is violated, hypothesis tests and confidence and prediction
intervals can be misleading.v
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