ISYE6414 MIDTERM COMPREHENSIVE
CERTIFICATION TEST PAPER QUESTIONS
AND SOLUTIONS GRADED A PLUS
VERIFIED
●● non-deterministic.
Answer: 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.
Answer: The response variable is a ___ variable, because it varies with
changes in the predicting variable, or with other changes in the
environment
●● fixed.
Answer: The predicting variable is a ___ variable. It is set fixed, before
the response is measured.
●● simple linear regression.
Answer: 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.
Answer: 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 equation to observed data
●● polynomial regression.
Answer: 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.
Answer: 1) Prediction
2) Modeling
3) Testing hypothesis
●● Prediction.
Answer: 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.
Answer: modeling the relationship between the response variable and
the explanatory variables, or predicting variables
,●● Testing hypotheses.
Answer: of association relationships
●● useful representation of reality.
Answer: We do not believe that the linear model represents a true
representation of reality. Rather, we think that, perhaps, it provides a ___
●● β0.
Answer: intercept parameter (the value at which the line intersects the y-
axis)
●● β1.
Answer: slope parameter (slope of the line we are trying to fit)
●● epsilon (ε).
Answer: is the deviance of the data from the linear model
●● to find β0 and β1.
Answer: to find the line that describes a linear relationship, such that we
fit this model.
●● simple linear regression data structure.
, Answer: 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:.
Answer: 1) identifying data structure
2) clearly stating the model assumptions
●● linear regression assumptions.
Answer: 1) linearity
2) constant variance assumption
3) independence assumption
●● linearity assumption.
Answer: 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.
●● constant variance assumption.
Answer: 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 parameters
CERTIFICATION TEST PAPER QUESTIONS
AND SOLUTIONS GRADED A PLUS
VERIFIED
●● non-deterministic.
Answer: 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.
Answer: The response variable is a ___ variable, because it varies with
changes in the predicting variable, or with other changes in the
environment
●● fixed.
Answer: The predicting variable is a ___ variable. It is set fixed, before
the response is measured.
●● simple linear regression.
Answer: 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.
Answer: 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 equation to observed data
●● polynomial regression.
Answer: 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.
Answer: 1) Prediction
2) Modeling
3) Testing hypothesis
●● Prediction.
Answer: 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.
Answer: modeling the relationship between the response variable and
the explanatory variables, or predicting variables
,●● Testing hypotheses.
Answer: of association relationships
●● useful representation of reality.
Answer: We do not believe that the linear model represents a true
representation of reality. Rather, we think that, perhaps, it provides a ___
●● β0.
Answer: intercept parameter (the value at which the line intersects the y-
axis)
●● β1.
Answer: slope parameter (slope of the line we are trying to fit)
●● epsilon (ε).
Answer: is the deviance of the data from the linear model
●● to find β0 and β1.
Answer: to find the line that describes a linear relationship, such that we
fit this model.
●● simple linear regression data structure.
, Answer: 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:.
Answer: 1) identifying data structure
2) clearly stating the model assumptions
●● linear regression assumptions.
Answer: 1) linearity
2) constant variance assumption
3) independence assumption
●● linearity assumption.
Answer: 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.
●● constant variance assumption.
Answer: 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 parameters
