ISyE 6402 Midterm Exam Questions with Verified
Correct Answers
If λ=1
we do not transform
non-deterministic
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
The response variable is a ___ variable, because it varies with changes in the predicting
variable, or with other changes in the environment
fixed
The predicting variable is a ___ variable. It is set fixed, before the response is measured.
simple linear regression
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
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
,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
1) Prediction
2) Modeling
3) Testing hypothesis
Prediction
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
modeling the relationship between the response variable and the explanatory variables, or
predicting variables
Testing hypotheses
of association relationships
useful representation of reality
We do not believe that the linear model represents a true representation of reality. Rather, we
think that, perhaps, it provides a ___
β0
intercept parameter (the value at which the line intersects the y-axis)
β1
, slope parameter (slope of the line we are trying to fit)
epsilon (ε)
is the deviance of the data from the linear model
to find β0 and β1
to find the line that describes a linear relationship, such that we fit this model.
simple linear regression data structure
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:
1) identifying data structure
2) clearly stating the model assumptions
linear regression assumptions
1) linearity
2) constant variance assumption
3) independence assumption
linearity assumption
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
Correct Answers
If λ=1
we do not transform
non-deterministic
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
The response variable is a ___ variable, because it varies with changes in the predicting
variable, or with other changes in the environment
fixed
The predicting variable is a ___ variable. It is set fixed, before the response is measured.
simple linear regression
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
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
,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
1) Prediction
2) Modeling
3) Testing hypothesis
Prediction
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
modeling the relationship between the response variable and the explanatory variables, or
predicting variables
Testing hypotheses
of association relationships
useful representation of reality
We do not believe that the linear model represents a true representation of reality. Rather, we
think that, perhaps, it provides a ___
β0
intercept parameter (the value at which the line intersects the y-axis)
β1
, slope parameter (slope of the line we are trying to fit)
epsilon (ε)
is the deviance of the data from the linear model
to find β0 and β1
to find the line that describes a linear relationship, such that we fit this model.
simple linear regression data structure
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:
1) identifying data structure
2) clearly stating the model assumptions
linear regression assumptions
1) linearity
2) constant variance assumption
3) independence assumption
linearity assumption
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
