ISYE 6414 - MIDTERM 1 PREP Questions with Correct Solutions
Question:
1. If λ=1
Answer:
we do not transform
Question:
2. 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
Question:
3. random
Answer:
The response variable is a ___ variable, because it varies with changes in the predicting
variable, or with other changes in the environment
Question:
4. fixed
Answer:
The predicting variable is a ___ variable. It is set fixed, before the response is measured.
Question:
5. 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
Question:
6. 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
,Question:
7. 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)
Question:
8. three objectives in regression
Answer:
1) Prediction
2) Modeling
3) Testing hypothesis
Question:
9. 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
Question:
10. Modeling
Answer:
modeling the relationship between the response variable and the explanatory variables, or
predicting variables
Question:
11. Testing hypotheses
Answer:
of association relationships
Question:
12. 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 ___
, Question:
13. β0
Answer:
intercept parameter (the value at which the line intersects the y-axis)
Question:
14. β1
Answer:
slope parameter (slope of the line we are trying to fit)
Question:
15. epsilon (ε)
Answer:
is the deviance of the data from the linear model
Question:
16. to find β0 and β1
Answer:
to find the line that describes a linear relationship, such that we fit this model.
Question:
17. 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
Question:
18. modeling framework for the simple linear regression:
Answer:
1) identifying data structure
2) clearly stating the model assumptions
Question:
19. linear regression assumptions
Answer:
1) linearity
2) constant variance assumption
3) independence assumption
Question:
1. If λ=1
Answer:
we do not transform
Question:
2. 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
Question:
3. random
Answer:
The response variable is a ___ variable, because it varies with changes in the predicting
variable, or with other changes in the environment
Question:
4. fixed
Answer:
The predicting variable is a ___ variable. It is set fixed, before the response is measured.
Question:
5. 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
Question:
6. 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
,Question:
7. 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)
Question:
8. three objectives in regression
Answer:
1) Prediction
2) Modeling
3) Testing hypothesis
Question:
9. 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
Question:
10. Modeling
Answer:
modeling the relationship between the response variable and the explanatory variables, or
predicting variables
Question:
11. Testing hypotheses
Answer:
of association relationships
Question:
12. 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 ___
, Question:
13. β0
Answer:
intercept parameter (the value at which the line intersects the y-axis)
Question:
14. β1
Answer:
slope parameter (slope of the line we are trying to fit)
Question:
15. epsilon (ε)
Answer:
is the deviance of the data from the linear model
Question:
16. to find β0 and β1
Answer:
to find the line that describes a linear relationship, such that we fit this model.
Question:
17. 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
Question:
18. modeling framework for the simple linear regression:
Answer:
1) identifying data structure
2) clearly stating the model assumptions
Question:
19. linear regression assumptions
Answer:
1) linearity
2) constant variance assumption
3) independence assumption
