ISYE 6644 MIDTERM EXAM NEWEST 2024 COMPLETE
100 QUESTIONS AND CORRECT DETAILED ANSWERS
(VERIFIED ANSWERS) |ALREADY GRADED
A+||BRAND NEW VERSION!
Regression Analysis - ANSWER-Regression analysis is a simple way to investigate the relationship
between 2 or more variables in a non-deterministic way.
Response/Target Variable (Y) - ANSWER-This is a variable we're interested in understanding,
modeling or testing
This is a random variable. It varies with changes in the predictor(s)
2. Predicting/Explanatory (independent) Variables(Xs ~ X1, X2) - ANSWER-These are variables
we think might be useful in predicting or modeling the response variable
This is a fixed variable. It does not change with the response
Simple Linear Regression - ANSWER-We have a straight line which doesn't fit perfectly to the
points
The objective is to fit a non-deterministic linear model between the predicting variable and Y.
In simple linear regression, we have 3 parameters to estimate.
Multiple Linear Regression - ANSWER-We can have a plane if we have two predictions
Polynomial Regression - ANSWER-We are capturing a nonlinear relationship
Objectives of Linear Regression - ANSWER-1. Prediction: We want to see how the response
variable behaves in different settings
2. Modeling: We are interested in modeling the relationship between the response variable and
the explanatory/predicting variables
, 3. Testing: We are also interested in testing the hypotheses of association relationships.
Simple Linear Regression Assumptions - ANSWER-• Linearity/Mean Zero Assumption: This
means that the expected value of the errors is zero
• Constant Variance Assumption: This means that the variance of the error term is equal to
sigma_squared is the same across all error terms
• Independence Assumption: This means that the error terms are independent random
variables i.e. deviances (response variables Ys) are independently drawn from the data
generating process -- it cannot be true that the model under-predicts Y for one particular case
tells you anything or all about what it does for any other case
• Normal Assumption: The errors are assumed to be normally distributed.
Linearity Assumption - ANSWER-A violation of this assumption will lead to difficulties in
estimating 0 and means that your model does not include a necessary systematic component
Constant Variance Assumption - ANSWER-This means that the model cannot be more accurate
in some parts and less accurate in other parts of the model. The variance has to be constant.
A violation of this assumption means that the estimates are not as efficient as they could be in
estimating the true parameters and better estimates can be calculated also results in poorly
calibrated prediction intervals
Independence Assumption - ANSWER-It cannot be true that the model under-predicts Y. One
particular case doesn't tell you anything or all about what it does for any other case
This violation most often occurs in data that are ordered in time (time series data) where areas
that are near each other in time are similar to each other.
Violation of this assumption can lead to very misleading assessments of the strength of the
regression
100 QUESTIONS AND CORRECT DETAILED ANSWERS
(VERIFIED ANSWERS) |ALREADY GRADED
A+||BRAND NEW VERSION!
Regression Analysis - ANSWER-Regression analysis is a simple way to investigate the relationship
between 2 or more variables in a non-deterministic way.
Response/Target Variable (Y) - ANSWER-This is a variable we're interested in understanding,
modeling or testing
This is a random variable. It varies with changes in the predictor(s)
2. Predicting/Explanatory (independent) Variables(Xs ~ X1, X2) - ANSWER-These are variables
we think might be useful in predicting or modeling the response variable
This is a fixed variable. It does not change with the response
Simple Linear Regression - ANSWER-We have a straight line which doesn't fit perfectly to the
points
The objective is to fit a non-deterministic linear model between the predicting variable and Y.
In simple linear regression, we have 3 parameters to estimate.
Multiple Linear Regression - ANSWER-We can have a plane if we have two predictions
Polynomial Regression - ANSWER-We are capturing a nonlinear relationship
Objectives of Linear Regression - ANSWER-1. Prediction: We want to see how the response
variable behaves in different settings
2. Modeling: We are interested in modeling the relationship between the response variable and
the explanatory/predicting variables
, 3. Testing: We are also interested in testing the hypotheses of association relationships.
Simple Linear Regression Assumptions - ANSWER-• Linearity/Mean Zero Assumption: This
means that the expected value of the errors is zero
• Constant Variance Assumption: This means that the variance of the error term is equal to
sigma_squared is the same across all error terms
• Independence Assumption: This means that the error terms are independent random
variables i.e. deviances (response variables Ys) are independently drawn from the data
generating process -- it cannot be true that the model under-predicts Y for one particular case
tells you anything or all about what it does for any other case
• Normal Assumption: The errors are assumed to be normally distributed.
Linearity Assumption - ANSWER-A violation of this assumption will lead to difficulties in
estimating 0 and means that your model does not include a necessary systematic component
Constant Variance Assumption - ANSWER-This means that the model cannot be more accurate
in some parts and less accurate in other parts of the model. The variance has to be constant.
A violation of this assumption means that the estimates are not as efficient as they could be in
estimating the true parameters and better estimates can be calculated also results in poorly
calibrated prediction intervals
Independence Assumption - ANSWER-It cannot be true that the model under-predicts Y. One
particular case doesn't tell you anything or all about what it does for any other case
This violation most often occurs in data that are ordered in time (time series data) where areas
that are near each other in time are similar to each other.
Violation of this assumption can lead to very misleading assessments of the strength of the
regression
