ISYE 6414 - Midterm Exam Practice Questions and Answers
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Course
ISYE 6414
Institution
ISYE 6414
ISYE 6414 - Midterm Exam Practice
Questions and Answers
Regression Analysis - Answer️️ -Regression analysis is a simple way to investigate
the relationship between 2 or more variables in a non-deterministic way.
Response/Target Variable (Y) - Answer️️ -This is a variable we're intereste...
,ISYE 6414 - Midterm Exam Practice
Questions and Answers
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.
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, 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.
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