ISYE6414 MIDTERM ASSESSMENT REVIEW SHEET CERTIFICATION QUESTIONS AND SOLUTIONS COMPLETE PREPARATION VERIFIED

ISYE6414 MIDTERM ASSESSMENT REVIEW
SHEET CERTIFICATION QUESTIONS AND
SOLUTIONS COMPLETE PREPARATION
VERIFIED

●● Which regression variable is a Random variable?.
Answer: Response Variable - It varies with changes in the predictor
along with other random changes


●● Which regression variable is a Fixed variable?.
Answer: Predicting Variable - It does not change with the response but it
is set fixed before the response is measured.


●● What are the objectives in regression analysis?.
Answer: 1. Prediction - of the response variable
2. Modelling - the relationship between the response variable and the
explanatory variables
3. Testing - hypotheses of association relationships.


●● What are the given assumptions when building a linear regression
model?.

,Answer: 1. Linearity/Mean Zero Assumption - it cannot be true that for
certain subgroups in the population, the model is consistently too low,
while for others, it's consistently too high.


2. Constant Variance Assumption - means that it cannot be true that the
model is more accurate for some parts of the population, and less
accurate for other parts of the populations.


3. Independence Assumption are independent random variables - it
cannot be true knowing that the model under-predicts y for one
particular case tells you anything or all about what it does for any other
case. (her language)


4. Normally distributed.


●● What is the value that is being optimized towards in a linear
regression problem?.
Answer: Minimizing the sum of squared errors


●● In terms of model parameter interpretation, how would you interpret
a positive value for B1, negative value, and value close to 0?.
Answer: A positive value of B1 is consistent with a direct relationship
between x and y

,A negative value of B1 is consistent with an inverse relationship
between x and y


A close to zero value of B1 means that there is not a significant
association between x and y


●● What are the interpretations of the Least Squares estimated
coefficients (B hat 1 and B hat 0).
Answer: B hat 1 is the estimated expected change in the response
variable associated with the unit of change in the predicting variable


B hat 0 is the estimated expected value of the response variable when the
predicting variable equals zero


●● What is extrapolation?.
Answer: When you try to predict a value using your regression model
that is outside of the observed range. It is unreliable to use extrapolation.


●● Assuming that the data are normally distributed, under the simple
linear model, the estimated variance has the following sample
distribution:


A) Chi-square with n-2 degrees of freedom
B) T-distribution with n-2 degrees of freedom

, C) Chi-square with n degrees of freedom
D) T-distribution with n degrees of freedom.
Answer: A) Chi-square with n-2 degrees of freedom


●● The fitted values are defined as:


A) The difference between observed and expected responses
B) The regression line with parameters replaced with the estimated
regression coefficients
C) The regression line
D) The response values..
Answer: B) The regression line with parameters replaced with the
estimated regression coefficients.


●● The estimators of the linear regression model are derived by:


A) Minimizing the sum of squared differences between observed and
expected values of the response variable.
B) Maximizing the sum of squared differences between observed and
expected values of the response variable.
C) Minimizing the sum of absolute differences between observed and
expected values of the response variable.
D) Maximizing the sum of absolute differences between observed and
expected values of the response variable..