ISYE 6414 - All Units Exam | Questions with 100%
Correct Answers | Verified | Latest Update 2026
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Terms in this set (427)
response (dependent) variables one particular variable that we are interested in
understanding or modeling (y)
predicting or explanatory a set of other variables that might be useful in
(independent) variables predicting or modeling the response variable (x1,
x2)
What kind of variable is a response random, because it varies with changes in the
variable and why? predictor/s along with other random changes.
What kind of variable is a predicting fixed, because it does not change with the
variable and why? response but it is fixed before the response is
measured.
linear relationship a simple deterministic relationship between 2
factors, x and y
what are three things that a 1. Prediction of the response variable, 2. Modeling
regression analysis is used for? the relationship between the response and
explanatory variables, 3. Testing hypotheses of
association relationships
B0 = ? intercept
, B1 = ? slope
for our linear model where: Y = B0 + deviance of the data from the linear model (error
B1 + EPSILON (E), what does the term)
epsilon represent?
what are the 4 assumptions of linear Linearity/Mean Zero, Constant Variance,
regression? Independence, Normality
Linearity/Mean zero assumption Means that the expected value (deviances) of
errors is zero. This leads to difficulties in estimating
B0 and means that our model does not include a
necessary systematic component
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. This
can result in less accurate parameters and poorly-
calibrated prediction intervals.
Assumption of Independence Means that the deviances, or in fact the response
variables ys, are independently drawn from the
data-generating process. (this most often occurs in
time series data) This can result in very misleading
assessments of the strength of regression.
Normality assumption This is needed if we want to do any confidence or
prediction intervals, or hypothesis test, which we
usually do. If this assumption is violated, hypothesis
test and confidence and prediction intervals and
be very misleading.
what are the 3 parameters we B0, B1, sigma squared (variance of the one pop.)
estimated in regression?
Correct Answers | Verified | Latest Update 2026
Save
Terms in this set (427)
response (dependent) variables one particular variable that we are interested in
understanding or modeling (y)
predicting or explanatory a set of other variables that might be useful in
(independent) variables predicting or modeling the response variable (x1,
x2)
What kind of variable is a response random, because it varies with changes in the
variable and why? predictor/s along with other random changes.
What kind of variable is a predicting fixed, because it does not change with the
variable and why? response but it is fixed before the response is
measured.
linear relationship a simple deterministic relationship between 2
factors, x and y
what are three things that a 1. Prediction of the response variable, 2. Modeling
regression analysis is used for? the relationship between the response and
explanatory variables, 3. Testing hypotheses of
association relationships
B0 = ? intercept
, B1 = ? slope
for our linear model where: Y = B0 + deviance of the data from the linear model (error
B1 + EPSILON (E), what does the term)
epsilon represent?
what are the 4 assumptions of linear Linearity/Mean Zero, Constant Variance,
regression? Independence, Normality
Linearity/Mean zero assumption Means that the expected value (deviances) of
errors is zero. This leads to difficulties in estimating
B0 and means that our model does not include a
necessary systematic component
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. This
can result in less accurate parameters and poorly-
calibrated prediction intervals.
Assumption of Independence Means that the deviances, or in fact the response
variables ys, are independently drawn from the
data-generating process. (this most often occurs in
time series data) This can result in very misleading
assessments of the strength of regression.
Normality assumption This is needed if we want to do any confidence or
prediction intervals, or hypothesis test, which we
usually do. If this assumption is violated, hypothesis
test and confidence and prediction intervals and
be very misleading.
what are the 3 parameters we B0, B1, sigma squared (variance of the one pop.)
estimated in regression?
