ISYE 6414 - UNIT 1- TEST QUESTIONS WITH ACCURATE SOLUTIONS

ISYE 6414 - UNIT 1- TEST QUESTIONS WITH ACCURATE
SOLUTIONS


Response (dependent) variables -- Answer ✔✔ one particular variable that we are
l, l, l, l, l, l, l, l, l, l, l,




l, interested in understanding or modeling (y) l, l, l, l, l,




Predicting or explanatory (independent) variables -- Answer ✔✔ a set of other variables
l, l, l, l, l, l, l, l, l, l, l, l,




l, that might be useful in predicting or modeling the response variable (x1, x2)
l, l, l, l, l, l, l, l, l, l, l, l,




What kind of variable is a response variable and why? -- Answer ✔✔ random, because it
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, varies with changes in the predictor/s along with other random changes.
l, l, l, l, l, l, l, l, l, l,




What kind of variable is a predicting variable and why? -- Answer ✔✔ fixed, because it does
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, not change with the response but it is fixed before the response is measured.
l, l, l, l, l, l, l, l, l, l, l, l, l,




Linear relationship -- Answer ✔✔ a simple deterministic relationship between 2 factors, x
l, l, l, l, l, l, l, l, l, l, l, l,




l, and y l,




What are three things that a regression analysis is used for? -- Answer ✔✔ 1. Prediction of
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, the response variable, 2. Modeling the relationship between the response and explanatory
l, l, l, l, l, l, l, l, l, l, l,




l, variables, 3. Testing hypotheses of association relationships
l, l, l, l, l, l,




B0 = ? -- Answer ✔✔ intercept
l, l, l, l, l, l,




B1 = ? -- Answer ✔✔ slope
l, l, l, l, l, l,

, For our linear model where: Y = B0 + B1 + EPSILON (E), what does the epsilon represent? -
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




- Answer ✔✔ deviance of the data from the linear model (error term)
l, l, l, l, l, l, l, l, l, l, l, l,




What are the 4 assumptions of linear regression? -- Answer ✔✔ Linearity/Mean Zero,
l, l, l, l, l, l, l, l, l, l, l, l,




l, Constant Variance, Independence, Normality l, l, l,




Linearity/Mean zero assumption -- Answer ✔✔ Means that the expected value (deviances) l, l, l, l, l, l, l, l, l, l, l,




l, of errors is zero. This leads to difficulties in estimating B0 and means that our model does
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, not include a necessary systematic component
l, l, l, l, l,




Constant variance assumption -- Answer ✔✔ Means that it cannot be true that the model is
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, more accurate for some parts of the population, and less accurate for other parts of the
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, populations. This can result in less accurate parameters and poorly-calibrated prediction
l, l, l, l, l, l, l, l, l, l,




l, intervals.


Assumption of Independence -- Answer ✔✔ Means that the deviances, or in fact the
l, l, l, l, l, l, l, l, l, l, l, l, l,




l, response variables ys, are independently drawn from the data-generating process. (this
l, l, l, l, l, l, l, l, l, l,




l, most often occurs in time series data) This can result in very misleading assessments of the
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, strength of regression. l, l,




Normality assumption -- Answer ✔✔ This is needed if we want to do any confidence or
l, l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, prediction intervals, or hypothesis test, which we usually do. If this assumption is violated,
l, l, l, l, l, l, l, l, l, l, l, l, l,




l, hypothesis test and confidence and prediction intervals and be very misleading.
l, l, l, l, l, l, l, l, l, l,




What are the 3 parameters we estimated in regression? -- Answer ✔✔ B0, B1, sigma
l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, squared (variance of the one pop.)
l, l, l, l, l,




What do we mean by model parameters in statistics? -- Answer ✔✔ Model parameters are
l, l, l, l, l, l, l, l, l, l, l, l, l, l,




l, unknown quantities, and they stay unknown regardless how much data are observed. We
l, l, l, l, l, l, l, l, l, l, l, l,




l, estimate those parameters given the model assumptions and the data, but through
l, l, l, l, l, l, l, l, l, l, l,