POS 3713 FINAL EXAM QUESTIONS & ANSWERS
Regression is a form of _________ Model. - Answers -Empirical.
Regression only gives you correlations, not... - Answers -Cause.
Regression gives us ___________ Expectations. - Answers -Conditional.
Show the model for Bivariate Regression. - Answers -Y = a + Bx
Y = Dependent Variable
X = Independent Variable
B = Coefficient for X on Y
a = Constant (Y, when X = 0)
a and B are the ________ of our Regression Models. - Answers -Parameters.
What is the Essence of Regression? - Answers -The Regression Line for Y on X
estimates the Average Value of Y corresponding to Each Value of X.
"What is the predicted value for Y given specific info/values for X?"
What is Ordinary Least Squares (OLS) Regression?
What is the Process (3)? - Answers -Minimizing the Sum of the Squared Errors?
1. Start with a Scattterplot
2. Draw the line that Minimizes the Sum of Squared Errors
3. Predict the Average Value of (Y) for variance in (X)
You may use OLS only if...(2) - Answers -1. (Y) is Continuous and Unbounded
2. (Y) is Normally Distributed
How are B and a estimates calculated in Bivariate Regression? - Answers -(...): Look in
Notes
Which form of Uncertainty in our sample parameter estimates is usually more
important? - Answers -Uncertainty around B (our slope estimate).
What are the 4 ways in which we can test for Statistical Significance in our B estimate? -
Answers -1. T-Test.
Find the t-ratio ((B-B*)/se(B)), then compare that to the corresponding Critical Value. If
the t-ratio is greater than the Critical Value, then your estimate is statistically significant.
, 2. If your B is Twice as Big as your Standard Error, then your B should be statistically
significant.
3. If your Confidence Interval overlaps 0, then your B is not Statistically Significant,
4. Look at the p-value for your B.
If it's less than .05, then your B is Statistically Significant.
(STN): These Tests are also dependent on whether you have a One or Two-Tailed
Hypothesis.
Regression does a better job of Predicting (Y) values than Predicting the Mean. Why? -
Answers -Because Regression gives you Coefficients, which estimate how much of an
effect (X) has on (Y). Not just the strength of the relationship like a Difference of Means
test would.
What is a Residual (ui)? - Answers -The difference between the actual value of (Y) and
the predicted value of (Y) from our empirical model.
Formula
Residual(ui) = Y(observed) - Yhat(predicted)
We can use Residuals in order to test... - Answers -Goodness of Fit.
What is the importance of Goodness of Fit? - Answers -We want to know how well the
model predicts/explains the Dependent Variable.
What are the 2 Measures of Model Fit? - Answers -1. Root Mean Squared Error (rMSE)
2. R2 (R^2) (R-Squared)
What is rMSE? - Answers -The measure of the Typical Deviations from the Regression
Line.
We typically use rMSE to... - Answers -Compare Models.
(The one with the smaller rMSE is usually better.)
What does rMSE tell us? - Answers -"On average, our model is off by ..."
What does "k" mean/signify? - Answers -The # of parameters.
What is R-Squared? - Answers -The proportion of the variance in (Y) that our model
explains.
What is the Range of R-Squared? - Answers -0-1.
Regression is a form of _________ Model. - Answers -Empirical.
Regression only gives you correlations, not... - Answers -Cause.
Regression gives us ___________ Expectations. - Answers -Conditional.
Show the model for Bivariate Regression. - Answers -Y = a + Bx
Y = Dependent Variable
X = Independent Variable
B = Coefficient for X on Y
a = Constant (Y, when X = 0)
a and B are the ________ of our Regression Models. - Answers -Parameters.
What is the Essence of Regression? - Answers -The Regression Line for Y on X
estimates the Average Value of Y corresponding to Each Value of X.
"What is the predicted value for Y given specific info/values for X?"
What is Ordinary Least Squares (OLS) Regression?
What is the Process (3)? - Answers -Minimizing the Sum of the Squared Errors?
1. Start with a Scattterplot
2. Draw the line that Minimizes the Sum of Squared Errors
3. Predict the Average Value of (Y) for variance in (X)
You may use OLS only if...(2) - Answers -1. (Y) is Continuous and Unbounded
2. (Y) is Normally Distributed
How are B and a estimates calculated in Bivariate Regression? - Answers -(...): Look in
Notes
Which form of Uncertainty in our sample parameter estimates is usually more
important? - Answers -Uncertainty around B (our slope estimate).
What are the 4 ways in which we can test for Statistical Significance in our B estimate? -
Answers -1. T-Test.
Find the t-ratio ((B-B*)/se(B)), then compare that to the corresponding Critical Value. If
the t-ratio is greater than the Critical Value, then your estimate is statistically significant.
, 2. If your B is Twice as Big as your Standard Error, then your B should be statistically
significant.
3. If your Confidence Interval overlaps 0, then your B is not Statistically Significant,
4. Look at the p-value for your B.
If it's less than .05, then your B is Statistically Significant.
(STN): These Tests are also dependent on whether you have a One or Two-Tailed
Hypothesis.
Regression does a better job of Predicting (Y) values than Predicting the Mean. Why? -
Answers -Because Regression gives you Coefficients, which estimate how much of an
effect (X) has on (Y). Not just the strength of the relationship like a Difference of Means
test would.
What is a Residual (ui)? - Answers -The difference between the actual value of (Y) and
the predicted value of (Y) from our empirical model.
Formula
Residual(ui) = Y(observed) - Yhat(predicted)
We can use Residuals in order to test... - Answers -Goodness of Fit.
What is the importance of Goodness of Fit? - Answers -We want to know how well the
model predicts/explains the Dependent Variable.
What are the 2 Measures of Model Fit? - Answers -1. Root Mean Squared Error (rMSE)
2. R2 (R^2) (R-Squared)
What is rMSE? - Answers -The measure of the Typical Deviations from the Regression
Line.
We typically use rMSE to... - Answers -Compare Models.
(The one with the smaller rMSE is usually better.)
What does rMSE tell us? - Answers -"On average, our model is off by ..."
What does "k" mean/signify? - Answers -The # of parameters.
What is R-Squared? - Answers -The proportion of the variance in (Y) that our model
explains.
What is the Range of R-Squared? - Answers -0-1.
