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SRM EXAM LATEST UPDATE
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SRM EXAM LATEST UPDATELinear model assumptions - ANSWER Linearity, independence, normality,
homoscedasticity
Problems with correlated errors in linear model - ANSWER Standard errors
underestimate true errors, CIs and PIs narrower, p-values lower
Effects of heteroscedasticity - ANSWER Estimates no longer BLUE, standard
errors, CIs, and hypothesis tests invalid
Outlier - ANSWER A response value that is far from the value predicted by the
model
Leverage point - ANSWER A value of a predictor variable that is far from the
rest of the x values
Effects of collinearity - ANSWER Hard to interpret individual coefficients,
larger standard errors, lower t-statistics, reduced power
Why use variance inflation factor (VIF)? - ANSWER Better detects
multicollinearity, i.e., when relationships are not limited to pairwise
VIF definition - ANSWER Ratio of the variance of beta_j when fitting the full
model divided by the variance of beta_j if fit on its own
Which data points do forecasts weight the most? - ANSWER The most recent
ones
Weakly stationary - ANSWER Constant mean and constant variance; the mean
of y does not depend on t, and Cov(y_t, y_s) depends only on | t - s |
Why is white noise both the least and most important model? - ANSWER Least
important because it assumes no relationship between points; most important
because most models try to reduce a time series to white noise
What type of model is a differenced random walk model? - ANSWER White
noise
When to use differences of logs in random walks - ANSWER When both the
series variance and log series variance increase over time
Interpretation of lag k partial autocorrelation - ANSWER Correlation between
, y_t and y_{t-k}, controlling for the effects of the intervening variables (y_{t-1}, ... ,
y_{t-k+1})
Odds ratio interpretation for binary X - ANSWER The odds when x = 1 are
exp(beta_j) greater than the odds when x = 0
Odds ratio interpretation for continuous X - ANSWER beta_j is the the
proportional change in the odds ratio
In GLMs, what drives many inference properties? - ANSWER The choice of
variance function, not the choice of the distribution
Usefulness of R squared in nonlinear models - ANSWER R squared is not a
useful statistic in nonlinear models, in part because the ANOVA decomposition is
no longer valid
Why use Anscombe residuals? - ANSWER Transform to normal distribution or
to stabilize variance
Properties of deviance residuals - ANSWER Similar to Anscombe for approx.
normality, readily defined for any GLM model, easy to compute
In classification trees, what measure(s) is (are) used for tree growing and why? -
ANSWER Gini index and entropy; they are more sensitive to node purity
When is classification error rate preferable in classification trees? - ANSWER
When pruning the tree and prediction accuracy of the final pruned tree is most
important
Advantages of decision trees - ANSWER Easy to explain (easier than linear
regression), mimic human decision-making, graphic displays, handle qualitative
predictors without the needs to create dummy variables
Disadvantages of decision trees - ANSWER Less predictive accuracy,
non-robust
Why use OOB estimation? - ANSWER Directly estimates test error and is
convenient when performing bagging on large data sets for which CV is
computationally difficult
Advantages of bagging over regular decision tree - ANSWER Variance
reduction and better accuracy
Parameters in boosting trees - ANSWER Number of trees, shrinkage
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