sample residuals correct answers The _______________ for multiple linear regression do not have constant variance.
error terms correct answers The ______________ for multiple linear regression have constant variance.
normality correct answers QQPlot and histogram are used to assess what in ML...
ISYE6414 Midterm 2 || with Error-free Solutions.
sample residuals correct answers The _______________ for multiple linear regression do not
have constant variance.
error terms correct answers The ______________ for multiple linear regression have constant
variance.
normality correct answers QQPlot and histogram are used to assess what in MLR?
linearity correct answers Residuals vs predictor are used to predict what in MLR
constant variance and independence correct answers Residuals vs fitted values are used to assess
what in MLR
leverage points correct answers Points that are far from the mean of the x's are called
Influential points correct answers Points that are far from the mean of the x's and y's are called
outliers correct answers It is good practice to perform regression analysis with and without what?
Cook's distance correct answers What is used to quantify outliers?
Cook's distance correct answers How much all the values in the model change when the ith value
is removed is known as what?
D_i > 4/n or D_i > 1 or Large D correct answers Cooks distance that should be investigated.
R-Squared correct answers The proportion of variability in Y than can be explained by the
predictor variables.
Controlling factors correct answers Model variables used to account for selection bias.
Indicator variable correct answers Continuous variables are converted to ________ when there is
a distinct gap in a variable distribution.
n-p-1 correct answers The number of degrees of freedom for a T test for the statistical
significance of a MLR coefficient?
At least one variable has explainitory power on the response variable. correct answers When the
regression model has a high F value/low p-value.
order matters correct answers When testing subsets of coefficients using anova command.
If the variable is not very granular correct answers When can "year" be used as a qualitative
variable?
, Pearson Chi-squared test correct answers Used to evaluate the relationship between any two
qualitative variables.
table correct answers A command needed prior to running a pearson chi-squared test of
qualitative variables.
Reduce the dummy variables into groups correct answers What should you do when you have a
high number of predicting variables due to a large number of categorical variables resulting in
numerous dummy variables.
The first correct answers Which category does R choose as the baseline label when creating
dummy variables with as.factor()
No baseline for comparison correct answers If you use a model without an intercept, how will
interpreting coefficients be different?
independent correct answers The statistical significance of a predicting variable in a marginal
and conditional models are _____________ .
cross validation correct answers No analysis of prediction is complete without evaluating the
performance of the model using this technique.
less bias correct answers Higher number of folds in k-fold cross validation means what?
large samples correct answers For logistic regression, the statistical inference based on the
normal distribution applies only under what?
Model is a good fit correct answers In goodness of fit tests, what is the null hypothesis?
True correct answers In MLR, the F test is used to evaluate the overall regression.
True correct answers In MLR, the coefficient of variation is interpreted as the percentage of
variability in the response variable explained by the model.
False correct answers Residual analysis is used to measure predictive value of a model.
False correct answers In the presence of multicollinearity, the coefficient of variation decreases.
False correct answers In the presence of multicollinearity, the regression coefficients will tend to
be identified as statistically significant even if they are not.
False correct answers In the presence of multicollinearity, the prediction will not be impacted.
True correct answers If the linearity assumption with respect to one or more predictors does not
hold, then we use transformations of the corresponding predictors to improve on this assumption.
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