True – The relationship that links the predictors is highly non-linear.
In Logistic Regression, the relationship between the probability of success and the predicting variables is non-linear.<\/p>\n\n\n\n
False – In logistic regression, there are no error terms.
In Logistic Regression, the error terms follow a normal distribution.<\/p>\n\n\n\n
True – the logit function is also known as the log-odds function, which is the ln(P\/1-p).
The logit function is the log of the ratio of the probability of success to the probability of failure and is also known as the log-odds function.<\/p>\n\n\n\n
False – As there is no error term in logistic regression, there is no additional parameter for the variance of the error terms.
The number of parameters that need to be estimated in a logistic regression model with 6 predicting variables and an intercept is the same as the number of parameters that need to be estimated in a standard linear regression model with an intercept and same predicting variables.<\/p>\n\n\n\n
False – log-likelihood is a non-linear function, and a numerical algorithm is needed in order to maximize it.
The log-likelihood function is a linear function with a closed form solution.<\/p>\n\n\n\n
False – We interpret logistic regression coefficients with respect to the odds of success.
In Logistic Regression, the estimated value for a regression coefficient B represents the estimated expected change in the response variable associated with a one unit increase in the predicting variable, holding all else fixed.<\/p>\n\n\n\n
False – The coefficient estimator follows an approximate normal distribution.
Under logistic regression, the sampling distribution used for a coefficient estimator is a chi-square distribution when the sample size is large.<\/p>\n\n\n\n
False – when testing a subset of coefficients, deviance follows a chi-square distribution with q degrees of freedom, where q is the number of regression coefficients discarded from the full model to get the reduced model.
When testing a subset of coefficients, deviance follows a chi-square distribution with q degrees of freedom, where q is the number of regression coefficients in the reduced model.<\/p>\n\n\n\n
True – logistic regression is the generalization of the standard regression model that is used when the response variable y is binary or binomial.
Logistic regression deals with the case where the dependent variable is binary and the conditional distribution is binomial.<\/p>\n\n\n\n
False – The residuals can only be defined for logistic regression with replications.
It is good practice to perform a goodness-of-fit test on logistic regression models without replications.<\/p>\n\n\n\n
False – for logistic regression, if the p-value of the deviance test for GOD is large, then the model is a good fit.
In Logistic regression, if the p-value of the deviance test for GOF is smaller than the significance level alpha, then is is plausible that the model is a good fit.<\/p>\n\n\n\n
False – GOF is no guarantee for good prediction and vice-versa.
If a logistic regression model provides accurate classification, then we can conclude that it is a good fir for the data.<\/p>\n\n\n\n
True – the deviance residuals are approximately N(0,1) if the model is a good fit to the data.
For both logistic regression and Poisson regression, the deviance residuals should follow an approximate standard normal distribution if the model is a good fit for the data.<\/p>\n\n\n\n
False
The logit link function is the best link function to model binary response data because it always fits the data better than other link functions.<\/p>\n\n\n\n
True – we can use the Pearson or deviance residuals, but only if the model has replications.
Although there are no error terms in logistic regression model using binary data with replications, we can still perform residual analysis.<\/p>\n\n\n\n
True – The error rate is biased downwards, since the model sees the data 2 times, once for training and once for testing.
For a classification model, the training error tends to underestimate the true classification error rate of the model.<\/p>\n\n\n\n
True – the parameters and their standard errors are approximate.
The estimated regression coefficients in Poisson regression are approximate.<\/p>\n\n\n\n
False – we use a z-test, since the the distributions are approximately normal with large N.
A t-test is used for testing the statistical significance of a coefficient given all predicting variables in a Poisson regression model.<\/p>\n\n\n\n
True
An overdispersion parameter of 1 indicates that the variability of the response is close to the variability estimated by the model.<\/p>\n\n\n\n
False – we assume that the log rate is a linear combination of the predicting variables, hence Poisson regression is a generalized linear model (GLM)
In Poisson regression, we assume a non linear relationship between the log rate and the predicting variables.<\/p>\n\n\n\n
True
Logistic regression models the probability of a success given a set of predicting variables.<\/p>\n\n\n\n
True
The estimation of logistic regression coefficients is based on maximum likelihood.<\/p>\n\n\n\n
1) no error term
2) the response variable is not normally distributes (binomial)
3) it models probability, not expectation of response
What are the differences between logistic regression and standard regression.<\/p>\n\n\n\n
False – Come back and name other link functions
The logit link function is the only link function that can be used for modeling binary response data.<\/p>\n\n\n\n
False – logistic regression coefficients are interpreted with respect to odds
The interpretation of the regression coefficients is the same in logistic regression as standard regression.<\/p>\n\n\n\n
False – there is no closed form solution, we we use a numerical approximation.
We can derive exact estimates for the logistic regression coefficients.<\/p>\n\n\n\n
False – fill in later
The estimations of the regression coefficients is based on minimizing the sum of least squares in logistic regression.<\/p>\n\n\n\n
1) The sampling distribution of the regression coefficients is approximate
2) a large sample size is required for making accurate statistical inferences
3) a normal sampling distribution is used instead of a t-distribution for statistical inference
Differences between logistic regression and linear regression – statistical inference.<\/p>\n\n\n\n
True – statistical inference in logistic regression is only reliable when N is large
in logistic regression, the hypothesis test for subsets of coefficients is approximate, it relies on a large sample size.<\/p>\n\n\n\n
True – we predict if a response will be a success or failure
In logistic regression, prediction is a classification of a future binary response.<\/p>\n\n\n\n
True
in k-fold cross validation, the larger K, the higher the variability in the estimation of the classification error is.<\/p>\n\n\n\n
1) to model count data
2) to model rate response data
3) to model response data with a Poisson distribution
What can Poisson regression be used for?<\/p>\n\n\n\n
True
The link function for the Poisson regression is the log function.<\/p>\n\n\n\n
False – constant variance will be violated.
If we apply a standard regression to response data with a Poisson distribution, constant variance assumption will hold.<\/p>\n\n\n\n
True
in Poisson regression, we model the log of the expected response variable, not the expected log response variable.<\/p>\n\n\n\n
False – fill in later
In Poisson regression, we use ordinary least squares to fit the model.<\/p>\n\n\n\n
True
In Poisson regression, we interpret the coefficients in terms of the ratio of the response rates.<\/p>\n\n\n\n
False – we use z-tests
In Poisson regression, we make inference using the t-intervals for the coefficients.<\/p>\n\n\n\n
False – the estimates for the coefficients are approximate in Poisson regression.
in Poisson regression, inference relies on the exact sampling distribution of the regression coefficients.<\/p>\n\n\n\n
True – the test for regression coefficients in Poisson regression follows a chi-square distribution with q degrees of freedom.
We use a chi-square testing procedure to test whether a subset of regression coefficients are zero in Poisson regression.<\/p>\n\n\n\n
False – fill in later
We can use residual analysis in Poisson regression to evaluate whether errors are uncorrelated.<\/p>\n\n\n\n
1) to address multicollinearity in multiple regression
2) To select among a large number of predicting variables
3) To fit a model when there are more predicting variables than observations
What are some common use cases for variable selection?<\/p>\n\n\n\n
True
When selecting variables, it is important to first establish which variables are used for controlling bias in the sample and which are explanatory.<\/p>\n\n\n\n
True -Variable selection balances bias with variance to select the model.
Variable selection methods are performed by balancing the bias-variance tradeoff.<\/p>\n\n\n\n
True
The penalty constant Lambda in regularized regression has the role of controlling the trade off between lack of fit and model complexity.<\/p>\n\n\n\n
True – we can find closed form solutions for the ridge coefficients
The ridge regression coefficients are obtained using an exact or closed form expression.<\/p>\n\n\n\n
True – in Lasso, the coefficient estimates are approximate, we used a numerical algorithm to estimate them.
The estimated coefficients in lasso regression are obtained using a numerical algorithm.<\/p>\n\n\n\n
True – fill in later
The regression coefficients in lasso are less efficient than those from the ordinary least squares estimation approach.<\/p>\n\n\n\n
True – this is true for explanatory purposes but NOT prediction.
When Selecting variables for explanatory purpose, one might consider including predicting variables which are correlated if it would help answer your research hypothesis.<\/p>\n\n\n\n
False – Variable selection has come a long way but is far from a solved problem, especially with many predictors.
Variable selection is a simple and solved statistical problem since we can implement it using software.<\/p>\n\n\n\n
False – it is not good practice to perform variable selection based on the statistical significance of the coefficients, as significance is almost always derived based on the other predictors in the model.
It is good practice to perform variable selection based on the statistical significance of the regression coefficients.<\/p>\n\n\n\n
True – since the model sees the data 2 times, training risk is generally too favorable when estimating true prediction risk.
The training risk is a biased estimator for prediction risk.<\/p>\n\n\n\n
True – AIC is a measure for prediction risk that adds a penalty term to correct for the bias in the training risk.
AIC is an estimate for the prediction risk.<\/p>\n\n\n\n
True – BIC generally penalized more than all of the other prediction risk estimated, and is especially useful when the objective is prediction, since it selects simpler models.
BIC penalizes for complexity of the model more than both VC and Mallow’s Cp statistic.<\/p>\n\n\n\n
False – often times these 2 approaches will not select the same set of variables.
When the number of predicting variables is large, both backward and forward stepwise regression will always select the same set of variables.<\/p>\n\n\n\n
False – complex models with many predictors have low bias but high variance.
Complex models with many predictors are often extremely biased, but have low variance.<\/p>\n\n\n\n
False – Backward stepwise regression is more computationally expensive than forward and generally selects a larger model. Forward is usually preferred over backward.
Backward stepwise regression is preferable over forward stepwise regression because it starts with larger models.<\/p>\n\n\n\n
False – stepwise regression is greedy, which means that it is a heuristic and not all combinations of the model are checked.
Stepwise regression is a greedy algorithm searching through all possible combinations of the predicting variables to find the model with the best score.<\/p>\n\n\n\n
True – if some variables are required for whatever reason, we should force the minimum model to add those variables.
If specific variables need to be included to control bias in the model, they should be forced into the model and not be a part of the variable selection process.<\/p>\n\n\n\n
False – the L1 penalty measures sparsity and is associated with Lasso, which forces coefficients to 0. The L2 penalty is used to deal with multicollinearity and does not force coefficients to 0.
The L2 penalty measures the sparsity of a vector and forces regression coefficients to be zero.<\/p>\n\n\n\n
True – Elastic net regression combines the L1 and L2 benefits, but it also combines the disadvantages of both approaches!
Elastic net regression uses both penalties of ridge and lasso regression and hence combined the benefits of both.<\/p>\n\n\n\n
False – regularized regression requires the predictors to be scaled.
It is not requires to standardize or rescale the predicting variables when performing regularized regression.<\/p>\n\n\n\n
False – Ridge regression is associated with the L2 penalty, which does not perform variable selection.
Ridge Regression is a regularized regression approach that can be used for variable selection.<\/p>\n\n\n\n
True – there is no closed-form solution for lasso regression coefficient estimates.
The lasso regression requires a numerical algorithm to minimize the penalized sum of least squares.<\/p>\n\n\n\n
False – the shrinkage penalty is applied to all b1\u2026BP, but not the intercept B0.
In regularized regression, the penalization is generally applied to all regression coefficients, where p = number of predictors.<\/p>\n\n\n\n
True – Lambda balances the bias-variance tradeoff.
The penalty constant Lambda is penalized regression controls the trade off between lack of fit and model complexity.<\/p>\n\n\n\n
False – when Lambda = 0, the corresponding regression coefficients are equal to OLS.
In ridge regression, when the penalty constant Lambda = 1, the corresponding ridge regression coefficients are the same as OLS.<\/p>\n\n\n\n
False – Ridge regression used the L2 penalty, which specifically addresses multicollinearity in the predictors.
Ridge Regression cannot be used to deal with problems caused by high correlation among predictors.<\/p>\n\n\n\n
True
We can obtain both the estimates and the standard deviations of the estimates for the regression coefficients in logistic regression.<\/p>\n\n\n\n
True
In Logistic regression, the sampling distribution of the residual is approximately normal if the data is a good fit.<\/p>\n\n\n\n
False
In logistic regression, the residuals are derived as the fitted values minus the observed responses.<\/p>\n\n\n\n
Least Square Elimination (LSE) cannot be applied to GLM models.
False – it is applicable but does not use data distribution information fully.<\/p>\n\n\n\n
In multiple linear regression with idd and equal variance, the least squares estimation of regression coefficients are always unbiased.
True – the least squares estimates are BLUE (Best Linear Unbiased Estimates) in multiple linear regression.<\/p>\n\n\n\n
Maximum Likelihood Estimation is not applicable for simple linear regression and multiple linear regression.
False – In SLR and MLR, the SLE and MLE are the same with normal idd data.<\/p>\n\n\n\n
The backward elimination requires a pre-set probability of type II error
False – Type I error<\/p>\n\n\n\n
The first degree of freedom in the F distribution for any of the three procedures in stepwise is always equal to one.
True<\/p>\n\n\n\n
MLE is used for the GLMs for handling complicated link function modeling in the X-Y relationship.
True<\/p>\n\n\n\n
In the GLMs the link function cannot be a non linear regression.
False – It can be linear, non linear, or parametric<\/p>\n\n\n\n
When the p-value of the slope estimate in the SLR is small the r-squared becomes smaller too.
False – When P value is small, the model fits become more significant and R squared become larger.<\/p>\n\n\n\n
In GLMs the main reason one does not use LSE to estimate model parameters is the potential constrained in the parameters.
False – The potential constraint in the parameters of GLMs is handled by the link function.<\/p>\n\n\n\n
The R-squared and adjusted R-squared are not appropriate model comparisons for non linear regression but are for linear regression models.
TRUE – The underlying assumption of R-squared calculations is that you are fitting a linear model.<\/p>\n\n\n\n
The decision in using ANOVA table for testing whether a model is significant depends on the normal distribution of the response variable
True<\/p>\n\n\n\n
When the data may not be normally distributed, AIC is more appropriate for variable selection than adjusted R-squared
True<\/p>\n\n\n\n
The slope of a linear regression equation is an example of a correlation coefficient.
False – the correlation coefficient is the r value. Will have the same + or – sign as the slope.<\/p>\n\n\n\n
In multiple linear regression, as the value of R-squared increases, the relationship
between predictors becomes stronger
False – r squared measures how much variability is explained by the model, NOT how strong the predictors are.<\/p>\n\n\n\n
When dealing with a multiple linear regression model, an adjusted R-squared can
be greater than the corresponding unadjusted R-Squared value.
False – the adjusted rsquared value take the number and types of predictors into account. It is lower than the r squared value.<\/p>\n\n\n\n
In a multiple regression problem, a quantitative input variable x is replaced by x \u2212
mean(x). The R-squared for the fitted model will be the same
True<\/p>\n\n\n\n
The estimated coefficients of a regression line is positive, when the coefficient of
determination is positive.
False – r squared is always positive.<\/p>\n\n\n\n
If the outcome variable is quantitative and all explanatory variables take values 0 or
1, a logistic regression model is most appropriate.
False – More research is necessary to determine the correct model.<\/p>\n\n\n\n
After fitting a logistic regression model, a plot of residuals versus fitted values is
useful for checking if model assumptions are violated.
False – for logistic regression use deviance residuals.<\/p>\n\n\n\n
In a greenhouse experiment with several predictors, the response variable is the
number of seeds that germinate out of 60 that are planted with different treatment
combinations. A Poisson regression model is most appropriate for modeling this
data
False – poisson regression models rate or count data.<\/p>\n\n\n\n
For Poisson regression, we can reduce type I errors of identifying statistical
significance in the regression coefficients by increasing the sample size.
True<\/p>\n\n\n\n
Both LASSO and ridge regression always provide greater residual sum of squares
than that of simple multiple linear regression.
True<\/p>\n\n\n\n
If data on (Y, X) are available at only two values of X, then the model Y = \\beta_1 X<\/p>\n\n\n\n
If the Cook’s distance for any particular observation is greater than one, that data
point is definitely a record error and thus needs to be discarded.
False – must see a comparison of data points. Is 1 too large?<\/p>\n\n\n\n
We can use residual analysis to conclusively determine the assumption of
independence
False – we can only determine uncorrelated errors.<\/p>\n\n\n\n
It is possible to apply logistic regression when the response variable Y has 3
classes.
True<\/p>\n\n\n\n
. A correlation coefficient close to 1 is evidence of a cause-and-effect relationship
between the two variables.
False- cause and effect can only be determined by a well designed experiment.<\/p>\n\n\n\n
Multiplying a variable by 10 in LASSO regression, decreases the chance that the
coefficient of this variable is nonzero.
False – I am not sure why anyone would think this would be true.<\/p>\n\n\n\n
In regression inference, the 99% confidence interval of coefficient \\beta_0 is always
wider than the 95% confidence interval of \\beta_1.
False- can only compare beta1 with beta1 and beta0 with beta0<\/p>\n\n\n\n
The regression coefficients for the Poisson regression model can be estimated in
exact\/closed form.
False – MLE is NOT closed form.<\/p>\n\n\n\n
Mean square error is commonly used in statistics to obtain estimators that may be biased, but less uncertain than unbiased ones. And that’s preferred.
True<\/p>\n\n\n\n
Regression models are only appropriate for continuous response variables.
False – logistic and poisson model probability and rate<\/p>\n\n\n\n
The assumptions in logistic regression are – Linearity, Independence of response variable, and the link function is the logit function.
True – linearity is measured through the link, , the g of the probability of success and the predicted variable.<\/p>\n\n\n\n
The log odds function, also called the logit function, which is the log of the ratio between the probability of a success and the probability of a failure
True<\/p>\n\n\n\n
In logistic regression we interpret the Betas in terms of the response variable.
False – we interpret it in terms of the odds of success or the log odds of success<\/p>\n\n\n\n
In logistic regression we have an additional error term to estimate.
False – there is not error term in logistic regression.<\/p>\n\n\n\n
The least square estimation for the standard regression model is equivalent with Maximum Likelihood Estimation, under the assumption of normality.
True<\/p>\n\n\n\n
The variance estimator in logistic regression has a closed form expression.
False – use statistical software to obtain the variance-co-variance matrix<\/p>\n\n\n\n
We can use the z value to determine if a coefficient is equal to zero in logistic regression.
True – z value = (Beta-0)\/(SE of Beta)<\/p>\n\n\n\n
In testing for a subset of coefficients in logistic regression the null hypothesis is that the coefficient is equal to zero
True<\/p>\n\n\n\n
Like standard linear regression we can use the F test to test for overall regression in logistic regression.
False – It’s 1-pchisq(null deviance-residual deviance, DFnull-DFresidual)<\/p>\n\n\n\n
For logistic regression we can define residuals for evaluating model goodness of fit for models with and without replication.
False – can only be with replication under the assumption that Yi is binary and n1 is greater than 1<\/p>\n\n\n\n
The deviance residuals are the signed square root of the log-likelihood evaluated at the saturated model
True<\/p>\n\n\n\n
From the binomial approximation with a normal distribution using the central limit theorem, the Pearson residuals have an approximately standard chi-squared distribution.
False – Normal distribution<\/p>\n\n\n\n
Visual Analytics for logistic regression
Normal probability plot of residuals
Residuals vs predictors
Logit of success rate vs predictors
True
Normal probability plot of residuals – Normality
Residuals vs predictors – Linearity\/Independence
Logit of success rate vs predictors – Linearity<\/p>\n\n\n\n
Under the null hypothesis of good fit for logistic regression, the test statistic has a Chi-Square distribution with n- p- 1 degrees of freedom
True – don’t forget, we want large P values<\/p>\n\n\n\n
For the testing procedure for subsets of coefficients, we compare the likelihood of a reduced model versus a full model. This is a goodness of fit test
False – it provides inference of the predictive power of the model<\/p>\n\n\n\n
Predictive power means that the predicting variables predict the data even if one or more of the assumptions do not hold.
True<\/p>\n\n\n\n
One reason why the logistic model may not fit is the relationship between logit of the expected probability and predictors might be multiplicative, rather than additive
True<\/p>\n\n\n\n
In logistic regression for goodness of fit, we can only use the Pearson residuals.
False – we can use Pearson or Deviance.<\/p>\n\n\n\n
An indication that a higher order non linear relationship better fits the data is that the dummy variables are all, or nearly all, statistically significant
True<\/p>\n\n\n\n
Simpson’s Paradox – the reversal of association when looking at marginal vs conditional relationships
True<\/p>\n\n\n\n
Classification is nothing else than prediction of binary responses.
True<\/p>\n\n\n\n
We cannot use the training error rate as an estimate of the true error classification error rate because it is biased upward.
False – biased downward<\/p>\n\n\n\n
Random sampling is computationally more expensive than the K-fold cross validation, with no clear advantage in terms of the accuracy of the estimation classification error rate.
True<\/p>\n\n\n\n
Leave on out cross validation is preferred
False – K fold is preferred.<\/p>\n\n\n\n
The larger K is, the larger the number of folds, the less bias the estimate of the classification the error is but has higher variability.
True<\/p>\n\n\n\n
In Poisson regression underlying assumption is that the response variable has a Poisson distribution, or responses could be wait times, or exponential distribution
True<\/p>\n\n\n\n
The g link function is also called the canonical link function.
True – which means that parameter estimates under logistic regression are fully efficient and tests on those parameters are better behaved for small samples.<\/p>\n\n\n\n
Poisson distribution, the variance is equal to the expectation. Thus, the variance is not constant
True<\/p>\n\n\n\n
For Poisson regression we estimate the expectation of the log response variable.
False – we estimate the log of the expectation of the response variable.<\/p>\n\n\n\n
Standard linear regression could be used to model Poisson regression using the variance stabilizing transformation sqrt(mu-3\/8) if the number of counts is large
True – the number of counts can be small – then use Poisson<\/p>\n\n\n\n
In Poisson Regression we do not interpret beta with respect to the response variable but with respect to the ratio of the rate.
True<\/p>\n\n\n\n
In Poisson regression we model the error term
False – there is no error term<\/p>\n\n\n\n
One problem with fitting a normal regression model to Poisson data is the departure from the assumption of constant variance
True<\/p>\n\n\n\n
Event rates can be calculated as events per units of varying size, this unit of size is called exposure
True<\/p>\n\n\n\n
The estimators for the regression coefficients in the Poisson regression are biased.
False – they are unbiased<\/p>\n\n\n\n
To perform hypothesis testing for Poisson, we can use again the approximate normal sampling distribution, also called the Wald test
True – Wald Test also used with logistic regression<\/p>\n\n\n\n
Hypothesis testing for Poisson regression can be done on small sample sizes
False – Approximation of normal distribution needs large sample sizes, so does hypothesis testing.<\/p>\n\n\n\n
For large sample size data, the distribution of the test statistic, assuming the null hypothesis, is a chi-squared distribution
True<\/p>\n\n\n\n
The p-value of the test computed as the left tail of the chi-squared distribution
False – Right tail<\/p>\n\n\n\n
Poisson Assumptions – log transformation of the rate is a linear combination of the predicting variables, the response variables are independently observed, the link function g is the log function
True – remember, NO ERROR TERM<\/p>\n\n\n\n
Overdispersion is when the variability of the response variable is larger than estimated by the model
True<\/p>\n\n\n\n
The gam() function is a non-parametric test to determine what transformation is best.
True<\/p>\n\n\n\n
The deviance and pearson residuals are normally distributed
TRUE – the residual deviances are chi square distributed<\/p>\n\n\n\n
Model with many predictors have high bias but low variance.
False – low bias and high variance<\/p>\n\n\n\n
When the objective is to explain the relationship to the response, one might consider including predicting variables which are correlated
True – But this should be avoided for prediction<\/p>\n\n\n\n
Variable selection addresses multicolinearity, high dimensionaltiy, and prediction vs explanatory prediction
TRUE<\/p>\n\n\n\n
The variables chosen for prediction and the variables chosen for explanatory objectives will be the same.
False<\/p>\n\n\n\n
Variable selection is not special, it is affected by highly correlated variables
TRUE<\/p>\n\n\n\n
Confounding variable is a variable that influences both the dependent variable and independent variable
True<\/p>\n\n\n\n
Explanatory variable is one that explains changes in the response variable
TRUE<\/p>\n\n\n\n
Predicting variable is used in regression to predict the outcome of another variable.
True<\/p>\n\n\n\n
It is good practice apply variable selection without understanding the problem at hand to reduce bias.
False – always understand the problem at hand to better select variables for the model.<\/p>\n\n\n\n
When a statistically insignificant variable is discarded from the model, there is little change in the other predictors statistical significance.
False – it is possible that when a predictor is discarded, the statistical significance of other variables will change.<\/p>\n\n\n\n
We can do a partial F test to determine if variable selection is necessary.
True<\/p>\n\n\n\n
When selecting variables for a model, one needs also to consider the research hypothesis, as well as any potential confounding variables to control for
True<\/p>\n\n\n\n
We would like to have a prediction with low uncertainty for new settings. This means that we’re willing to give up some bias to reduce the variability in the prediction.
True<\/p>\n\n\n\n
Generally models with covariance have high bias but low variance
False – they have low bias but high variance.<\/p>\n\n\n\n
A measure of the bias-variance tradeoff is the prediction risk
TRUE<\/p>\n\n\n\n
To estimate prediction risk we compute the prediction risk for the observed data and take the sum of squared differences between fitted values for sub model S and the observed values.
True – this is called training risk and it is a biased estimate of prediction risk<\/p>\n\n\n\n
The larger the number of variables in the model, the larger the training risk.
False – the larger the number of variables in a model the lower the training risk.<\/p>\n\n\n\n
The Mallow’s CP complexity penalty is two times the size of the model (the number of variables in the submodel) times the estimated variance divided by n.
True<\/p>\n\n\n\n
AIC looks just like the Mallow’s Cp except that the variance is the true variance and not its estimate.
True<\/p>\n\n\n\n
Another criteria for variable selection is cross validation which is a direct measure of explanatory power.
False – Predictive power<\/p>\n\n\n\n
Stepwise is a heuristic search
TRUE it is also a greedy search that does not guarantee to find the best score<\/p>\n\n\n\n
If p is larger than n, stepwise is feasible
TRUE – for forward, but not backward<\/p>\n\n\n\n
Forward stepwise will select larger models than backward.
False – it will typically select smaller models especially if p is large<\/p>\n\n\n\n
Mallow’s CP is useful when there are no control variables.
TRUE<\/p>\n\n\n\n
The overall regression F-statistic tests the null hypothesis that
the coefficients are equal to zero<\/p>\n\n\n\n
The test of subset of coefficients tests the null hypothesis that
discarded variables have coefficients equal to zero.<\/p>\n\n\n\n
Goodness of fit tests the null hypothesis that
the model fits the data<\/p>\n\n\n\n
The prediction risk is the sum between the irreducible error and the mean square error
True<\/p>\n\n\n\n
There is never a situation where a complex model is best.
False – there are situations where a complex model is best<\/p>\n\n\n\n
L0 penalty, which is the number of nonzero regression coefficients
True – not feasible for a large number of predicting variables as requires fitting all models<\/p>\n\n\n\n
L1 penalty will force many betas, many regression coefficients to be 0s
True – is equal to the sum of the absolute values of the regression coefficients to be penalized<\/p>\n\n\n\n
L2 does not perform variable selection
True – is equal to the sum of the squared regression coefficients to be penalized and does not do variable selection<\/p>\n\n\n\n
L2 penalty term measures sparsity
False – L1 penalty measures sparsity. L2 removes the limitation on variable selection<\/p>\n\n\n\n
The estimated regression coefficients from Lasso are less efficient than those provided by the ordinary least squares
True<\/p>\n\n\n\n
Where p the number of predictors is larger than n the number of observationsthe Lasso selects, at most, n variables
True when p is greater than n, lasso will select n variables at the most<\/p>\n\n\n\n
If there is a high correlation between variables, Lasso will select both.
False lasso will select 1<\/p>\n\n\n\n
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isye 6414 midterm 1<\/p>\n","protected":false},"excerpt":{"rendered":"
True – The relationship that links the predictors is highly non-linear.In Logistic Regression, the relationship between the probability of success and the predicting variables is non-linear. False – In logistic regression, there are no error terms.In Logistic Regression, the error terms follow a normal distribution. True – the logit function is also known as the […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[],"tags":[],"class_list":["post-111100","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts\/111100","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/comments?post=111100"}],"version-history":[{"count":0,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts\/111100\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/media?parent=111100"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/categories?post=111100"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/tags?post=111100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}