Poisson regression Samenvattingen, Aantekeningen en Examens

Factor Based Models classification, clustering, regression. Implicitly assumed that we have a lot of factors in the final model Why limit number of factors in a model? 2 reasons overfitting: when # of factors is close to or larger than # of data points. Model may fit too closely to random effects simplicity: simple models are usually better Classical variable selection approaches 1. Forward selection 2. Backwards elimination 3. Stepwise regression greedy algorithms Backward elimination...

Factor Based Models classification, clustering, regression. Implicitly assumed that we have a lot of factors in the final model Why limit number of factors in a model? 2 reasons overfitting: when # of factors is close to or larger than # of data points. Model may fit too closely to random effects simplicity: simple models are usually better Brainpower Read More Previous Play Next Rewind 10 seconds Move forward 10 seconds Unmute 0:01 / 0:15 Full screen Classical var...

Least Square Elimination (LSE) cannot be applied to GLM models. - False - it is applicable but does not use data distribution information fully. 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. Maximum Likelihood Estimation is not applicable for simple linear regression and multiple linear regres...

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 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 th...

1. If there are variables that need to be used to control the bias selection in the model, they should forced to be in the model and not being part of the variable selection process. - Answer-True 2. Penalization in linear regression models means penalizing for complex models, that is, models with a large number of predictors. - Answer-True 3. Elastic net regression uses both penalties of the ridge and lasso regression and hence combines the benefits of both. - Answer-True 4. Variable sele...

1. All regularized regression approaches can be used for variable selection. - False 2. Penalization in linear regression models means penalizing for complex models, that is, models with a large number of predictors. - True 3. Elastic net regression uses both penalties of the ridge and lasso regression and hence combines the benefits of both. - True 4. Variable selection can be applied to regression problems when the number of pre- dicting variables is larger than the number of observation...

1 A website simulates the outcome of throwing four fair dice. Ten thousand people take part in a challenge using the website in which they have one attempt at getting four sixes in the four throws of the dice. The number of people who succeed in getting four sixes is denoted by the random variable X. (a) Show that, for each person, the probability that the person gets four sixes is equal to 1 . [1] (b) Explain why you could use either a binomial distribution or a Poisson distribution to mo...

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ISYE 6414 – Final Exam Questions and answers, 100% Accurate. Rated A+ Logistic Regression - -Commonly used for modeling binary response data. The response variable is a binary variable, and thus, not normally distributed. In logistic regression, we model the probability of a success, not the response variable. In this model, we do not have an error term g-function - -We link the probability of success to the predicting variables using the g link function. The g function is the s-sh...

ISYE 6501 Midterm 2 Part 1 Latest 2023 Rated A greedy algorithm at each step, the algorithm does the thing that looks best without taking future options into consideration; more classical variable selection methods stepwise - (forward, backward, combination) lasso elastic net available metrics for variable selection criteria p-value r2 AIC / BIC lasso Giving regression a budget to use on coefficients which it uses on most important coefficients Have to scale first elastic net constrain combi...