General linear model glm - Study guides, Class notes & Summaries

What are three problems that variable selection tries to minimize? correct answers high dimensionality, multicollinearity, prediction vs explanatory high dimensionality correct answers In linear regression, when the number of predicting variables P is large, we might get better predictions by omitting some of the predicting variables. Models with many predictors have... correct answers low bias, high variance Models with few predictors have... correct answers high bias but low variance ...

QCM MULTIPLE CHOICE JAN EXAM 2024!!

STAT 404 - MIDTERM 2 Terms in this set (44) Why do we need functions? Data structures tie related values into one object Functions tie related commands into one object In both cases: easier to understand, easier to work with, easier to build into larger things Function structure - Name - Arguments - Body - Return The structure of a function has three basic parts: Inputs (or arguments): what should a user provide to the function? Body: code that is executed Output ...

ASSIGNMENT 3 - SOLUTIONS 1. [15 points] (a) [5 points] The likelihood for the data from the British for the most general multinomial model is P(Yij = yij , i = 1, . . . , 5; j = 1, . . . , 5|Y.. = y..) = y.. ! Q i Q j yij ! Y i Y j π yij ij where πij = µij µ.. and P i P j πij = 1. (b) [5 points] The form of the corresponding log-linear model that should be fit to assess the hypothesis of perfect social mobility in Britain is log µij = u + u X i + u Y j where ...

Interaction (definition) - answer-Effect of one variable on target variable depends on the value/level of another variable. GLM Description (idea) - answer-GLMs relate a function of target mean linearly to a set of predictors. Target itself is not transformed. Recursive binary splitting (idea) - answer-divide feature space recursively into a set of non-overlapping regions of relatively homogeneous observations until a stopping criterion is reached. Random Forests (idea) - answer-Reduce ...

What are three problems that variable selection tries to minimize? - ANSWER-high dimensionality, multicollinearity, prediction vs explanatory high dimensionality - ANSWER-In linear regression, when the number of predicting variables P is large, we might get better predictions by omitting some of the predicting variables. Models with many predictors have... - ANSWER-low bias, high variance Models with few predictors have... - ANSWER-high bias but low variance prediction risk - ANSWER-...