Final Module (11 & 12) Flashcards
Matrix
a rectangular array of numbers, symbols, expressions, functions etc. (called entries, or elements), arranged in rows and columns
Vectors
matrices with only one row or column
Element-by-element multiplication & division
multiplication: π΄ β π΅
division: π΄ β π΅
Basically multiplying and dividing by using the same rules as adding & subtracting instead of the normal matrix rule.
Matrix multiplication is not commutative (true or false)
TRUE
Logistic regression
a statistical method that models the relationship between a binary response variable and one or more
continuous predictor variables
Examples of Logistic regression
- Predicting whether or not a customer will default on a loan based on their credit score
- Predicting whether a basketball player will get drafted into the NBA based on average rebounds and points per game
Logistic regression assumes
instead of the linear dependence between the response variable and predictor variables π¦ = π½0 + π½1π₯1 + β― + π½ππ₯π there is a linear dependence between the βlogitβ function of probability that the response variable takes the value of 1 and the predictor variables
Parameter risk
the risk associated with underestimating or
overestimating the parameters of the model
Process Risk
the risk associated with the variability of the
process itself
Model Risk
the risk associated with choosing insufficiently
accurate model
Modeling parameter risk using
LINEST output
Total Risk =
Total Risk = parameter risk + process risk
βLogitβ function of the probability of response variable to be 1 is modeled as
a linear combination of predictors
MLE
used to find the set of parameters that provides the best loglikelihood of the model