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Midterm

# MISY261 Midterm: Exam 3 Prep - Videos Premium

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School
Department
Management Information Systems
Course
MISY261
Professor
Alex Chaplin
Semester
Spring

Description
Exam 3 Prep Videos Logistic Regression: Introduction Linear regression predicts continuous outcomes Binary logistic regression helps with yesno outcomes Logistic Regression: Setup Probability of success = p Probability of failure = 1p Defined the problem with just one unknown value, p One of the possible outcomes is always assigned as success value Construct a linear model of the odds ratio log( ) = + + + + 1 0 1 1 o Right hand side is identical to linear regression model o ( ) 1 o R assumes that the log is a log with a base e o This function will help us classify 0s and 1s more easily Odds Ratio Odds ratio is Odds ratio is not the same as the probability o success o Probability of success = p, odds ratio =1 o When the probability of success increases, the odds ratio increases Odds ratio can be >1 o If this is the case, it means the probability of success is greater than the probability of failure If the odds ratio = 1, this means the probability of success = the probability of failure Logarithm Review Log = function that takes a number and returns a number Cannot take the log of a number 0 o Odds ratio will never be negative because it is made of probabilities Log(1)=0, so if there is an odds ratio of 1, the result will be 0 o If the odds ratio is <1, then the log(odds ratio) will be <0 o If the odds ratio is >1, the log(odds ratio) will be >0 Logistic Regression in R (Implementation) In R, change lm to glm o Generalized linear model o Fewer assumptions o Takes more arguments o R uses Maximum Likelihood Estimation to try out a bunch of numbers to see what are the most likely slopes and coefficients Finds the best model o Ex: fit = glm(doptionsexp ~ dMarketVolume + dVIXclose, family=binomial doptionsex binary response (the thing youre trying to predict)
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