Course Description
In statistics, logistic regression,
or logit regression, or logit model is a type of probabilistic
statistical classification model. It is also used to predict a binary
response from a binary predictor, used for predicting the outcome of a
categorical dependent variable (i.e., a class label) based on one or
more predictor variables (features). That is, it is used in estimating
the parameters of a qualitative response model. The probabilities
describing the possible outcomes of a single trial are modeled, as a
function of the explanatory (predictor) variables, using a logistic
function. Frequently (and hereafter in this article) "logistic
regression" is used to refer specifically to the problem in which the
dependent variable is binary—that is, the number of available categories
is two—while problems with more than two categories are referred to as
multinomial logistic regression or, if the multiple categories are
ordered, as ordered logistic regression
Logistic
regression measures the relationship between the categorical dependent
variable and one or more independent variables, which are usually (but
not necessarily) continuous, by using probability scores as the
predicted values of the dependent variable.[3] Thus, it treats the same
set of problems as does probit regression using similar techniques; the
first assumes a logistic function and the second a standard normal
distribution function
Logistic regression can be seen as a
special case of generalized linear model and thus analogous to linear
regression. The model of logistic regression, however, is based on quite
different assumptions (about the relationship between dependent and
independent variables) from those of linear regression. In particular
the key differences of these two models can be seen in the following two
features of logistic regression
Logistic regression can
in many ways be seen to be similar to ordinary regression. It models the
relationship between a dependent and one or more independent variables,
and allows us to look at the fit of the model as well as at the
significance of the relationships (between dependent and independent
variables) that we are modelling. However, the underlying principle of
binomial logistic regression, and its statistical calculation, are quite
different to ordinary linear regression. While ordinary regression uses
ordinary least squares to find a best fitting line, and comes up with
coefficients that predict the change in the dependent variable for one
unit change in the independent variable, logistic regression estimates
the probability of an event occurring (e.g. the probability of a pupil
continuing in education post 16). What we want to predict from a
knowledge of relevant independent variables is not a precise numerical
value of a dependent variable, but rather the probability (p) that it is
1 (event occurring) rather than 0 (event not occurring). This means
that, while in linear regression, the relationship between the dependent
and the independent variables is linear, this assumption is not made in
logistic regression
Curriculum
Different Methods of Predicting Probabilities
Introduction
Method of Predicting Probabilities- Survival Analysis
Modeling Key Concepts
Reasons for using Regression Analysis
Regression Variables
Section 1: Introduction
Section 2: Regression Analysis
Section 3: Predicting Probabilities
Section 4: Logistics Regression
What is Logistic Regression?
What is Regression?
Why Logistic Regression and not OLS?
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