MLAI/Regression
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Polynomial Linear RegressionMLAI/Regression 2020. 1. 20. 19:43
1. Overview 2. Description Instead of the linear regression, we're going to conductive Pono regression and that's, in this case, fits perfectly. Why is it still called a linear regression if it's a polynomial regression? When we're talking about linear and nonlinear we're not actually talking about the X variables. You're talking about the coefficients here. 3. Reference
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Cluster AnalysisMLAI/Regression 2020. 1. 20. 14:12
1. Overview Technically speaking cluster analysis is a multivariate statistical technique. Intuitively speaking observations in a data set can be divided into different groups and sometimes this is very useful. Both results are perfectly logical but in a different way in the first two cases we were differentiating the clusters by geographic proximity while in the second by language geographic pr..
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Logistic Regression StatisticsMLAI/Regression 2020. 1. 20. 12:54
1. Overview 1.1 Likelihood function It is a function which estimates how likely it is that the model at hand describes the real underlying relationship of the variables. The bigger the likelihood function the higher the probability that our model is correct. MLE tries to maximize the likelihood function. The computer is going through different values until it finds a model for which the likeliho..
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Ordinary Least Squares AssumptionsMLAI/Regression 2020. 1. 20. 01:05
1. Overview If a regression assumption is violated, performing regression analysis will yield an incorrect result. The linear regression is the simplest non-trivial relationship. 2. Linearity γ=β0+β1x1+β2x2+⋯+βkxk+ε How can you verify if the relationship between two variables is linear The easiest way is to choose an independent var..
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Correlation vs RegressionMLAI/Regression 2020. 1. 19. 14:08
1. Overview Correlation does not imply causation. 2. Description The first correlation measures the degree of relationship between two variables. Regression analysis is about how one variable affects another or what changes it causes to the other. Second, Correlation doesn't capture causality but the degree of interrelation between the two variables. Regression is based on causality. It shows no..
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Multiple Linear regressionMLAI/Regression 2020. 1. 19. 00:01
1. Overview Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable. The goal of multiple linear regression (MLR) is to model the linear relationship between the explanatory (independent) variables and response (dependent) variable. 2. Description 2.1 Formula 2.1.1..
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Logistic RegressionMLAI/Regression 2019. 10. 20. 19:07
1. Overview We can predict categorical outcomes through a logistic regression yes or no will buy or won't buy 0 or 1. 2. Introduction Not only is this linear regression an awful fit but it also predicts values which are consist of only 0 and 1 which fall outside the natural domain of admission. This regression doesn't even know that our values are bounded between 0 and 1. The first assumption of..
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Simple Linear RegressionMLAI/Regression 2019. 10. 20. 18:20
1. Overview Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model. 2. Description 2.1 Process Ge..