# What is Logistic Regression and when it is used? A step by step guide. Logistic Regression was used in the biological sciences in early twentieth century. It was then used in many social science applications.

Regression has been an integral part of statistics and is used to predict the temperature, humidity and many of the daily use information. The term “regression” was coined by Francis Galton in the nineteenth century to describe a biological phenomenon. The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon is also known as regression toward the mean) For Galton, regression had only this biological meaning but his work was later extended by Udny Yule and Karl Pearson to a more general statistical context.

Logistic Regression is a. branch of regression which gives a binary output (0,1).In other words, the logistic regression model predicts P(Y=1) as a function of X.

Logistic Regression Assumptions

1. The logistic regression assumes that there is minimal or no multicollinearity among the independent variables.
2. The Logistic regression assumes that the independent variables are linearly related to the log of odds.
3. The logistic regression usually requires a large sample size to predict properly.
4. The Logistic regression which has two classes assumes that the dependent variable is binary and ordered logistic regression requires the dependent variable to be ordered.
5. The Logistic regression assumes the observations to be independent of each other.

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