Basic Statistics for Data Science – Part 1

Types of Statistics: Descriptive vs Inferential
Basic terminology like Population vs Sample
Types of Variables: Numerical vs Categorical
Measures of central tendencies: Mean, Median and Mode and their specific use cases
Measures of dispersion/spread: Variance, standard deviation etc.

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What is Linear Regression? Part:1

Linear Regression is a field of study which emphasizes on the statistical relationship between two continuous variables known as Predictor and Response variables. Predictor variable is most often denoted as x and also known as Independent variable. Response variable is most often denoted as y and also known as Dependent variable.

Covariance and Correlation

Covariance and Correlation are very helpful while understanding the relationship between two continuous variables. Covariance tells whether both variables vary in same direction (positive covariance) or in opposite direction (negative covariance). Whereas Correlation explains about the change in one variable leads how much proportion change in second variable.

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