{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<center><img src=\"https://www.avepoint.com/blog/wp-content/uploads/2019/01/helpful-tips-picture-id933100878.jpg\" style=\"width:1100px;height:300px;\"></center>","metadata":{}},{"cell_type":"markdown","source":"<div class=\"alert alert-warning\" role=\"alert\">\n<h1><span class=\"label label-success\">Correlation</span>  </h1> \n<ul type = \"circle\" style=\"font-size:17px;\"> \n    <li> Correlation is a term that is a measure of the strength and direction of a linear relationship between two quantitative variables (e.g., length, width) \n<li>Correlation ranges form  -1 to 1.\n<li>Positive correlation is a relationship between two variables in which both variables move in the same direction. This is when one variable increases     while the other increases and visa-versa.\n<li>If the correlation values between 2 variables lies near to 1, then it is said to be strongly positive correlated\n<li>If the correlation values between 2 variables lies near to -1, then it is said to be strongly negative correlated.\n<li>If the correlation values between 2 variables is 0, then there is no correlation between two variables.\n<h3>-Tests</h3>\n   <li>Pearson r correlation\n   <li>Kendall rank correlation\n   <li>Spearman rank correlation\n  </ul>\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div class=\"alert alert-info\" role=\"alert\">\n <h1><span class=\"label label-success\">Covariance</span></h1> \n    <ul type = \"circle\" style=\"font-size:17px;\"> \n<li>In statistics, covariance is a measure of the relationship between two random variables.Covariance is a measure of how much two random variables     vary together. It’s similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary       together.\n<li>Positive covariance: Indicates that two variables tend to move in the same direction.\n<li>Negative covariance: Reveals that two variables tend to move in inverse directions.\n<li>Covariance measures the total variation of two random variables from their expected values. Using covariance, we can only gauge the direction of     the relationship (whether the variables tend to move in tandem or show an inverse relationship).\n<li>On the other hand, correlation measures the strength of the relationship between variables. Correlation is the scaled measure of covariance.\n<li>The values of covariance can be any number between the two opposite infinities. Also, it’s important to mention that covariance only measures how     two variables change together, not the dependency of one variable on another one.\n <h3>-Test</h3>\n     <li>ANCOVA","metadata":{}},{"cell_type":"markdown","source":"<div class=\"alert alert-danger\" role=\"alert\">\n    <h1><span class=\"label label-success\">Causation</span></h1> \n    <ul type = \"circle\" style=\"font-size:17px;\">\n<li>Causation is same as correlation but what it means is that one variable will make another to surely occur\n<li>Causation is implying that A and B have a cause-and-effect relationship with one another. Saying event A causes event B. \n<li>Firstly, causation means that two events appear at the same time or one after the other. \n<li>And secondly, it means these two variables not only appear together, the existence of one causes the other to occur. \n    <h3>-Test</h3>\n  <li>Granger Test ","metadata":{}},{"cell_type":"markdown","source":"<div class=\"alert alert-success\" role=\"alert\">\n     <h1><span class=\"label label-success\">Collinearity</span></h1> \n    <ul type = \"circle\" style=\"font-size:17px;\">\n<li>In statistics, correlation between predictor variables (independent variables) and the target variable(dependent variable) is called as collinearity. When multiple predictor variables in the same regression model are correlated, they cannot independently predict the value of the dependent variable. In other words, they explain some of the same variance in the dependent variable, which in turn reduces their statistical significance.This condition is called as multicollinearity \n<li>In other words Multicollinearity is the occurrence of high intercorrelations among two or more independent variables in a multiple regression model \n<h3>Signs to detect </h3>\n<li>Large changes in coefficients when adding predictors \n<li>Very high standard errors for regression coefficients \n<li>The overall model is significant, but none of the coefficients are. \n<li>High Variance Inflation Factor (VIF). \n<li>High Condition Indices ","metadata":{}},{"cell_type":"markdown","source":"<h2 style=\"color:red\"> Happy learning !!</h2> ","metadata":{}}]}