Data and Tech

 What is Regression Analysis? Regression Analysis is a set of statistical processes to estimate the relationship between a dependent variable and one or more independent variables.  One of the regression analysis types is Linear regression. Linear regression simply assumes that there is a linear relationship between the dependent and the independent variable(s). The following is a general formula for Linear Regression. Y i = f(X i , β ) + e i Y = Dependent Variable X = Independent Variable e = Error term β  = Parameters i = 1,2,....n e is an important term here. e accounts for the unexplainable variations by the model. If the error term e is greater than  β terms, then our model is not a good one. We need to search for other independent variables that will help better explain our dependent variable.  R-Square value is another important thing we have to look out for. It provides percentage variability explained by the independent variables. The lower this value,...

What is hypothesis testing? Assume that we have a coffee shop. Since we always look for ways to improve the business, we decided to offer free wifi for our customers. We believe customers spent more time in the shop after the introduction of this new change. The data collected also shows an increase in the meantime spent by the customers. But wait, how can we make sure that it is not due to pure random chance? This is where hypothesis tests are used. It helps us to make sure that we are giving in to some random effects. We make a null and alternate hypothesis statement to test the effect.  After collecting the data of time spent by customers, before and after the change, we can use one of the statistical methods available to verify the effect. The most appropriate one for this problem is Z-test. P-value is very useful and helps in determining the chance of having the result due to pure random chance. The lower the p-value, the lower the chance of randomness. Accepting or rejecting ...