Probability Mass Function (PMF) of X says how the total probability of 1 is distributed (allocated to) among the various possible X values.
Multiple Linear Regression Analysis with Categorical Predictors is done using Indicator Variables. We have to clearly analyze all possible models and select the best fitting model.
We use indicator variables when we have categorical variables in the Regression Equation. They are also known as Dummy Variables.
Multiple Linear Regression helps us to make predictions using two or more predictor variables. Concept of Multicollinearity is also very important in Regression Analysis.
Linear regression is a way of modeling variables to make predictions. Here we discuss how can we do a simple linear regression analysis using Microsoft Excel.
Expected Value is the average value we get for a certain Random Variable when we repeat an experiment a large number of times. It is the theoretical mean of a Random Variable. Expected Value is based on population data. Therefore it is a parameter.
Simple Linear Regression is used to model the relationship between one predictor variable and one response variable. This is called ‘linear because the said relationship can be expressed in the form of the equation ‘y = mx + x”. And it is called ‘Simple’ because only one Predictor Variable is involved.
What is Regression Analysis? Regression Analysis is a Statistical Technique used to investigate and model the relationship between variables. It helps to identify trends associated with data and quantify them. In regression analysis, there are 2 main types of variables. Predictor Variable (Independent Variable) and Dependent Variable (Response Variable). For example, let’s say we use…