Regression

Correlation and Regression Analysis, simple and multiple regression, regression, regression diagnostics, Assumptions of the regression model, model selection criteria, Linear Regression models, Relationship between Variables, Strength, and direction of the relationship

Checking Normality of Error Term

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Normality of Error Term In multiple linear regression models, the sum of squared residuals (SSR) is divided by $n-p$ (degrees of freedom, where $n$ is the total number of observations, and $p$ is the number of the parameter in the model) is a good estimate of the error variance. In …

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Binary Logistic Regression Minitab Tutorial

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Binary Logistic Regression is used to perform logistic regression on a binary response (dependent) variable (a variable only that has two possible values, such as the presence or absence of a particular disease, this kind of variable is known as a dichotomous variable i.e. binary in nature). Binary Logistic Regression …

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Multiple Regression Model Introduction (2015)

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A multiple regression model (a regression having multi-variable) is referred to as a regression model having more than one predictor (independent and explanatory variable) to explain a response (dependent) variable. We know that simple regression models have one predictor used to explain a single response while for the case of …

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Logistic regression Introduction (2015)

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Logistic regression was introduced in the 1930s by Ronald Fisher and Frank Yates and was first proposed in the 1970s as an alternative technique to overcome the limitations of ordinary least square regression in handling dichotomous outcomes. It is a type of probabilistic statistical classification model which is a non-linear …

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