Research Consultancy

𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥:

• Linear regression model is a statistical method that is used to examine the relationship between two continuous variables: independent variable (X) and dependent variable (Y).

• In this model, a linear equation is used to predict the value of Y based on the value of X.

• The goal of a linear regression model is to find the line of best fit that minimizes the distance between the actual values and the predicted values.

• The equation for a linear regression model is y = b0 + b1x, where b0 is the y-intercept and b1 is the slope of the line.

• Points in a linear regression model are used to plot the data and determine the line of best fit.

𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥:

• A logistic regression model is a statistical method used to analyze the relationship between a binary dependent variable y and one or more independent variables (x).

• Unlike linear regression, logistic regression models are used to model the relationship between two binary outcomes.

• The goal of a logistic regression model is to determine the probability of a certain event occurring based on the values of the independent variables.

• The equation for a logistic regression model is P(y) = 1/(1 + e^(-b0 - b1x)), where b0 is the y-intercept and b1 is the slope of the line.

• Points in a logistic regression model are used to plot the data and determine the probability of a certain event occurring based on the values of the independent variables.

𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥:

• A multiple regression model is a statistical method used to analyze the relationship between a dependent variable y and multiple independent variables (x).

• In this model, multiple regression equation is used to predict the value of Y based on the values of multiple X variables

• The goal of a multiple regression model is to determine the relationship between the dependent variable and each independent variable and to find the equation that minimizes the distance between the actual values and the predicted values.

• The equation for a multiple regression model is y = b0 + b1x1 + b2x2 + ... + bnxn, where b0 is the y-intercept, b1, b2, ..., bn are the slopes of the lines, and x1, x2, ..., xn are the independent variables.

• Points in a multiple regression model are used to plot the data and determine the relationship between the dependent variable and each independent variable.

Compiled & Organized by: Abdalla Haji