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** Eram Khan ^{*}**

Department of Mathematics, Tel Aviv University, Tel Aviv, Israel

- *Corresponding Author:
- Eram Khan

Department of Mathematics, Tel Aviv University, Tel Aviv, Israelkhan.eram23@gmail.com

E-mail:

**Received:** 23-May-2023, Manuscript No. JSMS-23-99756; **Editor assigned:** 25-May-2023, Pre QC No. JSMS-23-99756 (PQ); **Reviewed:** 08-Jun-2023, QC No. JSMS-23-99756; **Revised:** 15-Jun-2023, Manuscript No. JSMS- 23-99756 (A); **Published:** 22-Jun-2023, DOI: 10.4172/J Stats Math Sci.9.2.001

**Citation: ** Khan E. Regression Analysis: An Overview of Techniques for Modelling Relationships between Variables. J Stats Math Sci. 2023;9:001.

**Copyright:** © 2023 Khan E. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Visit for more related articles at** Research & Reviews: Journal of Statistics and Mathematical Sciences

Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It is a powerful tool used in many fields, including economics, finance, biology, and engineering. Regression analysis is used to predict future outcomes, understand the relationship between variables, and to identify factors that affect the dependent variable. Simple linear regression is the most basic form of regression analysis. It involves modelling the relationship between a single independent variable and a dependent variable. The goal is to find a line that best fits the data, minimizing the sum of the squared differences between the observed values and the predicted values. Multiple linear regression involves modelling the relationship between a dependent variable and two or more independent variables. The goal is to find a linear equation that best fits the data, minimizing the sum of the squared differences between the observed values and the predicted values. Multiple linear regression allows us to model complex relationships between variables. It is used to identify factors that affect the dependent variable and to predict future outcomes based on the values of the independent variables.

Logistic regression is a type of regression analysis used to model the relationship between a dependent variable and one or more independent variables. The dependent variable in logistic regression is binary, meaning it takes on one of two values (0 or 1). Logistic regression is used to model the probability of the dependent variable being equal to 1 as a function of the independent variables. Logistic regression is used in many fields, including medicine, finance, and marketing. It is used to predict the probability of a certain outcome, such as whether a patient has a particular disease or whether a customer will buy a product. Time series regression is a type of regression analysis used to model the relationship between a dependent variable and time. It is used to predict future values of the dependent variable based on past values and other independent variables. Time series regression is used in many fields, including finance, economics, and engineering. It is used to predict future trends and to identify factors that affect the dependent variable over time.

Regression analysis is a powerful tool used to model the relationship between a dependent variable and one or more independent variables. It is used to predict future outcomes, understand the relationship between variables, and to identify factors that affect the dependent variable. Simple linear regression is the most basic form of regression analysis and involves modeling the relationship between a single independent variable and a dependent variable. Multiple linear regression involves modeling the relationship between a dependent variable and two or more independent variables. Logistic regression is used to model the probability of a binary dependent variable as a function of the independent variables. Time series regression is used to model the relationship between a dependent variable and time. Together, these techniques allow us to model complex relationships between variables and to make predictions about future outcomes.

** Eram Khan ^{*}**

Department of Mathematics, Tel Aviv University, Tel Aviv, Israel

- *Corresponding Author:
- Eram Khan

Department of Mathematics, Tel Aviv University, Tel Aviv, Israel**E-mail:**khan.eram23@gmail.com

**Received:** 23-May-2023, Manuscript No. JSMS-23-99756; **Editor assigned:** 25-May-2023, Pre QC No. JSMS-23-99756 (PQ); **Reviewed:** 08-Jun-2023, QC No. JSMS-23-99756; **Revised:** 15-Jun-2023, Manuscript No. JSMS- 23-99756 (A); **Published:** 22-Jun-2023, DOI: 10.4172/J Stats Math Sci.9.2.001

**Citation: ** Khan E. Regression Analysis: An Overview of Techniques for Modelling Relationships between Variables. J Stats Math Sci. 2023;9:001.

**Copyright:** © 2023 Khan E. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. It is a powerful tool used in many fields, including economics, finance, biology, and engineering. Regression analysis is used to predict future outcomes, understand the relationship between variables, and to identify factors that affect the dependent variable. Simple linear regression is the most basic form of regression analysis. It involves modelling the relationship between a single independent variable and a dependent variable. The goal is to find a line that best fits the data, minimizing the sum of the squared differences between the observed values and the predicted values. Multiple linear regression involves modelling the relationship between a dependent variable and two or more independent variables. The goal is to find a linear equation that best fits the data, minimizing the sum of the squared differences between the observed values and the predicted values. Multiple linear regression allows us to model complex relationships between variables. It is used to identify factors that affect the dependent variable and to predict future outcomes based on the values of the independent variables.

Logistic regression is a type of regression analysis used to model the relationship between a dependent variable and one or more independent variables. The dependent variable in logistic regression is binary, meaning it takes on one of two values (0 or 1). Logistic regression is used to model the probability of the dependent variable being equal to 1 as a function of the independent variables. Logistic regression is used in many fields, including medicine, finance, and marketing. It is used to predict the probability of a certain outcome, such as whether a patient has a particular disease or whether a customer will buy a product. Time series regression is a type of regression analysis used to model the relationship between a dependent variable and time. It is used to predict future values of the dependent variable based on past values and other independent variables. Time series regression is used in many fields, including finance, economics, and engineering. It is used to predict future trends and to identify factors that affect the dependent variable over time.

Regression analysis is a powerful tool used to model the relationship between a dependent variable and one or more independent variables. It is used to predict future outcomes, understand the relationship between variables, and to identify factors that affect the dependent variable. Simple linear regression is the most basic form of regression analysis and involves modeling the relationship between a single independent variable and a dependent variable. Multiple linear regression involves modeling the relationship between a dependent variable and two or more independent variables. Logistic regression is used to model the probability of a binary dependent variable as a function of the independent variables. Time series regression is used to model the relationship between a dependent variable and time. Together, these techniques allow us to model complex relationships between variables and to make predictions about future outcomes.