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First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables.
- Regression Analysis – Linear Model Assumptions
- Regression Analysis – Simple Linear Regression
- Regression Analysis – Multiple Linear Regression
- Regression Analysis in Finance
- Regression Tools
- Additional Resources
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Linear regression analysis is based on six fundamental assumptions: 1. The dependent and independent variables show a linear relationship between the slope and the intercept. 2. The independent variable is not random. 3. The value of the residual (error) is zero. 4. The value of the residual (error) is constant across all observations. 5. The value...
Simple linear regression is a model that assesses the relationship between a dependent variable and an independent variable. The simple linear model is expressed using the following equation: Where: 1. Y– Dependent variable 2. X– Independent (explanatory) variable 3. a– Intercept 4. b– Slope 5. ϵ– Residual (error) Check out the following video to l...
Multiple linear regression analysis is essentially similar to the simple linear model, with the exception that multiple independent variables are used in the model. The mathematical representation of multiple linear regression is: Where: 1. Y– Dependent variable 2. X1, X2, X3 – Independent (explanatory) variables 3. a– Intercept 4. b, c, d– Slopes ...
Regression analysis comes with several applications in finance. For example, the statistical method is fundamental to the Capital Asset Pricing Model (CAPM). Essentially, the CAPM equation is a model that determines the relationship between the expected return of an asset and the market risk premium. The analysis is also used to forecast the return...
Excel remains a popular tool to conduct basic regression analysis in finance, however, there are many more advanced statistical tools that can be used. Python and R are both powerful coding languages that have become popular for all types of financial modeling, including regression. These techniques form a core part of data science and machine lear...
To learn more about related topics, check out the following free CFI resources: 1. Cost Behavior Analysis 2. Forecasting Methods 3. Joseph Effect 4. R-Squared 5. Variance Inflation Factor (VIF) 6. See all data science resources
Learn what regression analysis is, how it works, and how to apply it in finance. Find out the difference between simple and multiple linear regression, the assumptions, and the tools.
Jul 31, 2024 · Regression analysis is a statistical method that relates a dependent variable to one or more independent variables. Learn about linear, multiple, and nonlinear regression, how to calculate regression lines, and how regression is used in finance and economics.
- Brian Beers
- 1 min
Mar 25, 2024 · Learn how to use regression analysis to estimate and model the relationships among variables. Explore different types of regression models, such as linear, multiple, logistic, polynomial, and more, with formulas and examples.
Feb 19, 2020 · Learn how to use simple linear regression to estimate the relationship between two quantitative variables. See examples, assumptions, formulas, and how to perform a regression analysis in R.
- A regression model is a statistical model that estimates the relationship between one dependent variable and one or more independent variables usin...
- Simple linear regression is a regression model that estimates the relationship between one independent variable and one dependent variable using a...
- Linear regression most often uses mean-square error (MSE) to calculate the error of the model. MSE is calculated by: measuring the distance of the...
Nov 4, 2015 · Learn what regression analysis is, how it works, and why it is important for data-driven decisions. This article explains the basics of regression analysis with examples and tips for interpreting the results.
Learn how to perform regression analysis with various types, models, and methods. This tutorial covers selecting the right regression, specifying the model, interpreting the results, making predictions, and checking the assumptions with examples.
