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Assumptions of Multiple Linear Regression on Time Series Data

Multiple linear regression is a statistical analysis technique used to model the relationship between one dependent variable and two or more independent variables. The multiple linear regression model is used to predict the value of the dependent variable based on the estimated values of the independent variables.

Analysis of Cobb-Douglas Production Function: Theoretical Basics and Case Study Examples

In economics, production function analysis is an essential tool for understanding how inputs like labor and capital are transformed into output. One of the most renowned and widely used models for analyzing this relationship is the Cobb-Douglas production function.

Understanding the Profit Formula in Financial Analysis and Examples of Its Calculation

In the business world, achieving optimal profit is a goal sought by entrepreneurs. In financial analysis, knowledge of profit calculation is a fundamental skill that entrepreneurs need to possess.

What to Do If the Regression Coefficient Is Negative?

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Linear regression is one of the most commonly used statistical analysis techniques to understand the impact of independent variables on a dependent variable. In regression analysis, the estimated coefficients indicate the extent to which each independent variable affects the dependent variable.

Why Should Data Transformation Be Done Only Once?

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Data transformation is an essential step in inferential statistical analysis. It can be a solution to ensure that research data meets certain required statistical model assumptions, such as normality, linearity, and homoscedasticity.