Category: Simple Linear Regression
Interpreting Negative Intercept in Regression
When conducting regression analysis, we obtain the intercept and coefficient estimates for each independent variable. These values, both intercept and coefficients, can be positive or negative.
Calculating Predicted Y and Residual Values in Simple Linear Regression
Residual values in linear regression analysis need to be calculated for several purposes. In linear regression using the ordinary least squares method, one of the assumptions that must be met is that residuals must be normally distributed, hence the necessity to first calculate residual values. However, before calculating the residual values, we need to first calculate the predicted Y values. Therefore, on this occasion, we will discuss how to calculate predicted Y values and residual values.
Calculation Formula for the Coefficient of Determination (R Square) in Simple Linear Regression
The coefficient of determination plays a crucial role in regression analysis. It is not surprising that various studies using regression analysis often present the value of the coefficient of determination. Recognizing the importance of this value, Kanda Data will discuss this topic in detail.
Simple Linear Regression Analysis Easily Using Excel
Simple linear regression analysis is a useful statistical technique for measuring and understanding the relationship between two variables. In this analysis, one variable (independent variable) is used to predict or explain the other variable (dependent variable).
Tutorial on How to Calculate Residual Values in Excel
Regression analysis is already widely used by researchers to explore the influence of independent variables on dependent variables. If we use regression analysis, we must have a good understanding of residual values. These residual values are needed in regression analysis. In addition, in the assumption tests required in linear regression analysis using the ordinary least square method, some also use residual values.
Simple Linear Regression Analysis in Excel and How to Interpret the Results
Simple linear regression analysis aims to determine the influence of one independent variable on a dependent variable. In this analysis, we can understand and measure how much the independent variable explains the variation in the dependent variable.
How to Interpret Negative Coefficients of Linear Regression Output
Regression analysis is commonly used by researchers to analyze influence of independent variables on a dependent variable. But what if the estimated regression coefficient turns out to be negative and its influence is significant? In this article, we will explore and review the based on a case study where the estimation coefficient is negative and how to interpret it in an academic context.
How to Perform Linear Regression using Data Analysis in Excel
Researchers have widely used linear regression analysis to analyze the effect of a variable on other variables. Linear regression analysis consists of the dependent variable and the independent variable. The difference between the two is that the dependent variable is the affected variable, while the independent variable is the influencing variable.