the simple regression not because it is easy, but because there is only one explanatory variable on the right-hand side of the equation .
It is a statistical method which focuses on the relationship between two continuous and quantitative variables. One dependent and one independent variable
It is a statistical method which focuses on the relationship between two continuous and quantitative variables. One dependent and one independent variable
It is a mathematical function in which one of the two variables can be calculated in terms of the other variable. The simplest case for this function is when we have only one independent variable that is associated with the dependent variable with a straight line relationship as follows:Y=bo+b1X+e
The mathematical function that shows the relationship between an independent variable and a dependent variable
As well as to the answer of Wisdom Okere, we can predict the dependent values from the knowledge of independent values. We can also measure the percent of dependent variation that contributed by independent variable.
Simple linear regression y =mx , having known m , x is used to get new values for . 'm' know is the regression that is looking back or with fixed slope metric
Simple linear regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables:
- One variable, denoted x, is regarded as the predictor, explanatory, or independent variable.
- The other variable, denoted y, is regarded as the response, outcome, or dependent variable.
Because the other terms are used less frequently today, we'll use the "predictor" and "response" terms to refer to the variables encountered in this course. The other terms are mentioned only to make you aware of them should you encounter them in other arenas. Simple linear regression gets its adjective "simple," because it concerns the study of only one predictor variable. In contrast, multiple linear regression, which we study later in this course, gets its adjective "multiple," because it concerns the study of two or more predictor variables.
regards
In simple linear regression, we predict scores on one variable from the scores on a second variable.The variable we are predicting is called the criterion variable and is referred to as Y. The variable we are basing our predictions on is called the predictor variable and is referred to as X. When there is only one predictor variable, the prediction method is called simple regression. In simple linear regression, the topic of this section, the predictions of Y when plotted as a function of X form a straight line.
Linear regression is a basic and commonly used type of predictive analysis. The overall idea of regression is to examine two things: (1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable? (2) Which variables in particular are significant predictors of the outcome variable, and in what way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable? These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables. The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable.
DearAhmad Battal
Use simple logistic regression when you have one nominal variable and one measurement variable, and you want to know whether variation in the measurement variable causes variation in the nominal variable.
http://www.biostathandbook.com/simplelogistic.html
https://onlinecourses.science.psu.edu/stat501/node/250
Regards
Houda
Simple Linear Regression s a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables: One variable, denoted x, is regarded as the predictor, explanatory, or independent variable.
Linear regression is a basic and commonly used type of predictive analysis. The overall idea of regression is to examine two things: (1) does a set of predictor variables do a good job in predicting an outcome (dependent) variable? (2) Which variables in particular are significant predictors of the outcome variable, and in what way do they–indicated by the magnitude and sign of the beta estimates–impact the outcome variable? These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables. The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable.
Source: https://www.statisticssolutions.com/what-is-linear-regression/
Regression is the application of Y = f (X) model to data when Y is continuous value in statistics.
In other words, fitting the model between the dependent variable (object variable) Y and the independent variable (explanatory variable) X on the continuous scale.
If X is one dimension, it is a single regression and if X is more than two dimensions it is called multiple regression. When Y is discrete, it is called classification.
The most basic model used in regression is a linear regression of the form Y = AX + B.
A simple regression is a linear statistical equation with one independent or predictor variable and one dependent or results variable looking at the correlation and significance of the relationship between the 2 variables as,well as whether there is a posiitive or negative relationship between the two variables. Linear regression is Y=AX+B . X is the predictor abd explanatory variable and Y is the continuous (Y = f (x) outcome results variable.
Dr Réagan
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