STATISTICAL REGRESSIONS Meaning and
Definition
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Statistical regressions, also known as regression analysis, refer to a collection of statistical techniques used to examine and quantify the relationship between a dependent variable and one or more independent variables. It is a widely utilized method in various disciplines, including social sciences, economics, and healthcare research.
In statistical regressions, the dependent variable represents the outcome or response of interest, while the independent variables are factors or predictors that may potentially influence the dependent variable. The goal is to understand how changes in the value of the independent variables impact or explain the variations in the dependent variable.
Regression analysis includes various models, such as simple linear regression, multiple linear regression, logistic regression, and polynomial regression, among others. These models estimate the mathematical relationship between the variables by fitting the observed data to a specific mathematical equation or line.
The analysis involves determining the regression coefficients, which represent the changes in the dependent variable associated with a unit change in the independent variable, while taking into account the effects of all the other independent variables.
Assumptions for conducting statistical regressions typically include linearity (the relationship between variables is linear), independence of observations, normality of the residuals (errors), constant variance of residuals (homoscedasticity), absence of multicollinearity (high correlation among independent variables), and absence of influential outliers.
Statistical regressions are widely used in research and data analysis to examine relationships, make predictions or forecasts, identify significant variables, and understand the effects of various factors on the outcome of interest.
Common Misspellings for STATISTICAL REGRESSIONS
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Etymology of STATISTICAL REGRESSIONS
The term "regression" has its roots in the work of Sir Francis Galton, a 19th-century English mathematician and scientist. Galton coined the term "regression" in reference to his observations on the hereditary nature of various traits. He studied the correlation between the heights of fathers and sons and noted that extreme values of fathers' heights tended to be less extreme in their sons, thus "regressing" towards the mean height of the population.
Later, the concept of regression was further developed by Karl Pearson, who introduced the idea of the "correlation coefficient" to quantify the degree of relationship between two variables. Pearson's work focused on the statistical analysis of observations and the calculation of regression equations to predict one variable from another.
The term "statistical regression" emerged as a way to describe the mathematical and statistical methods used in studying and analyzing relationships between variables.
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