"A set of statistical operations that helps to predict the value of the dependent variable from the values of one or more independent variables." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling 2nd Ed.", 2005)
"A statistical tool that measures the strength of relationship between one or more independent variables with a dependent variable. It builds upon the correlation concepts to develop an empirical, databased model. Correlation describes the X and Y relationship with a single number (the Pearson’s Correlation Coefficient (r)), whereas regression summarizes the relationship with a line - the regression line." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)
"A statistical procedure for estimating mathematically the average relationship between the dependent variable (e.g., sales) and one or more independent variables (e.g., price and advertising)." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)
"Regression analysis is a statistical technique for estimating the relationship between a set of predictors (independent variables) and an outcome variable (dependent variable). Linear least-squares regression, in which the relationship is expressed in a linear form, is the most common type of regression analysis. The mathematical model used in least-squares linear regression is often called the general linear model (GLM)." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)
"A statistical technique which seeks to find a line which best fits through a set of data as plotted on a graph, seeking to find the cleanest path which deviates the least from any instance within the set." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
[regression] "Using one data set to predict the results of a second." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"The statistical process of predicting one or more continuous variables, such as profit or loss, based on other attributes in the dataset." (Microsoft, "SQL Server 2012 Glossary", 2012)
"A family of methods for fitting a line or curve to a dataset, used to simplify or make sense of a number of apparently random data points." (Meta S Brown, "Data Mining For Dummies", 2014)
"An analytic technique where a series of input variables are examined in relation to their corresponding output results in order to develop a mathematical or statistical relationship." (For Dummies, "PMP Certification All-in-One For Dummies" 2nd Ed., 2013)
"A statistical technique for estimating relationships between variables." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)
"Process to statistically estimate the relationship between different attributes." (Sanjiv K Bhatia & Jitender S Deogun, "Data Mining Tools: Association Rules", 2014)
"Plotting pairs of independent and dependent variables in an XY chart and then finding a linear or exponential equation that best describes the plotted data." (E C Nelson & Stephen L Nelson, "Excel Data Analysis For Dummies", 2015)
"A statistical procedure that produces an equation for predicting a variable (the criterion measure) from one or more other variables (the predictor measures)." (K N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)
"A statistical technique used to estimate the mathematical relationship between a dependent variable, such as quantity demanded, and one or more explanatory variables, such as price and income." (Jeffrey M Perloff & James A Brander, "Managerial Economics and Strategy" 2nd Ed., 2016)
"A statistical process for estimating the relationships between variables, often used to forecast the change in a variable based on changes in other variables. Linear regression is used to analyze continuous variables, and logistic regression is used for discrete variables." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)
"In a machine learning context, regression is the task of assigning scalar value to examples." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)
"Algorithms used to predict values for new data based on training data fed into the system. Areas where regression in machine learning is used to predict future values include drug response modeling, marketing, real estate and financial forecasting." (Accenture)
"To define the dependency between variables. It assumes a one-way causal effect from one variable to the response of another variable." (Analytics Insight)
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