Which of the following stafements best describes why a linear regression is also called a least squares reģression mbdet? o a. A linear regression is alsp called IER ZHE Sauare of each actual y datä value and the'predictëd y value. regression model because the b. A linear regression is aISPcallednISIRA ZHE SUm orthe square of the differences regression line is calculąted by between each actual y a least sguares regression model þecause the válue and thể predictèd ý válue. iş also called a least şguares regressipn model because the O AInear fegressiorCliafed by'inmising tHe sum of the difference bětween êách actual régressioh line y dată valuë and the ý value. d. A linear regression line is calculated by minimising, the square of the difference between each actual x datä value and the predictëd x value. regression iş alsp,called a least sguares regression model because the
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
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