# Why do we use linear regression?

In simple

**linear regression**a single independent variable is**used**to predict the value of a dependent variable. In multiple**linear regression**two or more independent variables are**used**to predict the value of a dependent variable. The difference between the two is the number of independent variables.A.

### Why do we use a regression model?

**Regression analysis**is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances,

**regression analysis**can be used to infer causal relationships between the independent and dependent variables.

#### What does a regression analysis tell you?

In statistical modeling,**regression analysis**is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').#### Why do we use correlation analysis?

**Correlation analysis**is a method of statistical evaluation used to study the strength of a relationship between two, numerically measured, continuous variables (e.g. height and weight). This particular type of**analysis**is useful when a researcher wants to establish if there are possible connections between variables.#### What is meant by regression in statistics?

**Regression**is a**statistical**measure used in finance, investing and other disciplines that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing**variables**(known as independent**variables**).

B.

### What does the line of regression tell you?

The slope of a

**regression line**(b) represents the rate of change in y as x changes. Because y is dependent on x, the slope describes the predicted values of y given x. The slope of a**regression line**is used with a t-statistic to test the significance of a linear relationship between x and y.#### What does the intercept of a regression tell?

The**intercept**(often labeled the constant) is the expected mean value of Y when all X=0. Start with a**regression**equation with one predictor, X. If X sometimes = 0, the**intercept**is simply the expected mean value of Y at that value.#### What is a regression line for?

A scatter plot of the example data. Linear**regression**consists of finding the best-fitting straight**line**through the points. The best-fitting**line**is called a**regression line**. The black diagonal**line**in Figure 2 is the**regression line**and consists of the predicted score on Y for each possible value of X.#### What is the definition of the regression line?

**Definition**. A**regression line**is a straight**line**that de- scribes how a response variable y changes as an explanatory variable x changes. We often use a**regression line**to predict the value of y for a given value of x.

Updated: 21st November 2019