Linear Regression

Linear regression is defined as the method of organizing data. It evaluates the relationship between two variables X and Y. linear regression is determined as an algorithm for drawing straight lines through the data to assess the outcome of single independent variable. The term regression is used in many statistical terms but now it is used for many types of curve fitting. The best fitting line is called regression line.

• Its objective:  the aim of linear regression is to find the line that finely forecasts y from x. it is done by discovering the line that minimizes the sum of squares of the perpendicular distances of points from the line.
• Linear regression does not do any test to check whether your data are linear whereas it takes for granted that your data are linear. It determines the slope and intercept that formulates a straight line best fit your data.

The Mechanism Of Linear Regression:

• The main aim of linear regression is to alter the values of slope and intercept to get the line that best predicts Y from X.
• Most accurately its main aim is to lessen the sum of squares of the vertical distances of the points from the line.
• It minimizes because it follows the Gaussian distribution. It has two medium sized deviations rather than one small sized deviation.
•  It follows the process of minimizing the sum of squares basically to a distance.
• Prism informs the best-fit values of the slope and intercept, all along with their usual errors and confidence intervals. it also determines the graphs and best fit linear regression line.
• The slope enumerates the sharpness of the line. It equals the alteration in Y for each unit change in X. It is expressed in the units of the Y-axis divided by the units of the X-axis.
• Y axis increases as X axis increases if slope is positive but if slope is negative, Y axis decreases as X axis increases.
•  the Y intercept describes the elevation of the line and Y intercept is the Y value of the line and is X equals zero

Questions:

• Define linear regression?
• How does linear regression works?