Pearson correlation is one of the correlation measures used to measure the strength and direction of the linear relationship of two variables while linear regression is a statistical analysis technique used to estimate the functional relationship between two or more variables, namely between the dependent variables (dependent; response; response; Y) with one or more independent variables (independent, predictor, X).

Linear regression is the next step of correlation analysis . In correlation analysis, we only ask whether there is a relationship between the two variables, while in regression analysis it is more directed to a more complete process, namely studying the causal relationship between the two variables or answering how the two variables are related. More specifically, the regression equation is a mathematical equation that allows us to predict the values of the dependent variable (Y) from the value of the independent variable (X) thus allowing us to predict one parameter based on the value of another variable that we know.

You can learn more about Excel: Linear Correlation and Regression in the following video.

Excel Tutorial: Linear Correlation and Regression

• To get the correlation coefficient value :
  • Syntax: CORREL(array1,array2)
    • Array1 is a cell range of values.
    • Array2 is a second cell range of values.

• To get the Linear Regression Equation: Y = mx + b
  • the gradient value (m) is obtained by:
    • Syntax: SLOPE(known_y's,known_x's)
      • Known_y's is an array or cell range of numeric dependent data points.
      • Known_x's is the set of independent data points.
  • The value of the constant (intercept, b) is obtained by:
    • Syntax: INTERCEPT(known_y's,known_x's)
      • Known_y's is the dependent set of observations or data.
      • Known_x's is the independent set of observations or data.

Excel Tutorial: Linear Correlation and Regression

List of other Microsoft Excel Tutorials , you can learn at the following link:

Tutorial Microsoft Excel

Excel Tutorial: Linear Correlation and Regression