Correlation and Regression

Correlation and Regression

Correlation(상관분석)

display relationships between two variables by means of tables and by means of graphs.

• When the variables in the study are measured on the nominal -> contingency table
• When they are measured on a quantitative level -> scatterplot
  

  Pearson’s r

  shows us the direction and exact strength of the linear relationship between two qualititative variables.

• Pearson’s r is always a number between -1 and 1.
  • -1 refers to a perfect negative correlation
  • +1 to a perfect positive correlation
  • 0 means that there is no correlation at all

Regression(회귀 분석)

Describing the line

y^ = a + bx

• y^ = predicted value of y
• a = intercept(절편)

• b = regression coefficient(회귀계수, 종속변수에 미치는 영향력)

  
  
  

  How good is the line?
  

  Squared Residual(RSS, 잔차 제곱합)
  

  a measure of the discrepancy between the data and an estimation model(ex. linear regression)

• For example, r-squared is 0.69. This means that the prediction error is 69% smaller when we use the regression line than when we employ the mean.