Linear Correlation and Regression
Data-Import Version
For a sample of N bivariate values of X and Y, this page will calculate:T
  • r = the Pearson product-moment correlation coefficient;T
  • r2 = the coefficient of determination;T
  • the slope of the regression line;T
  • the Y intercept of the regression line;T
  • the standard error of estimate;T
  • the value of t associated with the calculated value of r, along with the corresponding one- and two-tailed probabilities;T
  • the residual for each value of Y, calculated as
    residual = Y(intercept+[slope(X)])T
  • the lower and upper limits of the .95 and .99 confidence intervals for the correlation coefficient (rho) that exists within the bivariate population from which the sample is drawn;     andT
  • the lower and upper limits of the .95 and .99 confidence intervals for the slope of the regression.
The logic and computational details of correlation and regression
are described in Chapter 3 of Concepts and Applications.
Data Entry
Data Report
Please remember to
perform the Data Check
procedure.
Column 1: X
Column 2: Y
Column 3: Residual
Data SummaryT
iX =
iX2i =
iY = iY2i =
iXY =
X Y
Mean 
Variance 
Std.Dev. 
Std.Err. 
r r2 Slope Y
Intercept
Std. Err. of
Estimate
t df
  P    one-tailed 
 two-tailed 

0.95 and 0.99 Confidence Intervals for rhoQ
Lower Limit Upper Limit
0.95
0.99

0.95 and 0.99 Confidence Intervals for the Slope of the RegressionQ
Lower Limit Upper Limit
0.95
0.99


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