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What we will learn? ,[object Object],[object Object],[object Object]
 
Intro ,[object Object],[object Object],[object Object],y x
Linear Regression ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],y x
Linear Regression - Example ,[object Object],Pressure to Leak 70 76 54 66 64 74 76 62 54 68
Linear Regression - Example ,[object Object],[object Object],[object Object],[object Object],y x 64 26 54 28 62 34 68 36 70 42 54 18 76 48 66 52 76 54 74 60
Linear Regression – Scatter Diagram ,[object Object]
Linear Regression – Least Squares ,[object Object]
Linear Regression – Formula ,[object Object],[object Object], 0 = the y intercept  1 = the slope of the regression line  0
Linear Regression – Best Fit Line y x x 2 xy 64 26 676 1664 54 28 784 1540 62 34 1156 2108 68 36 1296 2448 70 42 1764 2940 54 18 324 972 76 48 2304 3648 66 52 2704 3432 76 54 2916 4104 74 60 3600 4440 664 398 17524 27268
Linear Regression – Equation Pressure to Leak = 46.5 + 0.499(Adhesive Amount) We have established the relationship between our variables
 

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R E G R E S S I O N A N A L Y S I S

  • 1.  
  • 2.
  • 3.  
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11. Linear Regression – Best Fit Line y x x 2 xy 64 26 676 1664 54 28 784 1540 62 34 1156 2108 68 36 1296 2448 70 42 1764 2940 54 18 324 972 76 48 2304 3648 66 52 2704 3432 76 54 2916 4104 74 60 3600 4440 664 398 17524 27268
  • 12. Linear Regression – Equation Pressure to Leak = 46.5 + 0.499(Adhesive Amount) We have established the relationship between our variables
  • 13.