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PRECALCULUS I

PRECALCULUS I. Mathematical Modeling Direct, inverse, joint variations; Least squares regression. Dr. Claude S. Moore Danville Community College. Direct Variation Statements. 1. y varies directly as x. 2. y is directly proportional to x. 3. y = mx for some nonzero constant m.

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PRECALCULUS I

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  1. PRECALCULUS I • Mathematical Modeling • Direct, inverse, joint variations;Least squares regression Dr. Claude S. MooreDanville Community College

  2. Direct Variation Statements 1. y varies directly as x. 2. y is directly proportional to x. 3. y = mx for some nonzero constant m. NOTE: m is the constant of variation or the constant of proportionality. Example: If y = 3 when x = 2, find m.y = mx yields 3 = m(2) or m = 1.5. Thus, y = 1.5x.

  3. Direct Variation as nth Power 1. y varies directly as the nth power of x. 2. y is directly proportional to the nth power of x. 3. y = kxn for some nonzero constant k. NOTE: k is the constant of variation or constant of proportionality.

  4. Inverse Variation Statements 1. y varies inversely as x. 2. y is inversely proportional to x. 3. y = k / x for some nonzero constant k. NOTE: k is the constant of variation or the constant of proportionality. Example: If y = 3 when x = 2, find k.y = k / x yields 3 = k / 2 or k = 6. Thus, y = 6 / x.

  5. Joint Variation Statements 1. z varies jointly as x and y. 2. z is jointly proportional to x and y. 3. y = kxy for some nonzero constant k. NOTE: k is the constant of variation. Example: If z = 15 when x = 2 and y = 3,find k.y = kxy yields 15 = k(2)(3) or k = 15/6 = 2.5. Thus, y = 2.5xy.

  6. Least Squares Regression This method is used to find the “best fit” straight line y = ax + bfor a set of points, (x,y), in the x-y coordinate plane.

  7. Least Squares Regression Line The “best fit” straight line, y = ax + b, for a set of points, (x,y), in the x-y coordinate plane.

  8. Least Squares Regression Line X Y X2 XY 1 3 1 3 2 5 4 10 4 5 16 20 å 7 13 21 33 Solving for a = 0.57 and b = 3, yields y = 0.57x + 3.

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