SlideShare a Scribd company logo
1 of 10
Understand the concept of a tangent line approximation Compare the value of the differential, dy, with the actual change in y, ∆y
Consider the equation for the tangent line for a differentiable function at the point (c, f(c)) or This tangent line is called the tangent line approximation of f(x) at c.  By choosing values of x sufficiently close to c, the values of y can be used as approximations for values of  f.   In other words, as x->c, the limit of y is f(c). slope
Ex. 1 p. 235  Using a tangent line approximation Find the tangent line approximation for  f(x) = 1 + sin x at the point (0, 1).  Then use the table to compare the y-values of the linear function with those of f(x) on an open interval containing x = 0. f ‘ (x) = cos x. Slope at (0, 1) = cos 0 = 1 Tangent line is  y – f(0) = f ‘(0)(x – 0) y – 1 = 1(x – 0) y = x + 1 x -0.5 -0.1 -0.01 0 0.01 0.1 0.5 f(x) = 1 + sin x 0.521 0.9002 0.9900002 1 1.0099998 1.0998 1.479 y = x + 1 0.5 0.9 0.99 1 1.01 1.1 1.5
Differentials When ∆x is small, ∆y = f(c + ∆x) – f(c) is       ≈  f ‘(c) ∆x  which we’ll call dy
For such an approximation, the quantity ∆x = dx, and is called the differential of x.  Then  ∆y ≈ dy as defined below. In many types of applications, the differential of y can be used as an approximation of the change in y.  That is,  ∆y ≈ dy,  or  ∆y ≈ f ‘(x) dx
Ex 2. p 236  Comparing ∆y and dy Let y = x 3   Find dy when x = 1 and dx = 0.01.  Compare this value with ∆y for x = 1 and ∆x = 0.01 Solution:  f ‘(x) = 3x 2  .  dy = f ‘(x)dx = f ‘(1)(0.01) = 3(0.01) = 0.03 Now, using ∆x = 0.01, the change in y is  ∆ y = f(x + ∆x) – f(x) = f(1 + 0.01) – f(1) = (1.01) 3  – 1 3 = 1.030301 – 1    = 0. 030301 In this example, the tangent line at (1,1) is  y = 3x – 2  or g(x) = 3x – 2 .  Choosing x-values near 1,  f (1.01) = 1.030301  and  g(1.01) = 1.03
Error Propagation Physicists and engineers make liberal use of approximation dy to replace ∆y.  In practice this is in estimation of errors propagated by measuring devices. If  x  represents the measured value of variable, and  x + ∆ x  is the exact value, then  ∆x  is the  error in measurement .  If the measured value x is used to compute another value f(x), the difference between f(x + ∆x) and f(x) is the  propagated error . f(x + ∆x) – f(x) = ∆y
Example 3  p.237  Estimation of Error The radius of a ball bearing is measured to be 0.7 inches.  If the measurement is correct to within 0.01 inch, estimate the propagated error in the volume of the ball bearing. The formula for volume is  where r is the radius of the sphere.  So r = 0.7 (measured radius) and Differentiate V to obtain dV/dr = 4 π r 2 ∆ V ≈ dV = 4 π r 2 dr = 4 π (.7) 2 (  0.01) ≈   0.06158 cubic inches Is that a lot of change in volume?
The answer is best given in relative terms by comparing dV with V. This is called the  relative error  .  The corresponding  percent error  is approximately 4.29%
3.9a  p. 240/ 1-5 odd, 7-11 all, 13-33 every other odd

More Related Content

What's hot

Lesson 6 differentials parametric-curvature
Lesson 6 differentials parametric-curvatureLesson 6 differentials parametric-curvature
Lesson 6 differentials parametric-curvatureLawrence De Vera
 
8 arc length and area of surfaces x
8 arc length and area of surfaces x8 arc length and area of surfaces x
8 arc length and area of surfaces xmath266
 
Applications of maxima and minima
Applications of maxima and minimaApplications of maxima and minima
Applications of maxima and minimarouwejan
 
Numerical integration
Numerical integrationNumerical integration
Numerical integrationSunny Chauhan
 
Multi variable integral
Multi variable integralMulti variable integral
Multi variable integralJ M
 
The Application of Derivatives
The Application of DerivativesThe Application of Derivatives
The Application of Derivativesdivaprincess09
 
divergence of vector and divergence theorem
divergence of vector and divergence theoremdivergence of vector and divergence theorem
divergence of vector and divergence theoremAbhishekLalkiya
 
Application of Integrals
Application of IntegralsApplication of Integrals
Application of Integralssarcia
 
Maxima & Minima of Calculus
Maxima & Minima of CalculusMaxima & Minima of Calculus
Maxima & Minima of CalculusArpit Modh
 
Application of integral calculus
Application of integral calculusApplication of integral calculus
Application of integral calculusHabibur Rahman
 
APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONDhrupal Patel
 
A NEW STUDY OF TRAPEZOIDAL, SIMPSON’S1/3 AND SIMPSON’S 3/8 RULES OF NUMERICAL...
A NEW STUDY OF TRAPEZOIDAL, SIMPSON’S1/3 AND SIMPSON’S 3/8 RULES OF NUMERICAL...A NEW STUDY OF TRAPEZOIDAL, SIMPSON’S1/3 AND SIMPSON’S 3/8 RULES OF NUMERICAL...
A NEW STUDY OF TRAPEZOIDAL, SIMPSON’S1/3 AND SIMPSON’S 3/8 RULES OF NUMERICAL...mathsjournal
 
Math lecture 10 (Introduction to Integration)
Math lecture 10 (Introduction to Integration)Math lecture 10 (Introduction to Integration)
Math lecture 10 (Introduction to Integration)Osama Zahid
 
Derivatives and their Applications
Derivatives and their ApplicationsDerivatives and their Applications
Derivatives and their Applicationsusmancp2611
 

What's hot (20)

Lesson 6 differentials parametric-curvature
Lesson 6 differentials parametric-curvatureLesson 6 differentials parametric-curvature
Lesson 6 differentials parametric-curvature
 
8 arc length and area of surfaces x
8 arc length and area of surfaces x8 arc length and area of surfaces x
8 arc length and area of surfaces x
 
Divrgence theorem with example
Divrgence theorem with exampleDivrgence theorem with example
Divrgence theorem with example
 
Applications of maxima and minima
Applications of maxima and minimaApplications of maxima and minima
Applications of maxima and minima
 
Application of derivatives
Application of derivativesApplication of derivatives
Application of derivatives
 
Numerical integration
Numerical integrationNumerical integration
Numerical integration
 
Ch04
Ch04Ch04
Ch04
 
Application of Derivative 1
Application of Derivative 1Application of Derivative 1
Application of Derivative 1
 
Multi variable integral
Multi variable integralMulti variable integral
Multi variable integral
 
The Application of Derivatives
The Application of DerivativesThe Application of Derivatives
The Application of Derivatives
 
divergence of vector and divergence theorem
divergence of vector and divergence theoremdivergence of vector and divergence theorem
divergence of vector and divergence theorem
 
Ch02
Ch02Ch02
Ch02
 
Application of Integrals
Application of IntegralsApplication of Integrals
Application of Integrals
 
Maxima & Minima of Calculus
Maxima & Minima of CalculusMaxima & Minima of Calculus
Maxima & Minima of Calculus
 
Application of integral calculus
Application of integral calculusApplication of integral calculus
Application of integral calculus
 
APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATION
 
A NEW STUDY OF TRAPEZOIDAL, SIMPSON’S1/3 AND SIMPSON’S 3/8 RULES OF NUMERICAL...
A NEW STUDY OF TRAPEZOIDAL, SIMPSON’S1/3 AND SIMPSON’S 3/8 RULES OF NUMERICAL...A NEW STUDY OF TRAPEZOIDAL, SIMPSON’S1/3 AND SIMPSON’S 3/8 RULES OF NUMERICAL...
A NEW STUDY OF TRAPEZOIDAL, SIMPSON’S1/3 AND SIMPSON’S 3/8 RULES OF NUMERICAL...
 
Stoke’s theorem
Stoke’s theoremStoke’s theorem
Stoke’s theorem
 
Math lecture 10 (Introduction to Integration)
Math lecture 10 (Introduction to Integration)Math lecture 10 (Introduction to Integration)
Math lecture 10 (Introduction to Integration)
 
Derivatives and their Applications
Derivatives and their ApplicationsDerivatives and their Applications
Derivatives and their Applications
 

Viewers also liked

Derivative Flashcards To Memorize
Derivative Flashcards To MemorizeDerivative Flashcards To Memorize
Derivative Flashcards To Memorizejadunn
 
Ap and dual enrollment presentation
Ap and dual enrollment presentationAp and dual enrollment presentation
Ap and dual enrollment presentationhartcher
 
2.6b scatter plots and lines of best fit
2.6b scatter plots and lines of best fit2.6b scatter plots and lines of best fit
2.6b scatter plots and lines of best fithartcher
 
10.2 using combinations and the binomial theorem
10.2 using combinations and the binomial theorem10.2 using combinations and the binomial theorem
10.2 using combinations and the binomial theoremhartcher
 
Physics B - Basics of Capacitors
Physics B - Basics of Capacitors Physics B - Basics of Capacitors
Physics B - Basics of Capacitors Kenyon Hundley
 
Robert's derivative project
Robert's derivative projectRobert's derivative project
Robert's derivative projecthartcher
 

Viewers also liked (20)

Calc 1.2a
Calc 1.2aCalc 1.2a
Calc 1.2a
 
Calc 2.3b
Calc 2.3bCalc 2.3b
Calc 2.3b
 
Calc 3.6b
Calc 3.6bCalc 3.6b
Calc 3.6b
 
Calc 1.3a
Calc 1.3aCalc 1.3a
Calc 1.3a
 
Calc 3.9b
Calc 3.9bCalc 3.9b
Calc 3.9b
 
Derivative Flashcards To Memorize
Derivative Flashcards To MemorizeDerivative Flashcards To Memorize
Derivative Flashcards To Memorize
 
Calc 3.4b
Calc 3.4bCalc 3.4b
Calc 3.4b
 
Ap and dual enrollment presentation
Ap and dual enrollment presentationAp and dual enrollment presentation
Ap and dual enrollment presentation
 
2.6b scatter plots and lines of best fit
2.6b scatter plots and lines of best fit2.6b scatter plots and lines of best fit
2.6b scatter plots and lines of best fit
 
10.2 using combinations and the binomial theorem
10.2 using combinations and the binomial theorem10.2 using combinations and the binomial theorem
10.2 using combinations and the binomial theorem
 
Physics B - Basics of Capacitors
Physics B - Basics of Capacitors Physics B - Basics of Capacitors
Physics B - Basics of Capacitors
 
Calc 2.5b
Calc 2.5bCalc 2.5b
Calc 2.5b
 
Calc 2.4a
Calc 2.4aCalc 2.4a
Calc 2.4a
 
Robert's derivative project
Robert's derivative projectRobert's derivative project
Robert's derivative project
 
Calc 3.4
Calc 3.4Calc 3.4
Calc 3.4
 
Calc 3.7a
Calc 3.7aCalc 3.7a
Calc 3.7a
 
Calc 2.4b
Calc 2.4bCalc 2.4b
Calc 2.4b
 
Calc 2.2a
Calc 2.2aCalc 2.2a
Calc 2.2a
 
Calc 3.5
Calc 3.5Calc 3.5
Calc 3.5
 
Calc 3.5
Calc 3.5Calc 3.5
Calc 3.5
 

Similar to Calc 3.9a

Local linear approximation
Local linear approximationLocal linear approximation
Local linear approximationTarun Gehlot
 
Linear approximations and_differentials
Linear approximations and_differentialsLinear approximations and_differentials
Linear approximations and_differentialsTarun Gehlot
 
L15 the differentials & parametric equations
L15 the differentials & parametric equationsL15 the differentials & parametric equations
L15 the differentials & parametric equationsJames Tagara
 
3.7 applications of tangent lines
3.7 applications of tangent lines3.7 applications of tangent lines
3.7 applications of tangent linesmath265
 
Application of partial derivatives with two variables
Application of partial derivatives with two variablesApplication of partial derivatives with two variables
Application of partial derivatives with two variablesSagar Patel
 
Applicationofpartialderivativeswithtwovariables 140225070102-phpapp01 (1)
Applicationofpartialderivativeswithtwovariables 140225070102-phpapp01 (1)Applicationofpartialderivativeswithtwovariables 140225070102-phpapp01 (1)
Applicationofpartialderivativeswithtwovariables 140225070102-phpapp01 (1)shreemadghodasra
 
Lesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsLesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsMatthew Leingang
 
Continuous random variables and probability distribution
Continuous random variables and probability distributionContinuous random variables and probability distribution
Continuous random variables and probability distributionpkwilambo
 
Applications of partial differentiation
Applications of partial differentiationApplications of partial differentiation
Applications of partial differentiationVaibhav Tandel
 
Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Mel Anthony Pepito
 
Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Matthew Leingang
 
Applications of Differential Calculus in real life
Applications of Differential Calculus in real life Applications of Differential Calculus in real life
Applications of Differential Calculus in real life OlooPundit
 
math vysh.pptx
math vysh.pptxmath vysh.pptx
math vysh.pptxVyshali6
 
Project in Calcu
Project in CalcuProject in Calcu
Project in Calcupatrickpaz
 
IVS-B UNIT-1_merged. Semester 2 fundamental of sciencepdf
IVS-B UNIT-1_merged. Semester 2 fundamental of sciencepdfIVS-B UNIT-1_merged. Semester 2 fundamental of sciencepdf
IVS-B UNIT-1_merged. Semester 2 fundamental of sciencepdf42Rnu
 
25 surface area
25 surface area25 surface area
25 surface areamath267
 

Similar to Calc 3.9a (20)

Local linear approximation
Local linear approximationLocal linear approximation
Local linear approximation
 
Linear approximations and_differentials
Linear approximations and_differentialsLinear approximations and_differentials
Linear approximations and_differentials
 
L15 the differentials & parametric equations
L15 the differentials & parametric equationsL15 the differentials & parametric equations
L15 the differentials & parametric equations
 
3.7 applications of tangent lines
3.7 applications of tangent lines3.7 applications of tangent lines
3.7 applications of tangent lines
 
Application of partial derivatives with two variables
Application of partial derivatives with two variablesApplication of partial derivatives with two variables
Application of partial derivatives with two variables
 
The integral
The integralThe integral
The integral
 
Applicationofpartialderivativeswithtwovariables 140225070102-phpapp01 (1)
Applicationofpartialderivativeswithtwovariables 140225070102-phpapp01 (1)Applicationofpartialderivativeswithtwovariables 140225070102-phpapp01 (1)
Applicationofpartialderivativeswithtwovariables 140225070102-phpapp01 (1)
 
Chap6_Sec1.ppt
Chap6_Sec1.pptChap6_Sec1.ppt
Chap6_Sec1.ppt
 
Lesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsLesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite Integrals
 
Continuous random variables and probability distribution
Continuous random variables and probability distributionContinuous random variables and probability distribution
Continuous random variables and probability distribution
 
Applications of partial differentiation
Applications of partial differentiationApplications of partial differentiation
Applications of partial differentiation
 
Quadrature
QuadratureQuadrature
Quadrature
 
Derivatie class 12
Derivatie class 12Derivatie class 12
Derivatie class 12
 
Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)
 
Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)
 
Applications of Differential Calculus in real life
Applications of Differential Calculus in real life Applications of Differential Calculus in real life
Applications of Differential Calculus in real life
 
math vysh.pptx
math vysh.pptxmath vysh.pptx
math vysh.pptx
 
Project in Calcu
Project in CalcuProject in Calcu
Project in Calcu
 
IVS-B UNIT-1_merged. Semester 2 fundamental of sciencepdf
IVS-B UNIT-1_merged. Semester 2 fundamental of sciencepdfIVS-B UNIT-1_merged. Semester 2 fundamental of sciencepdf
IVS-B UNIT-1_merged. Semester 2 fundamental of sciencepdf
 
25 surface area
25 surface area25 surface area
25 surface area
 

More from hartcher

Binomial distributions
Binomial distributionsBinomial distributions
Binomial distributionshartcher
 
Ap and Dual Enrollment Presentation
Ap and Dual Enrollment PresentationAp and Dual Enrollment Presentation
Ap and Dual Enrollment Presentationhartcher
 
AP and Dual Enrollment Presentation
AP and Dual Enrollment PresentationAP and Dual Enrollment Presentation
AP and Dual Enrollment Presentationhartcher
 
Ap and dual enrollment presentation final
Ap and dual enrollment presentation   finalAp and dual enrollment presentation   final
Ap and dual enrollment presentation finalhartcher
 
7.4 A arc length
7.4 A arc length7.4 A arc length
7.4 A arc lengthhartcher
 
Calc 8.7 again
Calc 8.7 againCalc 8.7 again
Calc 8.7 againhartcher
 
Calc 8.7 l'hopital
Calc 8.7 l'hopitalCalc 8.7 l'hopital
Calc 8.7 l'hopitalhartcher
 

More from hartcher (20)

Binomial distributions
Binomial distributionsBinomial distributions
Binomial distributions
 
Ap and Dual Enrollment Presentation
Ap and Dual Enrollment PresentationAp and Dual Enrollment Presentation
Ap and Dual Enrollment Presentation
 
AP and Dual Enrollment Presentation
AP and Dual Enrollment PresentationAP and Dual Enrollment Presentation
AP and Dual Enrollment Presentation
 
Ap and dual enrollment presentation final
Ap and dual enrollment presentation   finalAp and dual enrollment presentation   final
Ap and dual enrollment presentation final
 
7.4 A arc length
7.4 A arc length7.4 A arc length
7.4 A arc length
 
Calc 2.2b
Calc 2.2bCalc 2.2b
Calc 2.2b
 
Calc 8.7 again
Calc 8.7 againCalc 8.7 again
Calc 8.7 again
 
Calc 8.7 l'hopital
Calc 8.7 l'hopitalCalc 8.7 l'hopital
Calc 8.7 l'hopital
 
Calc 2.6
Calc 2.6Calc 2.6
Calc 2.6
 
Calc 6.1b
Calc 6.1bCalc 6.1b
Calc 6.1b
 
Calc 6.1a
Calc 6.1aCalc 6.1a
Calc 6.1a
 
Calc 7.3a
Calc 7.3aCalc 7.3a
Calc 7.3a
 
Calc 7.3b
Calc 7.3bCalc 7.3b
Calc 7.3b
 
Calc 7.2a
Calc 7.2aCalc 7.2a
Calc 7.2a
 
Calc 7.2b
Calc 7.2bCalc 7.2b
Calc 7.2b
 
Calc 7.1b
Calc 7.1bCalc 7.1b
Calc 7.1b
 
Calc 7.1a
Calc 7.1aCalc 7.1a
Calc 7.1a
 
Calc 5.8b
Calc 5.8bCalc 5.8b
Calc 5.8b
 
Calc 5.8a
Calc 5.8aCalc 5.8a
Calc 5.8a
 
Calc 5.7
Calc 5.7Calc 5.7
Calc 5.7
 

Recently uploaded

"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Alan Dix
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang
 
Science&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfScience&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfjimielynbastida
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr LapshynFwdays
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
Bluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfBluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfngoud9212
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024BookNet Canada
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsAndrey Dotsenko
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Neo4j
 
costume and set research powerpoint presentation
costume and set research powerpoint presentationcostume and set research powerpoint presentation
costume and set research powerpoint presentationphoebematthew05
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsHyundai Motor Group
 

Recently uploaded (20)

"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
 
Science&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfScience&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdf
 
Pigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping ElbowsPigging Solutions Piggable Sweeping Elbows
Pigging Solutions Piggable Sweeping Elbows
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
Bluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfBluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdf
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024Build your next Gen AI Breakthrough - April 2024
Build your next Gen AI Breakthrough - April 2024
 
costume and set research powerpoint presentation
costume and set research powerpoint presentationcostume and set research powerpoint presentation
costume and set research powerpoint presentation
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter RoadsSnow Chain-Integrated Tire for a Safe Drive on Winter Roads
Snow Chain-Integrated Tire for a Safe Drive on Winter Roads
 

Calc 3.9a

  • 1. Understand the concept of a tangent line approximation Compare the value of the differential, dy, with the actual change in y, ∆y
  • 2. Consider the equation for the tangent line for a differentiable function at the point (c, f(c)) or This tangent line is called the tangent line approximation of f(x) at c. By choosing values of x sufficiently close to c, the values of y can be used as approximations for values of f. In other words, as x->c, the limit of y is f(c). slope
  • 3. Ex. 1 p. 235 Using a tangent line approximation Find the tangent line approximation for f(x) = 1 + sin x at the point (0, 1). Then use the table to compare the y-values of the linear function with those of f(x) on an open interval containing x = 0. f ‘ (x) = cos x. Slope at (0, 1) = cos 0 = 1 Tangent line is y – f(0) = f ‘(0)(x – 0) y – 1 = 1(x – 0) y = x + 1 x -0.5 -0.1 -0.01 0 0.01 0.1 0.5 f(x) = 1 + sin x 0.521 0.9002 0.9900002 1 1.0099998 1.0998 1.479 y = x + 1 0.5 0.9 0.99 1 1.01 1.1 1.5
  • 4. Differentials When ∆x is small, ∆y = f(c + ∆x) – f(c) is ≈ f ‘(c) ∆x which we’ll call dy
  • 5. For such an approximation, the quantity ∆x = dx, and is called the differential of x. Then ∆y ≈ dy as defined below. In many types of applications, the differential of y can be used as an approximation of the change in y. That is, ∆y ≈ dy, or ∆y ≈ f ‘(x) dx
  • 6. Ex 2. p 236 Comparing ∆y and dy Let y = x 3 Find dy when x = 1 and dx = 0.01. Compare this value with ∆y for x = 1 and ∆x = 0.01 Solution: f ‘(x) = 3x 2 . dy = f ‘(x)dx = f ‘(1)(0.01) = 3(0.01) = 0.03 Now, using ∆x = 0.01, the change in y is ∆ y = f(x + ∆x) – f(x) = f(1 + 0.01) – f(1) = (1.01) 3 – 1 3 = 1.030301 – 1 = 0. 030301 In this example, the tangent line at (1,1) is y = 3x – 2 or g(x) = 3x – 2 . Choosing x-values near 1, f (1.01) = 1.030301 and g(1.01) = 1.03
  • 7. Error Propagation Physicists and engineers make liberal use of approximation dy to replace ∆y. In practice this is in estimation of errors propagated by measuring devices. If x represents the measured value of variable, and x + ∆ x is the exact value, then ∆x is the error in measurement . If the measured value x is used to compute another value f(x), the difference between f(x + ∆x) and f(x) is the propagated error . f(x + ∆x) – f(x) = ∆y
  • 8. Example 3 p.237 Estimation of Error The radius of a ball bearing is measured to be 0.7 inches. If the measurement is correct to within 0.01 inch, estimate the propagated error in the volume of the ball bearing. The formula for volume is where r is the radius of the sphere. So r = 0.7 (measured radius) and Differentiate V to obtain dV/dr = 4 π r 2 ∆ V ≈ dV = 4 π r 2 dr = 4 π (.7) 2 (  0.01) ≈  0.06158 cubic inches Is that a lot of change in volume?
  • 9. The answer is best given in relative terms by comparing dV with V. This is called the relative error . The corresponding percent error is approximately 4.29%
  • 10. 3.9a p. 240/ 1-5 odd, 7-11 all, 13-33 every other odd

Editor's Notes

  1. x – c is called the change in x and is denoted delta x. When delta x is small, the change in y, denoted delta y, can be approximated by