SlideShare ist ein Scribd-Unternehmen logo
1 von 36
Downloaden Sie, um offline zu lesen
Lesson 21 (Sections 15.6–7)
         Partial Derivatives in Economics
     Linear Models with Quadratic Objectives

                         Math 20


                     November 7, 2007


Announcements
   Problem Set 8 assigned today. Due November 14.
   No class November 12. Yes class November 21.
   OH: Mondays 1–2, Tuesdays 3–4, Wednesdays 1–3 (SC 323)
   Prob. Sess.: Sundays 6–7 (SC B-10), Tuesdays 1–2 (SC 116)
Part I

Partial Derivatives in Economics
Outline



   Marginal Quantities


   Marginal products in a Cobb-Douglas function


   Marginal Utilities


   Case Study
Marginal Quantities
   If a variable u depends on some quantity x, the amount that u
   changes by a unit increment in x is called the marginal u of x.
   For instance, the demand q for a quantity is usually assumed to
   depend on several things, including price p, and also perhaps
   income I . If we use a nonlinear function such as

                           q(p, I ) = p −2 + I

   to model demand, then the marginal demand of price is
                             ∂q
                                = −2p −3
                             ∂p
   Similarly, the marginal demand of income is
                                ∂q
                                   =1
                                ∂I
A point to ponder




   The act of fixing all variables and varying only one is the
   mathematical formulation of the ceteris paribus (“all other things
   being equal”) motto.
Outline



   Marginal Quantities


   Marginal products in a Cobb-Douglas function


   Marginal Utilities


   Case Study
Marginal products in a Cobb-Douglas function


   Example (15.20)
   Consider an agricultural production function

                      Y = F (K , L, T ) = AK a Lb T c

   where
       Y is the number of units produced
       K is capital investment
       L is labor input
       T is the area of agricultural land produced
       A, a, b, and c are positive constants
   Find and interpret the first and second partial derivatives of F .
Outline



   Marginal Quantities


   Marginal products in a Cobb-Douglas function


   Marginal Utilities


   Case Study
Let u(x, z) be a measure of the total well-being of a society, where
    x is the total amount of goods produced and consumed
    z is a measure of the level of pollution
What can you estimate about the signs of ux ? uz ? uxz ? What
formula might the function have? What might the shape of the
graph of u be?
Outline



   Marginal Quantities


   Marginal products in a Cobb-Douglas function


   Marginal Utilities


   Case Study
Anti-utility



   Found on The McIntyre Conspiracy:
       I had a suck show last night. Many comics have suck
       shows sometimes. But “suck” is such a vague term. I
       think we need to develop a statistic to help us quantify
       just how much gigs suck relative to each other. This way,
       when comparing bag gigs, I can say,“My show had a suck
       factor of 7.8” and you’ll know just how [bad] it was.
Anti-utility



   Found on The McIntyre Conspiracy:
       I had a suck show last night. Many comics have suck
       shows sometimes. But “suck” is such a vague term. I
       think we need to develop a statistic to help us quantify
       just how much gigs suck relative to each other. This way,
       when comparing bag gigs, I can say,“My show had a suck
       factor of 7.8” and you’ll know just how [bad] it was.

   This is a opposite to utility, but the same analysis can be applied
   mutatis mutandis
Inputs
   These are the things which make a comic unhappy about his set:
         low pay
         gig far away from home
         Bad Lights
         Bad Sound
         Bad Stage
         Bad Chair Arrangement/Audience Seating
         Bad Environment (TVs on, loud waitstaff, etc.)
         No Heckler Control
         Restrictive Limits on Material
         Bachelorette Party In Room
         No Cover Charge
         Random Bizarreness
Variables




   Tim settled on the following variables:
       t: drive time to the venue
       w : amount paid for the show
       S: venue quality (count of bad qualities) from above
   Let σ(t, w , S) be the suckiness function. What can you estimate
   about the partial derivatives of σ? Can you devise a formula for S?
Result
   Tim tried the function
                                            t(S + 1)
                            σ(t, w , S) =
                                               w
Result
   Tim tried the function
                                            t(S + 1)
                            σ(t, w , S) =
                                               w


   Example (Good Gig)
   500 dollars in a town 50 miles from your house. When you get
   there, the place is packed, there’s a 10 dollar cover, and the lights
   and sound are good. However, they leave the Red Sox game on,
   and they tell you you have to follow a speech about the club
   founder, who just died of cancer. Your Steen Coefficient is
   therefore 2 (TVs on, random bizarreness for speech)
Result
   Tim tried the function
                                            t(S + 1)
                            σ(t, w , S) =
                                               w


   Example (Good Gig)
   500 dollars in a town 50 miles from your house. When you get
   there, the place is packed, there’s a 10 dollar cover, and the lights
   and sound are good. However, they leave the Red Sox game on,
   and they tell you you have to follow a speech about the club
   founder, who just died of cancer. Your Steen Coefficient is
   therefore 2 (TVs on, random bizarreness for speech)

                              100
                       σ=         (1 + 2) = 3/5 = 0.6
                              500
Example (Bad Gig)
300 dollars in a town 200 miles from your house. Bad lights, bad
sound, drunken hecklers, and no cover charge. That’s a Steen
Coefficient of 4.
                          400
                     σ=       (1 + 4) = 6.666
                          300
Part II

Linear Models with Quadratic Objectives
Outline




   Algebra primer: Completing the square



   A discriminating monopolist



   Linear Regression
Algebra primer: Completing the square
Outline




   Algebra primer: Completing the square



   A discriminating monopolist



   Linear Regression
Example
A firm sells a product in two separate areas with distinct linear
demand curves, and has monopoly power to decide how much to
sell in each area. How does its maximal profit depend on the
demand in each area?
Outline




   Algebra primer: Completing the square



   A discriminating monopolist



   Linear Regression
Example
Suppose we’re given a data set (xt , yt ), where t = 1, 2, . . . , T are
discrete observations. What line best fits these data?

Weitere ähnliche Inhalte

Was ist angesagt?

Partial differentiation
Partial differentiationPartial differentiation
Partial differentiationTanuj Parikh
 
Lesson 7: Vector-valued functions
Lesson 7: Vector-valued functionsLesson 7: Vector-valued functions
Lesson 7: Vector-valued functionsMatthew Leingang
 
Limits And Derivative
Limits And DerivativeLimits And Derivative
Limits And DerivativeAshams kurian
 
Heteroskedasticity
HeteroskedasticityHeteroskedasticity
Heteroskedasticityhalimuth
 
functions-of-several-variables.ppt
functions-of-several-variables.pptfunctions-of-several-variables.ppt
functions-of-several-variables.pptDavidbham
 
Application of derivatives
Application of derivatives Application of derivatives
Application of derivatives Seyid Kadher
 
Continuity and differentiability
Continuity and differentiability Continuity and differentiability
Continuity and differentiability Seyid Kadher
 
Limit of complex number
Limit of complex numberLimit of complex number
Limit of complex numberBilal Amjad
 
Time series analysis- Part 2
Time series analysis- Part 2Time series analysis- Part 2
Time series analysis- Part 2QuantUniversity
 
Autocorrelation- Concept, Causes and Consequences
Autocorrelation- Concept, Causes and ConsequencesAutocorrelation- Concept, Causes and Consequences
Autocorrelation- Concept, Causes and ConsequencesShilpa Chaudhary
 
4.5 continuous functions and differentiable functions
4.5 continuous functions and differentiable functions4.5 continuous functions and differentiable functions
4.5 continuous functions and differentiable functionsmath265
 
Lesson 19: Partial Derivatives
Lesson 19: Partial DerivativesLesson 19: Partial Derivatives
Lesson 19: Partial DerivativesMatthew Leingang
 

Was ist angesagt? (20)

Economic Optimization Numerical
Economic Optimization NumericalEconomic Optimization Numerical
Economic Optimization Numerical
 
Partial differentiation
Partial differentiationPartial differentiation
Partial differentiation
 
Chapter 17 - Multivariable Calculus
Chapter 17 - Multivariable CalculusChapter 17 - Multivariable Calculus
Chapter 17 - Multivariable Calculus
 
Lesson 7: Vector-valued functions
Lesson 7: Vector-valued functionsLesson 7: Vector-valued functions
Lesson 7: Vector-valued functions
 
Accelerator Theory
Accelerator TheoryAccelerator Theory
Accelerator Theory
 
Metric space
Metric spaceMetric space
Metric space
 
Basic calculus (ii) recap
Basic calculus (ii) recapBasic calculus (ii) recap
Basic calculus (ii) recap
 
Introduction to Econometrics
Introduction to EconometricsIntroduction to Econometrics
Introduction to Econometrics
 
Limits And Derivative
Limits And DerivativeLimits And Derivative
Limits And Derivative
 
Heteroskedasticity
HeteroskedasticityHeteroskedasticity
Heteroskedasticity
 
functions-of-several-variables.ppt
functions-of-several-variables.pptfunctions-of-several-variables.ppt
functions-of-several-variables.ppt
 
Application of derivatives
Application of derivatives Application of derivatives
Application of derivatives
 
Euler’s Theorem Homogeneous Function Of Two Variables
Euler’s Theorem Homogeneous Function Of  Two VariablesEuler’s Theorem Homogeneous Function Of  Two Variables
Euler’s Theorem Homogeneous Function Of Two Variables
 
Roll's theorem
Roll's theoremRoll's theorem
Roll's theorem
 
Continuity and differentiability
Continuity and differentiability Continuity and differentiability
Continuity and differentiability
 
Limit of complex number
Limit of complex numberLimit of complex number
Limit of complex number
 
Time series analysis- Part 2
Time series analysis- Part 2Time series analysis- Part 2
Time series analysis- Part 2
 
Autocorrelation- Concept, Causes and Consequences
Autocorrelation- Concept, Causes and ConsequencesAutocorrelation- Concept, Causes and Consequences
Autocorrelation- Concept, Causes and Consequences
 
4.5 continuous functions and differentiable functions
4.5 continuous functions and differentiable functions4.5 continuous functions and differentiable functions
4.5 continuous functions and differentiable functions
 
Lesson 19: Partial Derivatives
Lesson 19: Partial DerivativesLesson 19: Partial Derivatives
Lesson 19: Partial Derivatives
 

Andere mochten auch

APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONDhrupal Patel
 
application of partial differentiation
application of partial differentiationapplication of partial differentiation
application of partial differentiationeteaching
 
APPLICATIONS OF DIFFERENTIAL EQUATIONS-ZBJ
APPLICATIONS OF DIFFERENTIAL EQUATIONS-ZBJAPPLICATIONS OF DIFFERENTIAL EQUATIONS-ZBJ
APPLICATIONS OF DIFFERENTIAL EQUATIONS-ZBJZuhair Bin Jawaid
 
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
 
Applications of Derivatives
Applications of DerivativesApplications of Derivatives
Applications of DerivativesIram Khan
 
Some Engg. Applications of Matrices and Partial Derivatives
Some Engg. Applications of Matrices and Partial DerivativesSome Engg. Applications of Matrices and Partial Derivatives
Some Engg. Applications of Matrices and Partial DerivativesSanjaySingh011996
 
Applications of differential equations by shahzad
Applications of differential equations by shahzadApplications of differential equations by shahzad
Applications of differential equations by shahzadbiotech energy pvt limited
 
partial diffrentialequations
partial diffrentialequationspartial diffrentialequations
partial diffrentialequations8laddu8
 
The Application of Derivatives
The Application of DerivativesThe Application of Derivatives
The Application of Derivativesdivaprincess09
 
Chapter 5(partial differentiation)
Chapter 5(partial differentiation)Chapter 5(partial differentiation)
Chapter 5(partial differentiation)Eko Wijayanto
 
Production Function & cost elasticity Maruti Suzuki
Production Function & cost elasticity Maruti Suzuki Production Function & cost elasticity Maruti Suzuki
Production Function & cost elasticity Maruti Suzuki Akhilendra Tiwari
 
Lesson 26: Optimization II: Data Fitting
Lesson 26: Optimization II: Data FittingLesson 26: Optimization II: Data Fitting
Lesson 26: Optimization II: Data FittingMatthew Leingang
 
Lesson 25: Unconstrained Optimization I
Lesson 25: Unconstrained Optimization ILesson 25: Unconstrained Optimization I
Lesson 25: Unconstrained Optimization IMatthew Leingang
 
Lesson24 Implicit Differentiation Slides
Lesson24    Implicit  Differentiation SlidesLesson24    Implicit  Differentiation Slides
Lesson24 Implicit Differentiation SlidesMatthew Leingang
 
Lesson20 Tangent Planes Slides+Notes
Lesson20   Tangent Planes Slides+NotesLesson20   Tangent Planes Slides+Notes
Lesson20 Tangent Planes Slides+NotesMatthew Leingang
 
Lesson 28: Lagrange Multipliers II
Lesson 28: Lagrange  Multipliers IILesson 28: Lagrange  Multipliers II
Lesson 28: Lagrange Multipliers IIMatthew Leingang
 
Lesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsLesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsMatthew Leingang
 
Lesson 25: Indeterminate Forms and L'Hôpital's Rule
Lesson 25: Indeterminate Forms and L'Hôpital's RuleLesson 25: Indeterminate Forms and L'Hôpital's Rule
Lesson 25: Indeterminate Forms and L'Hôpital's RuleMatthew Leingang
 

Andere mochten auch (20)

APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATION
 
application of partial differentiation
application of partial differentiationapplication of partial differentiation
application of partial differentiation
 
APPLICATIONS OF DIFFERENTIAL EQUATIONS-ZBJ
APPLICATIONS OF DIFFERENTIAL EQUATIONS-ZBJAPPLICATIONS OF DIFFERENTIAL EQUATIONS-ZBJ
APPLICATIONS OF DIFFERENTIAL EQUATIONS-ZBJ
 
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
 
Applications of Derivatives
Applications of DerivativesApplications of Derivatives
Applications of Derivatives
 
Some Engg. Applications of Matrices and Partial Derivatives
Some Engg. Applications of Matrices and Partial DerivativesSome Engg. Applications of Matrices and Partial Derivatives
Some Engg. Applications of Matrices and Partial Derivatives
 
Applications of differential equations by shahzad
Applications of differential equations by shahzadApplications of differential equations by shahzad
Applications of differential equations by shahzad
 
partial diffrentialequations
partial diffrentialequationspartial diffrentialequations
partial diffrentialequations
 
The Application of Derivatives
The Application of DerivativesThe Application of Derivatives
The Application of Derivatives
 
Chapter 5(partial differentiation)
Chapter 5(partial differentiation)Chapter 5(partial differentiation)
Chapter 5(partial differentiation)
 
Production Function & cost elasticity Maruti Suzuki
Production Function & cost elasticity Maruti Suzuki Production Function & cost elasticity Maruti Suzuki
Production Function & cost elasticity Maruti Suzuki
 
Lesson 26: Optimization II: Data Fitting
Lesson 26: Optimization II: Data FittingLesson 26: Optimization II: Data Fitting
Lesson 26: Optimization II: Data Fitting
 
Lesson 25: Unconstrained Optimization I
Lesson 25: Unconstrained Optimization ILesson 25: Unconstrained Optimization I
Lesson 25: Unconstrained Optimization I
 
Lesson 29: Areas
Lesson 29: AreasLesson 29: Areas
Lesson 29: Areas
 
Lesson24 Implicit Differentiation Slides
Lesson24    Implicit  Differentiation SlidesLesson24    Implicit  Differentiation Slides
Lesson24 Implicit Differentiation Slides
 
Midterm II Review
Midterm II ReviewMidterm II Review
Midterm II Review
 
Lesson20 Tangent Planes Slides+Notes
Lesson20   Tangent Planes Slides+NotesLesson20   Tangent Planes Slides+Notes
Lesson20 Tangent Planes Slides+Notes
 
Lesson 28: Lagrange Multipliers II
Lesson 28: Lagrange  Multipliers IILesson 28: Lagrange  Multipliers II
Lesson 28: Lagrange Multipliers II
 
Lesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsLesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite Integrals
 
Lesson 25: Indeterminate Forms and L'Hôpital's Rule
Lesson 25: Indeterminate Forms and L'Hôpital's RuleLesson 25: Indeterminate Forms and L'Hôpital's Rule
Lesson 25: Indeterminate Forms and L'Hôpital's Rule
 

Ähnlich wie Lesson 21: Partial Derivatives in Economics

Year 12 Maths A Textbook - Chapter 9
Year 12 Maths A Textbook - Chapter 9Year 12 Maths A Textbook - Chapter 9
Year 12 Maths A Textbook - Chapter 9westy67968
 
STAB52 Lecture Notes (Week 2)
STAB52 Lecture Notes (Week 2)STAB52 Lecture Notes (Week 2)
STAB52 Lecture Notes (Week 2)Danny Cao
 
KARNAUGH MAP(K-MAP)
KARNAUGH MAP(K-MAP)KARNAUGH MAP(K-MAP)
KARNAUGH MAP(K-MAP)mihir jain
 
Basic Concept Of Probability
Basic Concept Of ProbabilityBasic Concept Of Probability
Basic Concept Of Probabilityguest45a926
 
1. (10 pts) For each graph, is the graph symmetric with respect to t.docx
1. (10 pts) For each graph, is the graph symmetric with respect to t.docx1. (10 pts) For each graph, is the graph symmetric with respect to t.docx
1. (10 pts) For each graph, is the graph symmetric with respect to t.docxlindorffgarrik
 
Multiple Choice Type your answer choice in the blank next to each.docx
Multiple Choice Type your answer choice in the blank next to each.docxMultiple Choice Type your answer choice in the blank next to each.docx
Multiple Choice Type your answer choice in the blank next to each.docxadelaidefarmer322
 
Pre-Calculus Midterm Exam 1 Score ______ ____.docx
Pre-Calculus Midterm Exam  1  Score ______  ____.docxPre-Calculus Midterm Exam  1  Score ______  ____.docx
Pre-Calculus Midterm Exam 1 Score ______ ____.docxChantellPantoja184
 
Name ____________________________Student Number ________________.docx
Name ____________________________Student Number ________________.docxName ____________________________Student Number ________________.docx
Name ____________________________Student Number ________________.docxTanaMaeskm
 
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)Nicholas Arcolano
 
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)TrueMotion
 
State Space c-Reductions of Concurrent Systems in Rewriting Logic @ ETAPS Wor...
State Space c-Reductions of Concurrent Systems in Rewriting Logic @ ETAPS Wor...State Space c-Reductions of Concurrent Systems in Rewriting Logic @ ETAPS Wor...
State Space c-Reductions of Concurrent Systems in Rewriting Logic @ ETAPS Wor...Alberto Lluch Lafuente
 
Pre-Calculus Midterm ExamScore ______ ______Name _______.docx
Pre-Calculus Midterm ExamScore ______  ______Name _______.docxPre-Calculus Midterm ExamScore ______  ______Name _______.docx
Pre-Calculus Midterm ExamScore ______ ______Name _______.docxharrisonhoward80223
 
Lesson 5: Tangents, Velocity, the Derivative
Lesson 5: Tangents, Velocity, the DerivativeLesson 5: Tangents, Velocity, the Derivative
Lesson 5: Tangents, Velocity, the DerivativeMatthew Leingang
 
Generalized CDT as a scaling limit of planar maps
Generalized CDT as a scaling limit of planar mapsGeneralized CDT as a scaling limit of planar maps
Generalized CDT as a scaling limit of planar mapsTimothy Budd
 
Chapter5 data handling grade 8 cbse
Chapter5  data handling grade 8 cbseChapter5  data handling grade 8 cbse
Chapter5 data handling grade 8 cbsehtanny
 
Name _________________________ Score ______ ______1..docx
Name _________________________  Score ______  ______1..docxName _________________________  Score ______  ______1..docx
Name _________________________ Score ______ ______1..docxlea6nklmattu
 
Hidden Markov Model in Natural Language Processing
Hidden Markov Model in Natural Language ProcessingHidden Markov Model in Natural Language Processing
Hidden Markov Model in Natural Language Processingsachinmaskeen211
 

Ähnlich wie Lesson 21: Partial Derivatives in Economics (20)

Mit6 006 f11_quiz1
Mit6 006 f11_quiz1Mit6 006 f11_quiz1
Mit6 006 f11_quiz1
 
Year 12 Maths A Textbook - Chapter 9
Year 12 Maths A Textbook - Chapter 9Year 12 Maths A Textbook - Chapter 9
Year 12 Maths A Textbook - Chapter 9
 
STAB52 Lecture Notes (Week 2)
STAB52 Lecture Notes (Week 2)STAB52 Lecture Notes (Week 2)
STAB52 Lecture Notes (Week 2)
 
KARNAUGH MAP(K-MAP)
KARNAUGH MAP(K-MAP)KARNAUGH MAP(K-MAP)
KARNAUGH MAP(K-MAP)
 
Basic Concept Of Probability
Basic Concept Of ProbabilityBasic Concept Of Probability
Basic Concept Of Probability
 
1. (10 pts) For each graph, is the graph symmetric with respect to t.docx
1. (10 pts) For each graph, is the graph symmetric with respect to t.docx1. (10 pts) For each graph, is the graph symmetric with respect to t.docx
1. (10 pts) For each graph, is the graph symmetric with respect to t.docx
 
Multiple Choice Type your answer choice in the blank next to each.docx
Multiple Choice Type your answer choice in the blank next to each.docxMultiple Choice Type your answer choice in the blank next to each.docx
Multiple Choice Type your answer choice in the blank next to each.docx
 
C2.0 propositional logic
C2.0 propositional logicC2.0 propositional logic
C2.0 propositional logic
 
Pre-Calculus Midterm Exam 1 Score ______ ____.docx
Pre-Calculus Midterm Exam  1  Score ______  ____.docxPre-Calculus Midterm Exam  1  Score ______  ____.docx
Pre-Calculus Midterm Exam 1 Score ______ ____.docx
 
Name ____________________________Student Number ________________.docx
Name ____________________________Student Number ________________.docxName ____________________________Student Number ________________.docx
Name ____________________________Student Number ________________.docx
 
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
 
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
A/B Testing Theory and Practice (TrueMotion Data Science Lunch Seminar)
 
State Space c-Reductions of Concurrent Systems in Rewriting Logic @ ETAPS Wor...
State Space c-Reductions of Concurrent Systems in Rewriting Logic @ ETAPS Wor...State Space c-Reductions of Concurrent Systems in Rewriting Logic @ ETAPS Wor...
State Space c-Reductions of Concurrent Systems in Rewriting Logic @ ETAPS Wor...
 
Pre-Calculus Midterm ExamScore ______ ______Name _______.docx
Pre-Calculus Midterm ExamScore ______  ______Name _______.docxPre-Calculus Midterm ExamScore ______  ______Name _______.docx
Pre-Calculus Midterm ExamScore ______ ______Name _______.docx
 
Lesson 5: Tangents, Velocity, the Derivative
Lesson 5: Tangents, Velocity, the DerivativeLesson 5: Tangents, Velocity, the Derivative
Lesson 5: Tangents, Velocity, the Derivative
 
Generalized CDT as a scaling limit of planar maps
Generalized CDT as a scaling limit of planar mapsGeneralized CDT as a scaling limit of planar maps
Generalized CDT as a scaling limit of planar maps
 
Chapter5 data handling grade 8 cbse
Chapter5  data handling grade 8 cbseChapter5  data handling grade 8 cbse
Chapter5 data handling grade 8 cbse
 
Name _________________________ Score ______ ______1..docx
Name _________________________  Score ______  ______1..docxName _________________________  Score ______  ______1..docx
Name _________________________ Score ______ ______1..docx
 
Statistics Coursework Help
Statistics Coursework HelpStatistics Coursework Help
Statistics Coursework Help
 
Hidden Markov Model in Natural Language Processing
Hidden Markov Model in Natural Language ProcessingHidden Markov Model in Natural Language Processing
Hidden Markov Model in Natural Language Processing
 

Mehr von Matthew Leingang

Streamlining assessment, feedback, and archival with auto-multiple-choice
Streamlining assessment, feedback, and archival with auto-multiple-choiceStreamlining assessment, feedback, and archival with auto-multiple-choice
Streamlining assessment, feedback, and archival with auto-multiple-choiceMatthew Leingang
 
Electronic Grading of Paper Assessments
Electronic Grading of Paper AssessmentsElectronic Grading of Paper Assessments
Electronic Grading of Paper AssessmentsMatthew Leingang
 
Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)Matthew Leingang
 
Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Matthew Leingang
 
Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Matthew Leingang
 
Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)Matthew Leingang
 
Lesson 26: The Fundamental Theorem of Calculus (handout)
Lesson 26: The Fundamental Theorem of Calculus (handout)Lesson 26: The Fundamental Theorem of Calculus (handout)
Lesson 26: The Fundamental Theorem of Calculus (handout)Matthew Leingang
 
Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)Matthew Leingang
 
Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)Matthew Leingang
 
Lesson 24: Areas and Distances, The Definite Integral (handout)
Lesson 24: Areas and Distances, The Definite Integral (handout)Lesson 24: Areas and Distances, The Definite Integral (handout)
Lesson 24: Areas and Distances, The Definite Integral (handout)Matthew Leingang
 
Lesson 24: Areas and Distances, The Definite Integral (slides)
Lesson 24: Areas and Distances, The Definite Integral (slides)Lesson 24: Areas and Distances, The Definite Integral (slides)
Lesson 24: Areas and Distances, The Definite Integral (slides)Matthew Leingang
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Matthew Leingang
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Matthew Leingang
 
Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)Matthew Leingang
 
Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)Matthew Leingang
 
Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Matthew Leingang
 
Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)Matthew Leingang
 
Lesson 20: Derivatives and the Shapes of Curves (slides)
Lesson 20: Derivatives and the Shapes of Curves (slides)Lesson 20: Derivatives and the Shapes of Curves (slides)
Lesson 20: Derivatives and the Shapes of Curves (slides)Matthew Leingang
 
Lesson 20: Derivatives and the Shapes of Curves (handout)
Lesson 20: Derivatives and the Shapes of Curves (handout)Lesson 20: Derivatives and the Shapes of Curves (handout)
Lesson 20: Derivatives and the Shapes of Curves (handout)Matthew Leingang
 

Mehr von Matthew Leingang (20)

Making Lesson Plans
Making Lesson PlansMaking Lesson Plans
Making Lesson Plans
 
Streamlining assessment, feedback, and archival with auto-multiple-choice
Streamlining assessment, feedback, and archival with auto-multiple-choiceStreamlining assessment, feedback, and archival with auto-multiple-choice
Streamlining assessment, feedback, and archival with auto-multiple-choice
 
Electronic Grading of Paper Assessments
Electronic Grading of Paper AssessmentsElectronic Grading of Paper Assessments
Electronic Grading of Paper Assessments
 
Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)Lesson 27: Integration by Substitution (slides)
Lesson 27: Integration by Substitution (slides)
 
Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)
 
Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)Lesson 26: The Fundamental Theorem of Calculus (slides)
Lesson 26: The Fundamental Theorem of Calculus (slides)
 
Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)Lesson 27: Integration by Substitution (handout)
Lesson 27: Integration by Substitution (handout)
 
Lesson 26: The Fundamental Theorem of Calculus (handout)
Lesson 26: The Fundamental Theorem of Calculus (handout)Lesson 26: The Fundamental Theorem of Calculus (handout)
Lesson 26: The Fundamental Theorem of Calculus (handout)
 
Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)Lesson 25: Evaluating Definite Integrals (slides)
Lesson 25: Evaluating Definite Integrals (slides)
 
Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)Lesson 25: Evaluating Definite Integrals (handout)
Lesson 25: Evaluating Definite Integrals (handout)
 
Lesson 24: Areas and Distances, The Definite Integral (handout)
Lesson 24: Areas and Distances, The Definite Integral (handout)Lesson 24: Areas and Distances, The Definite Integral (handout)
Lesson 24: Areas and Distances, The Definite Integral (handout)
 
Lesson 24: Areas and Distances, The Definite Integral (slides)
Lesson 24: Areas and Distances, The Definite Integral (slides)Lesson 24: Areas and Distances, The Definite Integral (slides)
Lesson 24: Areas and Distances, The Definite Integral (slides)
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)
 
Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)Lesson 23: Antiderivatives (slides)
Lesson 23: Antiderivatives (slides)
 
Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)Lesson 22: Optimization Problems (slides)
Lesson 22: Optimization Problems (slides)
 
Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)Lesson 22: Optimization Problems (handout)
Lesson 22: Optimization Problems (handout)
 
Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)Lesson 21: Curve Sketching (slides)
Lesson 21: Curve Sketching (slides)
 
Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)Lesson 21: Curve Sketching (handout)
Lesson 21: Curve Sketching (handout)
 
Lesson 20: Derivatives and the Shapes of Curves (slides)
Lesson 20: Derivatives and the Shapes of Curves (slides)Lesson 20: Derivatives and the Shapes of Curves (slides)
Lesson 20: Derivatives and the Shapes of Curves (slides)
 
Lesson 20: Derivatives and the Shapes of Curves (handout)
Lesson 20: Derivatives and the Shapes of Curves (handout)Lesson 20: Derivatives and the Shapes of Curves (handout)
Lesson 20: Derivatives and the Shapes of Curves (handout)
 

Kürzlich hochgeladen

presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024The Digital Insurer
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdflior mazor
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 

Kürzlich hochgeladen (20)

presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdfGenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 

Lesson 21: Partial Derivatives in Economics

  • 1. Lesson 21 (Sections 15.6–7) Partial Derivatives in Economics Linear Models with Quadratic Objectives Math 20 November 7, 2007 Announcements Problem Set 8 assigned today. Due November 14. No class November 12. Yes class November 21. OH: Mondays 1–2, Tuesdays 3–4, Wednesdays 1–3 (SC 323) Prob. Sess.: Sundays 6–7 (SC B-10), Tuesdays 1–2 (SC 116)
  • 3. Outline Marginal Quantities Marginal products in a Cobb-Douglas function Marginal Utilities Case Study
  • 4. Marginal Quantities If a variable u depends on some quantity x, the amount that u changes by a unit increment in x is called the marginal u of x. For instance, the demand q for a quantity is usually assumed to depend on several things, including price p, and also perhaps income I . If we use a nonlinear function such as q(p, I ) = p −2 + I to model demand, then the marginal demand of price is ∂q = −2p −3 ∂p Similarly, the marginal demand of income is ∂q =1 ∂I
  • 5. A point to ponder The act of fixing all variables and varying only one is the mathematical formulation of the ceteris paribus (“all other things being equal”) motto.
  • 6. Outline Marginal Quantities Marginal products in a Cobb-Douglas function Marginal Utilities Case Study
  • 7. Marginal products in a Cobb-Douglas function Example (15.20) Consider an agricultural production function Y = F (K , L, T ) = AK a Lb T c where Y is the number of units produced K is capital investment L is labor input T is the area of agricultural land produced A, a, b, and c are positive constants Find and interpret the first and second partial derivatives of F .
  • 8.
  • 9.
  • 10.
  • 11. Outline Marginal Quantities Marginal products in a Cobb-Douglas function Marginal Utilities Case Study
  • 12. Let u(x, z) be a measure of the total well-being of a society, where x is the total amount of goods produced and consumed z is a measure of the level of pollution What can you estimate about the signs of ux ? uz ? uxz ? What formula might the function have? What might the shape of the graph of u be?
  • 13.
  • 14.
  • 15. Outline Marginal Quantities Marginal products in a Cobb-Douglas function Marginal Utilities Case Study
  • 16. Anti-utility Found on The McIntyre Conspiracy: I had a suck show last night. Many comics have suck shows sometimes. But “suck” is such a vague term. I think we need to develop a statistic to help us quantify just how much gigs suck relative to each other. This way, when comparing bag gigs, I can say,“My show had a suck factor of 7.8” and you’ll know just how [bad] it was.
  • 17. Anti-utility Found on The McIntyre Conspiracy: I had a suck show last night. Many comics have suck shows sometimes. But “suck” is such a vague term. I think we need to develop a statistic to help us quantify just how much gigs suck relative to each other. This way, when comparing bag gigs, I can say,“My show had a suck factor of 7.8” and you’ll know just how [bad] it was. This is a opposite to utility, but the same analysis can be applied mutatis mutandis
  • 18. Inputs These are the things which make a comic unhappy about his set: low pay gig far away from home Bad Lights Bad Sound Bad Stage Bad Chair Arrangement/Audience Seating Bad Environment (TVs on, loud waitstaff, etc.) No Heckler Control Restrictive Limits on Material Bachelorette Party In Room No Cover Charge Random Bizarreness
  • 19. Variables Tim settled on the following variables: t: drive time to the venue w : amount paid for the show S: venue quality (count of bad qualities) from above Let σ(t, w , S) be the suckiness function. What can you estimate about the partial derivatives of σ? Can you devise a formula for S?
  • 20. Result Tim tried the function t(S + 1) σ(t, w , S) = w
  • 21.
  • 22. Result Tim tried the function t(S + 1) σ(t, w , S) = w Example (Good Gig) 500 dollars in a town 50 miles from your house. When you get there, the place is packed, there’s a 10 dollar cover, and the lights and sound are good. However, they leave the Red Sox game on, and they tell you you have to follow a speech about the club founder, who just died of cancer. Your Steen Coefficient is therefore 2 (TVs on, random bizarreness for speech)
  • 23. Result Tim tried the function t(S + 1) σ(t, w , S) = w Example (Good Gig) 500 dollars in a town 50 miles from your house. When you get there, the place is packed, there’s a 10 dollar cover, and the lights and sound are good. However, they leave the Red Sox game on, and they tell you you have to follow a speech about the club founder, who just died of cancer. Your Steen Coefficient is therefore 2 (TVs on, random bizarreness for speech) 100 σ= (1 + 2) = 3/5 = 0.6 500
  • 24. Example (Bad Gig) 300 dollars in a town 200 miles from your house. Bad lights, bad sound, drunken hecklers, and no cover charge. That’s a Steen Coefficient of 4. 400 σ= (1 + 4) = 6.666 300
  • 25. Part II Linear Models with Quadratic Objectives
  • 26. Outline Algebra primer: Completing the square A discriminating monopolist Linear Regression
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 33. Outline Algebra primer: Completing the square A discriminating monopolist Linear Regression
  • 34. Example A firm sells a product in two separate areas with distinct linear demand curves, and has monopoly power to decide how much to sell in each area. How does its maximal profit depend on the demand in each area?
  • 35. Outline Algebra primer: Completing the square A discriminating monopolist Linear Regression
  • 36. Example Suppose we’re given a data set (xt , yt ), where t = 1, 2, . . . , T are discrete observations. What line best fits these data?