 Chapter 1: Introduction to Differential Equations
 Chapter 1.1: Definitions and Terminology
 Chapter 1.2: InitialValue Problems
 Chapter 1.3: Differential Equations as Mathematical Models
 Chapter 10: Systems of Linear Differential Equations
 Chapter 10.1: Theory of Linear Systems
 Chapter 10.2: Homogeneous Linear Systems
 Chapter 10.3: Solution by Diagonalization
 Chapter 10.4: Nonhomogeneous Linear Systems
 Chapter 10.5: Matrix Exponential
 Chapter 11.1: Autonomous Systems
 Chapter 11.2: Stability of Linear Systems
 Chapter 11.3: Linearization and Local Stability
 Chapter 11.4: Autonomous Systems as Mathematical Models
 Chapter 11.5: Periodic Solutions, Limit Cycles, and Global Stability
 Chapter 12.1: Orthogonal Functions
 Chapter 12.2: Fourier Series
 Chapter 12.3: Fourier Cosine and Sine Series
 Chapter 12.4: Complex Fourier Series
 Chapter 12.5: SturmLiouville Problem
 Chapter 12.6: Bessel and Legendre Series
 Chapter 13: BoundaryValue Problems in Rectangular Coordinates
 Chapter 13.1: Separable Partial Differential Equations
 Chapter 13.2: Classical PDEs and BoundaryValue Problems
 Chapter 13.3: Heat Equation
 Chapter 13.4: Wave Equation
 Chapter 13.5: Laplace's Equation
 Chapter 13.6: Nonhomogeneous BVPs
 Chapter 13.7: Orthogonal Series Expansions
 Chapter 13.8: Fourier Series in Two Variables
 Chapter 14: BoundaryValue Problems in Other Coordinate Systems
 Chapter 14.1: Problems in Polar Coordinates
 Chapter 14.2: Problems in Cylindrical Coordinates
 Chapter 14.3: Problems in Spherical Coordinates
 Chapter 15: Integral Transform Method
 Chapter 15.1: Error Function
 Chapter 15.2: Applications of the Laplace Transform
 Chapter 15.3: Fourier Integral
 Chapter 15.4: Fourier Transforms
 Chapter 15.5: Fast Fourier Transform
 Chapter 16: Numerical Solutions of Partial Differential Equations
 Chapter 16.1: Laplace's Equation
 Chapter 16.2: The Heat Equation
 Chapter 16.3: The Wave Equation
 Chapter 17.1: Complex Numbers
 Chapter 17.2: Powers and Roots
 Chapter 17.3: Sets in the Complex Plane
 Chapter 17.4: Functions of a Complex Variable
 Chapter 17.5: CauchyRiemann Equations
 Chapter 17.6: Exponential and Logarithmic Functions
 Chapter 17.7: Trigonometric and Hyperbolic Functions
 Chapter 17.8: Inverse Trigonometric and Hyperbolic Functions
 Chapter 18.1: Contour Integrals
 Chapter 18.2: CauchyGoursat Theorem
 Chapter 18.3: Independence of the Path
 Chapter 18.4: Cauchy's Integral Formulas
 Chapter 19: Series and Residues
 Chapter 19.1: Sequences and Series
 Chapter 19.2: Taylor Series
 Chapter 19.3: Laurent Series
 Chapter 19.4: Zeros and Poles
 Chapter 19.5: Residues and Residue Theorem
 Chapter 19.6: Evaluation of Real Integrals
 Chapter 2: FirstOrder Differential Equations
 Chapter 2.1: Solution Curves Without a Solution
 Chapter 2.2: Separable Equations
 Chapter 2.3: Linear Equations
 Chapter 2.4: Exact Equations
 Chapter 2.5: Solutions by Substitutions
 Chapter 2.6: A Numerical Method
 Chapter 2.7: Linear Models
 Chapter 2.8: Nonlinear Models
 Chapter 2.9: Modeling with Systems of FirstOrder DEs
 Chapter 20: Conformal Mappings
 Chapter 20.1: Complex Functions as Mappings
 Chapter 20.2: Conformal Mappings
 Chapter 20.3: Linear Fractional Transformations
 Chapter 20.4: SchwarzChristoffel Transformations
 Chapter 20.5: Poisson Integral Formulas
 Chapter 20.6: Applications
 Chapter 3: HigherOrder Differential Equations
 Chapter 3.1: Theory of Linear Equations
 Chapter 3.11: Nonlinear Models
 Chapter 3.12: Solving Systems of Linear Equations
 Chapter 3.2: Reduction of Order
 Chapter 3.3: Homogeneous Linear Equations with Constant Coefficients
 Chapter 3.4: Undetermined Coefficients
 Chapter 3.5: Variation of Parameters
 Chapter 3.6: CauchyEuler Equations
 Chapter 3.7: Nonlinear Equations
 Chapter 3.8: Linear Models: InitialValue Problems
 Chapter 3.9: Linear Models: BoundaryValue Problems
 Chapter 4: The Laplace Transform
 Chapter 4.1: Definition of the Laplace Transform
 Chapter 4.2: The Inverse Transform and Transforms of Derivatives
 Chapter 4.3: Translation Theorems
 Chapter 4.4: Additional Operational Properties
 Chapter 4.5: The Dirac Delta Function
 Chapter 4.6: Systems of Linear Differential Equations
 Chapter 5: Series Solutions of Linear Differential Equations
 Chapter 5.1: Solutions about Ordinary Points
 Chapter 5.2: Solutions about Singular Points
 Chapter 5.3: Special Functions
 Chapter 6: Numerical Solutions of Ordinary Differential Equations
 Chapter 6.1: Euler Methods and Error Analysis
 Chapter 6.2: RungeKutta Methods
 Chapter 6.3: Multistep Methods
 Chapter 6.4: HigherOrder Equations and Systems
 Chapter 6.5: SecondOrder BoundaryValue Problems
 Chapter 7: Vectors
 Chapter 7.1: Vectors in 2Space
 Chapter 7.2: Vectors in 3Space
 Chapter 7.3: Dot Product
 Chapter 7.4: Cross Produc
 Chapter 7.5: Lines and Planes in 3Space
 Chapter 7.6: Vector Spaces
 Chapter 7.7: GramSchmidt Orthogonalization Process
 Chapter 8.1: Matrix Algebra
 Chapter 8.11: Approximation of Eigenvalues
 Chapter 8.12: Diagonalization
 Chapter 8.13: LUFactorization
 Chapter 8.2: Systems of Linear Algebraic Equations
 Chapter 8.3: Rank of a Matrix
 Chapter 8.4: Determinants
 Chapter 8.5: Properties of Determinants
 Chapter 8.6: Inverse of a Matrix
 Chapter 8.7: Cramer's Rule
 Chapter 8.8: The Eigenvalue Problem
 Chapter 8.9: Powers of Matrices
 Chapter 9: Vector Calculus
 Chapter 9.1: Vector Functions
 Chapter 9.10: Double Integrals
 Chapter 9.11: Double Integrals in Polar Coordinates
 Chapter 9.12: Green's Theorem
 Chapter 9.13: Surface Integrals
 Chapter 9.14: Stokes'Theorem
 Chapter 9.15: Triple Integrals
 Chapter 9.16: Divergence Theorem
 Chapter 9.17: Change of Variables in Multiple Integrals
 Chapter 9.2: Motion on a Curve
 Chapter 9.3: Curvature and Components of Acceleration
 Chapter 9.4: Partial Derivatives
 Chapter 9.5: Directional Derivative
 Chapter 9.6: Tangent Planes and Normal Lines
 Chapter 9.7: Curl and Divergence
 Chapter 9.8: Line Integrals
 Chapter 9.9: Independence of the Path
 Chapter Chapter 11: Systems of Nonlinear Differential Equations
 Chapter Chapter 12: Orthogonal Functions and Fourier Series
 Chapter Chapter 17: Functions of a Complex Variable
 Chapter Chapter 18: Integration in the Complex Plane
Advanced Engineering Mathematics 5th Edition  Solutions by Chapter
Full solutions for Advanced Engineering Mathematics  5th Edition
ISBN: 9781449691721
Advanced Engineering Mathematics  5th Edition  Solutions by Chapter
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Bayes’ theorem
An equation for a conditional probability such as PA B (  ) in terms of the reverse conditional probability PB A (  ).

Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.

Bivariate normal distribution
The joint distribution of two normal random variables

C chart
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defectsperunit or U chart.

Center line
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.

Central composite design (CCD)
A secondorder response surface design in k variables consisting of a twolevel factorial, 2k axial runs, and one or more center points. The twolevel factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a secondorder model.

Conditional mean
The mean of the conditional probability distribution of a random variable.

Conditional probability
The probability of an event given that the random experiment produces an outcome in another event.

Continuous distribution
A probability distribution for a continuous random variable.

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

Covariance matrix
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the offdiagonal elements are the covariances between Xi and Xj . Also called the variancecovariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.

Critical region
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Dispersion
The amount of variability exhibited by data

Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).

Enumerative study
A study in which a sample from a population is used to make inference to the population. See Analytic study

Expected value
The expected value of a random variable X is its longterm average or mean value. In the continuous case, the expected value of X is E X xf x dx ( ) = ?? ( ) ? ? where f ( ) x is the density function of the random variable X.

Generator
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .