 5.1.1: In 14, find the radius of convergence and interval of convergence ...
 5.1.2: In 14, find the radius of convergence and interval of convergence ...
 5.1.3: In 14, find the radius of convergence and interval of convergence ...
 5.1.4: In 14, find the radius of convergence and interval of convergence ...
 5.1.5: In 5 and 6, the given function is analytic at x = 0. Find the first...
 5.1.6: In 5 and 6, the given function is analytic at x = 0. Find the first...
 5.1.7: In 7 and 8, the given function is analytic at x = 0. Find the first...
 5.1.8: In 7 and 8, the given function is analytic at x = 0. Find the first...
 5.1.9: In 9 and 10, rewrite the given power series so that its general ter...
 5.1.10: In 9 and 10, rewrite the given power series so that its general ter...
 5.1.11: In 11 and 12, rewrite the given expression as a single power series...
 5.1.12: In 11 and 12, rewrite the given expression as a single power series...
 5.1.13: In 13 and 14, verify by direct substitution that the given power se...
 5.1.14: In 13 and 14, verify by direct substitution that the given power se...
 5.1.15: In 15 and 16, without actually solving the given differential equat...
 5.1.16: In 15 and 16, without actually solving the given differential equat...
 5.1.17: In 1728, fmd two power series solutions of the given differential ...
 5.1.18: In 1728, fmd two power series solutions of the given differential ...
 5.1.19: In 1728, fmd two power series solutions of the given differential ...
 5.1.20: In 1728, fmd two power series solutions of the given differential ...
 5.1.21: In 1728, fmd two power series solutions of the given differential ...
 5.1.22: In 1728, fmd two power series solutions of the given differential ...
 5.1.23: In 1728, fmd two power series solutions of the given differential ...
 5.1.24: In 1728, fmd two power series solutions of the given differential ...
 5.1.25: In 1728, fmd two power series solutions of the given differential ...
 5.1.26: In 1728, fmd two power series solutions of the given differential ...
 5.1.27: In 1728, fmd two power series solutions of the given differential ...
 5.1.28: In 1728, fmd two power series solutions of the given differential ...
 5.1.29: In 2932, use the power series method to solve the given initialva...
 5.1.30: In 2932, use the power series method to solve the given initialva...
 5.1.31: In 2932, use the power series method to solve the given initialva...
 5.1.32: In 2932, use the power series method to solve the given initialva...
 5.1.33: In 33 and 34, use the procedure in Example 5 to find two power seri...
 5.1.34: In 33 and 34, use the procedure in Example 5 to find two power seri...
 5.1.35: Without actually solving the differential equation (cos x)y' + y' +...
 5.1.36: How can the method described in this section be used to find a powe...
 5.1.37: Is x = 0 an ordinary or a singular point of the differential equati...
 5.1.38: For purposes of this problem, ignore the graphs given in Figure 5.1...
 5.1.39: (a) Find two power series solutions for y" + xy' + y = 0 and expres...
 5.1.40: (a) Find one more nonzero term for each of the solutions y1(x) and ...
Solutions for Chapter 5.1: Solutions about Ordinary Points
Full solutions for Advanced Engineering Mathematics  5th Edition
ISBN: 9781449691721
Solutions for Chapter 5.1: Solutions about Ordinary Points
Get Full SolutionsAdvanced Engineering Mathematics was written by and is associated to the ISBN: 9781449691721. Chapter 5.1: Solutions about Ordinary Points includes 40 full stepbystep solutions. Since 40 problems in chapter 5.1: Solutions about Ordinary Points have been answered, more than 32531 students have viewed full stepbystep solutions from this chapter. This textbook survival guide was created for the textbook: Advanced Engineering Mathematics , edition: 5. This expansive textbook survival guide covers the following chapters and their solutions.

Acceptance region
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion

Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

Analysis of variance (ANOVA)
A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

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.

Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.

Conditional probability distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables

Conidence interval
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

Contrast
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

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.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

Defect
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

Design matrix
A matrix that provides the tests that are to be conducted in an experiment.

Discrete random variable
A random variable with a inite (or countably ininite) range.

Estimate (or point estimate)
The numerical value of a point estimator.

Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

Gamma function
A function used in the probability density function of a gamma random variable that can be considered to extend factorials

Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r

Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.