Binomial Distribution Part 4; deals with M.g.f,Additive property,Characteristic function of B.D & Mode of B D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
This document discusses various numerical integration techniques including Newton-Cotes formulas, the trapezoidal rule, Simpson's rules, integration with unequal segments, open integration formulas, integration of equations, and Romberg integration. The key Newton-Cotes formulas covered are the trapezoidal rule, Simpson's 1/3 rule, and Simpson's 3/8 rule. The document provides examples of applying these formulas to numerically evaluate definite integrals and calculates the associated errors. It also discusses using Richardson extrapolation, known as Romberg integration, to iteratively improve the accuracy of numerical integration compared to the standard Newton-Cotes formulas.
This document discusses the capacity of multiple-input multiple-output (MIMO) systems in wireless communications. It begins by outlining the topics to be covered, including channel capacity for single-input single-output (SISO), single-input multiple-output (SIMO), and MIMO systems employing space-time block coding. It then provides introductions and definitions for key information theory concepts such as entropy, gamma distributions, and mutual information. The document derives expressions for the channel capacities of SISO, SIMO, and MIMO systems under different assumptions about channel knowledge and fading distributions.
This MATLAB code provides an example of plotting a truncated Fourier series representation of a square wave signal. It computes the Fourier series in both complex exponential form (yce) and trigonometric form (yt) up to the Nth term, where N is an odd integer. It plots the original square wave, the truncated Fourier series approximations yce and yt, and their amplitude and phase spectra. The code demonstrates how to calculate and visualize truncated Fourier series representations of a periodic signal.
The document provides information about binomial probability distributions including:
- Binomial experiments have a fixed number (n) of independent trials with two possible outcomes and a constant probability (p) of success.
- The binomial probability distribution gives the probability of getting exactly x successes in n trials. It is calculated using the binomial coefficient and p and q=1-p.
- The mean, variance and standard deviation of a binomial distribution are np, npq, and √npq respectively.
- Examples demonstrate calculating probabilities of outcomes for binomial experiments and determining if results are significantly low or high using the range rule of μ ± 2σ.
classification of second order partial differential equationjigar methaniya
This active learning assignment discusses the classification of second order partial differential equations. The general form of a non-homogeneous second order PDE is presented. A PDE is classified as elliptic if B^2 - 4AC < 0, parabolic if B^2 - 4AC = 0, and hyperbolic if B^2 - 4AC > 0. Three examples are worked through to demonstrate classifying PDEs as elliptic, parabolic, and hyperbolic by comparing them to the general form.
The hypergeometric distribution models sampling without replacement from a finite population. It gives the probability of getting x successes in n draws from a population of size N that contains a number of successes. The mean is equal to n(a/N) and the variance is equal to n(a/N)(1-a/N)(N-n)/(N-1). When the sample size n is small compared to the population N, the binomial distribution is a good approximation to the hypergeometric.
The document discusses the classical definition of probability as well as axioms that define probability mathematically. It introduces the classical definition where probability is defined as the number of favorable outcomes divided by the total number of possible outcomes. It then discusses limitations of the classical definition and introduces the frequency interpretation of probability. Finally, it outlines three axioms that define a function as a valid probability function: 1) probabilities are between 0 and 1, 2) the total probability of the sample space is 1, and 3) probabilities of mutually exclusive events sum to the total probability.
Logic Level Techniques for Power Reduction GargiKhanna1
This document discusses various logic level techniques for low power VLSI design, including:
- Gate reorganization techniques like combining gates to reduce switching activity.
- Signal gating to block propagation of unwanted signals using AND/OR gates or latches.
- Logic encoding methods like gray code counting to reduce bit transitions.
- State machine encoding to lower expected bit transitions in the state register and outputs.
- Precomputation logic that disables inputs to combinational logic when output is invariant, reducing switching activity at the cost of increased area.
This document discusses various numerical integration techniques including Newton-Cotes formulas, the trapezoidal rule, Simpson's rules, integration with unequal segments, open integration formulas, integration of equations, and Romberg integration. The key Newton-Cotes formulas covered are the trapezoidal rule, Simpson's 1/3 rule, and Simpson's 3/8 rule. The document provides examples of applying these formulas to numerically evaluate definite integrals and calculates the associated errors. It also discusses using Richardson extrapolation, known as Romberg integration, to iteratively improve the accuracy of numerical integration compared to the standard Newton-Cotes formulas.
This document discusses the capacity of multiple-input multiple-output (MIMO) systems in wireless communications. It begins by outlining the topics to be covered, including channel capacity for single-input single-output (SISO), single-input multiple-output (SIMO), and MIMO systems employing space-time block coding. It then provides introductions and definitions for key information theory concepts such as entropy, gamma distributions, and mutual information. The document derives expressions for the channel capacities of SISO, SIMO, and MIMO systems under different assumptions about channel knowledge and fading distributions.
This MATLAB code provides an example of plotting a truncated Fourier series representation of a square wave signal. It computes the Fourier series in both complex exponential form (yce) and trigonometric form (yt) up to the Nth term, where N is an odd integer. It plots the original square wave, the truncated Fourier series approximations yce and yt, and their amplitude and phase spectra. The code demonstrates how to calculate and visualize truncated Fourier series representations of a periodic signal.
The document provides information about binomial probability distributions including:
- Binomial experiments have a fixed number (n) of independent trials with two possible outcomes and a constant probability (p) of success.
- The binomial probability distribution gives the probability of getting exactly x successes in n trials. It is calculated using the binomial coefficient and p and q=1-p.
- The mean, variance and standard deviation of a binomial distribution are np, npq, and √npq respectively.
- Examples demonstrate calculating probabilities of outcomes for binomial experiments and determining if results are significantly low or high using the range rule of μ ± 2σ.
classification of second order partial differential equationjigar methaniya
This active learning assignment discusses the classification of second order partial differential equations. The general form of a non-homogeneous second order PDE is presented. A PDE is classified as elliptic if B^2 - 4AC < 0, parabolic if B^2 - 4AC = 0, and hyperbolic if B^2 - 4AC > 0. Three examples are worked through to demonstrate classifying PDEs as elliptic, parabolic, and hyperbolic by comparing them to the general form.
The hypergeometric distribution models sampling without replacement from a finite population. It gives the probability of getting x successes in n draws from a population of size N that contains a number of successes. The mean is equal to n(a/N) and the variance is equal to n(a/N)(1-a/N)(N-n)/(N-1). When the sample size n is small compared to the population N, the binomial distribution is a good approximation to the hypergeometric.
The document discusses the classical definition of probability as well as axioms that define probability mathematically. It introduces the classical definition where probability is defined as the number of favorable outcomes divided by the total number of possible outcomes. It then discusses limitations of the classical definition and introduces the frequency interpretation of probability. Finally, it outlines three axioms that define a function as a valid probability function: 1) probabilities are between 0 and 1, 2) the total probability of the sample space is 1, and 3) probabilities of mutually exclusive events sum to the total probability.
Logic Level Techniques for Power Reduction GargiKhanna1
This document discusses various logic level techniques for low power VLSI design, including:
- Gate reorganization techniques like combining gates to reduce switching activity.
- Signal gating to block propagation of unwanted signals using AND/OR gates or latches.
- Logic encoding methods like gray code counting to reduce bit transitions.
- State machine encoding to lower expected bit transitions in the state register and outputs.
- Precomputation logic that disables inputs to combinational logic when output is invariant, reducing switching activity at the cost of increased area.
This document outlines probability density functions (PDFs) including:
- The definition of a PDF as describing the relative likelihood of a random variable taking a value.
- Properties of PDFs such as being nonnegative and integrating to 1.
- Joint PDFs describing the probability of multiple random variables taking values simultaneously.
- Marginal PDFs describing probabilities of single variables without reference to others.
- An example calculating a joint PDF and its marginals.
1. Continuous random variables are defined over intervals rather than discrete points. The probability that a continuous random variable takes on a value in an interval from a to b is given by an integral of the probability density function f(x) over that interval.
2. The probability density function f(x) defines the probabilities of intervals of the continuous random variable rather than individual points. It has the properties that it is always nonnegative and its integral over all values is 1.
3. The cumulative distribution function F(x) gives the probability that the random variable takes on a value less than or equal to x. It is defined as the integral of the probability density function from negative infinity to x.
This document provides a probability cheatsheet compiled by William Chen and Joe Blitzstein with contributions from others. It is licensed under CC BY-NC-SA 4.0 and contains information on topics like counting rules, probability definitions, random variables, expectations, independence, and more. The cheatsheet is designed to summarize essential concepts in probability.
Digital Signal Processing[ECEG-3171]-Ch1_L04Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
The exponential probability distribution is useful for describing the time it takes to complete random tasks. It can model the time between events like vehicle arrivals at a toll booth, time to complete a survey, or distance between defects on a highway. The distribution is defined by a probability density function that uses the mean time or rate of the process. It can calculate the probability that an event will occur within a certain time threshold, like the chance a car will arrive at a gas pump within 2 minutes. The mean and standard deviation of the exponential distribution are equal, and it is an extremely skewed distribution without a defined mode.
This document provides an introduction to Bayesian analysis and Metropolis-Hastings Markov chain Monte Carlo (MCMC). It explains the foundations of Bayesian analysis and how MCMC sampling methods like Metropolis-Hastings can be used to draw samples from posterior distributions that are intractable. The Metropolis-Hastings algorithm works by constructing a Markov chain with the target distribution as its stationary distribution. The document provides an example of using MCMC to perform linear regression in a Bayesian framework.
The document discusses the geometric distribution, a discrete probability distribution that models the number of Bernoulli trials needed to get one success. It defines the geometric distribution and gives its probability mass function. Some key properties and applications are discussed, including: the mean is 1/p, the variance is q/p^2, where q is 1-p. It is used in situations like modeling the probability of events occurring after repeated independent trials with a constant probability of success each trial. Examples given include analyzing success rates in sports and deciding when to stop research trials.
This document provides an overview of various probability distributions including discrete and continuous distributions. It defines key probability distributions such as binomial, Poisson, exponential, gamma, Weibull, beta, and log-normal distributions. Examples are given for how each distribution can be used to model different types of random variables and calculate probabilities. Applications of several distributions are demonstrated through examples in finance, healthcare, and engineering to show how the distributions can be used to model real-world scenarios.
This document discusses Fourier series and transforms. It begins by introducing periodic functions and their fundamental periods. It then defines Fourier series and derives the formulas for the Fourier coefficients. Several examples of calculating Fourier series are provided. It also covers Fourier series for functions with any period, complex Fourier series, Parseval's identity and its applications, and Dirichlet's theorem. The key topics of Fourier series, Fourier transforms, and their applications in engineering mathematics are covered over multiple sections.
Presentation on Fourier Series
contents are:-
Euler’s Formula
Functions having point of discontinuity
Change of interval
Even and Odd functions
Half Range series
Harmonic analysis
The Newton-Raphson method is an iterative method used to find approximations of the roots, or zeros, of a real-valued function. It uses the function's derivative to improve its guess for the root during each iteration. The method starts with an initial guess and iteratively computes better approximations until the root is found within a specified tolerance. The algorithm involves calculating the slope of the tangent line to the function at each guess and using the x-intercept of this line as the next guess. The process repeats until convergence within the tolerance is reached. The method is efficient and fast compared to other root-finding algorithms.
1) The document introduces basic concepts of probability such as sample spaces, events, outcomes, and how to calculate classical and empirical probabilities.
2) It discusses approaches to determining probability including classical, empirical, and subjective probabilities. Simulations can also be used to estimate probabilities.
3) Examples are provided to illustrate calculating probabilities using classical and empirical approaches for single and compound events with different sample spaces.
The document is a series of scanned pages from a notebook. Each page contains the repeated text "Scanned by CamScanner" and mentions the YouTube channel name "s s kiran". No other substantive information is provided across the many duplicated pages.
This document provides an overview of Fourier series and Fourier transforms. It discusses the history of Fourier analysis and how Fourier introduced Fourier series to solve heat equations. It defines Fourier series and covers topics like odd and even functions, half-range Fourier series, and the complex form of Fourier series. The document also discusses the relationship between Fourier transforms and Laplace transforms. It concludes by listing some applications of Fourier analysis in fields like electrical engineering, acoustics, optics, and more.
Presentation on Numerical Method (Trapezoidal Method)Syed Ahmed Zaki
The document discusses the trapezoidal method, which is a technique for approximating definite integrals. It provides the general formula for the trapezoidal rule, explains how it works by approximating the area under a function as a trapezoid, and discusses its history, advantages of being easy to use and having powerful convergence properties. An example application of the trapezoidal rule is shown, along with pseudocode and a C code implementation. The document concludes the trapezoidal rule can accurately integrate non-periodic and periodic functions.
Integration is used in physics to determine rates of change and distances given velocities. Numerical integration is required when the antiderivative is unknown. It involves approximating the definite integral of a function as the area under its curve between bounds. The Trapezoidal Rule approximates this area using straight lines between points, while Simpson's Rule uses quadratic or cubic functions, achieving greater accuracy with fewer points. Both methods involve dividing the area into strips and summing their widths multiplied by the function values at strip points.
Existence, Uniqueness and Stability Solution of Differential Equations with B...iosrjce
In this work, we investigate the existence ,uniqueness and stability solution of non-linear
differential equations with boundary conditions by using both method Picard approximation and
Banach fixed point theorem which were introduced by [6] .These investigations lead us to improving
and extending the above method. Also we expand the results obtained by [1] to change the non-linear
differential equations with initial condition to non-linear differential equations with boundary
conditions
1. The document discusses the Poisson probability distribution, which models random processes with discrete outcomes.
2. A Poisson experiment has properties including a known average number of successes (μ) that is proportional to the region size, with extremely small regions having virtually zero probability of success.
3. Examples of Poisson applications include the number of car accidents per month or network failures per day.
This document discusses properties of estimators such as bias and mean square error. Bias is defined as the difference between the expected value of an estimator and the true parameter value. An unbiased estimator has zero bias. Mean square error is a measure of how far an estimator is from the true parameter value on average, and incorporates both the variance and bias of the estimator. Examples are provided to demonstrate calculating the bias and mean square error of common estimators such as the sample mean and sample proportion. Having low bias and mean square error makes an estimator more efficient and accurate, especially as the sample size increases.
Negative Binomial Distribution introduction & over view under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Poisson Distribution Part 5 deals with some selected excercises of P D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
This document outlines probability density functions (PDFs) including:
- The definition of a PDF as describing the relative likelihood of a random variable taking a value.
- Properties of PDFs such as being nonnegative and integrating to 1.
- Joint PDFs describing the probability of multiple random variables taking values simultaneously.
- Marginal PDFs describing probabilities of single variables without reference to others.
- An example calculating a joint PDF and its marginals.
1. Continuous random variables are defined over intervals rather than discrete points. The probability that a continuous random variable takes on a value in an interval from a to b is given by an integral of the probability density function f(x) over that interval.
2. The probability density function f(x) defines the probabilities of intervals of the continuous random variable rather than individual points. It has the properties that it is always nonnegative and its integral over all values is 1.
3. The cumulative distribution function F(x) gives the probability that the random variable takes on a value less than or equal to x. It is defined as the integral of the probability density function from negative infinity to x.
This document provides a probability cheatsheet compiled by William Chen and Joe Blitzstein with contributions from others. It is licensed under CC BY-NC-SA 4.0 and contains information on topics like counting rules, probability definitions, random variables, expectations, independence, and more. The cheatsheet is designed to summarize essential concepts in probability.
Digital Signal Processing[ECEG-3171]-Ch1_L04Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
The exponential probability distribution is useful for describing the time it takes to complete random tasks. It can model the time between events like vehicle arrivals at a toll booth, time to complete a survey, or distance between defects on a highway. The distribution is defined by a probability density function that uses the mean time or rate of the process. It can calculate the probability that an event will occur within a certain time threshold, like the chance a car will arrive at a gas pump within 2 minutes. The mean and standard deviation of the exponential distribution are equal, and it is an extremely skewed distribution without a defined mode.
This document provides an introduction to Bayesian analysis and Metropolis-Hastings Markov chain Monte Carlo (MCMC). It explains the foundations of Bayesian analysis and how MCMC sampling methods like Metropolis-Hastings can be used to draw samples from posterior distributions that are intractable. The Metropolis-Hastings algorithm works by constructing a Markov chain with the target distribution as its stationary distribution. The document provides an example of using MCMC to perform linear regression in a Bayesian framework.
The document discusses the geometric distribution, a discrete probability distribution that models the number of Bernoulli trials needed to get one success. It defines the geometric distribution and gives its probability mass function. Some key properties and applications are discussed, including: the mean is 1/p, the variance is q/p^2, where q is 1-p. It is used in situations like modeling the probability of events occurring after repeated independent trials with a constant probability of success each trial. Examples given include analyzing success rates in sports and deciding when to stop research trials.
This document provides an overview of various probability distributions including discrete and continuous distributions. It defines key probability distributions such as binomial, Poisson, exponential, gamma, Weibull, beta, and log-normal distributions. Examples are given for how each distribution can be used to model different types of random variables and calculate probabilities. Applications of several distributions are demonstrated through examples in finance, healthcare, and engineering to show how the distributions can be used to model real-world scenarios.
This document discusses Fourier series and transforms. It begins by introducing periodic functions and their fundamental periods. It then defines Fourier series and derives the formulas for the Fourier coefficients. Several examples of calculating Fourier series are provided. It also covers Fourier series for functions with any period, complex Fourier series, Parseval's identity and its applications, and Dirichlet's theorem. The key topics of Fourier series, Fourier transforms, and their applications in engineering mathematics are covered over multiple sections.
Presentation on Fourier Series
contents are:-
Euler’s Formula
Functions having point of discontinuity
Change of interval
Even and Odd functions
Half Range series
Harmonic analysis
The Newton-Raphson method is an iterative method used to find approximations of the roots, or zeros, of a real-valued function. It uses the function's derivative to improve its guess for the root during each iteration. The method starts with an initial guess and iteratively computes better approximations until the root is found within a specified tolerance. The algorithm involves calculating the slope of the tangent line to the function at each guess and using the x-intercept of this line as the next guess. The process repeats until convergence within the tolerance is reached. The method is efficient and fast compared to other root-finding algorithms.
1) The document introduces basic concepts of probability such as sample spaces, events, outcomes, and how to calculate classical and empirical probabilities.
2) It discusses approaches to determining probability including classical, empirical, and subjective probabilities. Simulations can also be used to estimate probabilities.
3) Examples are provided to illustrate calculating probabilities using classical and empirical approaches for single and compound events with different sample spaces.
The document is a series of scanned pages from a notebook. Each page contains the repeated text "Scanned by CamScanner" and mentions the YouTube channel name "s s kiran". No other substantive information is provided across the many duplicated pages.
This document provides an overview of Fourier series and Fourier transforms. It discusses the history of Fourier analysis and how Fourier introduced Fourier series to solve heat equations. It defines Fourier series and covers topics like odd and even functions, half-range Fourier series, and the complex form of Fourier series. The document also discusses the relationship between Fourier transforms and Laplace transforms. It concludes by listing some applications of Fourier analysis in fields like electrical engineering, acoustics, optics, and more.
Presentation on Numerical Method (Trapezoidal Method)Syed Ahmed Zaki
The document discusses the trapezoidal method, which is a technique for approximating definite integrals. It provides the general formula for the trapezoidal rule, explains how it works by approximating the area under a function as a trapezoid, and discusses its history, advantages of being easy to use and having powerful convergence properties. An example application of the trapezoidal rule is shown, along with pseudocode and a C code implementation. The document concludes the trapezoidal rule can accurately integrate non-periodic and periodic functions.
Integration is used in physics to determine rates of change and distances given velocities. Numerical integration is required when the antiderivative is unknown. It involves approximating the definite integral of a function as the area under its curve between bounds. The Trapezoidal Rule approximates this area using straight lines between points, while Simpson's Rule uses quadratic or cubic functions, achieving greater accuracy with fewer points. Both methods involve dividing the area into strips and summing their widths multiplied by the function values at strip points.
Existence, Uniqueness and Stability Solution of Differential Equations with B...iosrjce
In this work, we investigate the existence ,uniqueness and stability solution of non-linear
differential equations with boundary conditions by using both method Picard approximation and
Banach fixed point theorem which were introduced by [6] .These investigations lead us to improving
and extending the above method. Also we expand the results obtained by [1] to change the non-linear
differential equations with initial condition to non-linear differential equations with boundary
conditions
1. The document discusses the Poisson probability distribution, which models random processes with discrete outcomes.
2. A Poisson experiment has properties including a known average number of successes (μ) that is proportional to the region size, with extremely small regions having virtually zero probability of success.
3. Examples of Poisson applications include the number of car accidents per month or network failures per day.
This document discusses properties of estimators such as bias and mean square error. Bias is defined as the difference between the expected value of an estimator and the true parameter value. An unbiased estimator has zero bias. Mean square error is a measure of how far an estimator is from the true parameter value on average, and incorporates both the variance and bias of the estimator. Examples are provided to demonstrate calculating the bias and mean square error of common estimators such as the sample mean and sample proportion. Having low bias and mean square error makes an estimator more efficient and accurate, especially as the sample size increases.
Negative Binomial Distribution introduction & over view under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Poisson Distribution Part 5 deals with some selected excercises of P D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Poisson Distribution Part 4 deals with some selected exercises of PD under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Poisson Distribution Part 3 deals with recurrence relations and fitting of PD under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
This document is from Suchithra's Statistics Classes and discusses the Poisson distribution in part 2. It covers the moment generating function and characteristic function of the Poisson distribution and how to derive the raw moments. It also mentions the additive property and mode of the Poisson distribution. The document encourages liking and subscribing to Suchithra's Statistics classes for more helpful content.
Poisson Distribution Part 1 deals with Definition & Moments of P D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Binomial Distribution part 1 deals with introduction & the derivation of pdf of B D under the syllabus of complementary statistics for BSc Mathematics, Physics & Computer Science.
Binomial Distribution Part 5 deals with fitting & familiaring some concepts of B D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Binomial Distribution Part 3 deals with the recurrence formula of binomial probabilities, central moment and raw moments of B D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
Binomial distribution Part 2 deals with raw moments, mean, variance, skewness & kurtosis of B D under the complementary Statistics syllabus of University of Calicut in BSc core of Mathematics, Physics & Computer Science.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
2. Binomial Distribution – B.D
Part – 4
(Based on complementary Statistics
of Bsc , University of Calicut)
Suchithra's Statistics Classes -- Binomial Distribution, Part 4
3. Suchithra's Statistics Classes -- Binomial Distribution, Part 4
P(X=x) = f(x) = nCx px qn-x
Moment generating function --- m.g.f
7. Suchithra's Statistics Classes -- Binomial Distribution, Part 4
Additive property of B. D
This can be extend to any number of independent r.vs if p is same.
If the p is not the same then X+Y will not be binomial.
9. Suchithra's Statistics Classes -- Binomial Distribution, Part 4
Mode of a probability distribution is the value of the r.v
having maximum probability.
If x is the mode of a distribution, f(x) will be the maximum
This can be split into
Mode of B.D
11. Suchithra's Statistics Classes -- Binomial Distribution, Part 4
On simplification we get
Therefore the mode corresponds to the values of x which lies
between
Combing (1) & (2)
If (n+1) p is an integer say k , then the mode is at X=k & X=k-1 ,i.e;
the B.D is bimodal.
If (n+1)p is not an integer, then integer part of (n+1)p is the mode of
the B.D (Uni model).
12. Things to be clear from this class
• How to find m.g.f of a B.D ? Hence deduce
central moments.
• Additive property of B.D.
• How to find the Characteristic function of a
B.D.
• Calculate & interpret Mode of a B.D.
Suchithra's Statistics Classes -- Binomial Distribution, Part 4
13. Thank you for watching,
if this is found to be useful then
like & subscribe.
Suchithra’s Statistics classes
Suchithra's Statistics Classes -- Binomial Distribution, Part 4