Probability Distributions Assignment Help

Probability Distribution Assignment Help

Do not waste more time searching through the internet for a reliable and legit probability assignment help provider. Statisticsassignmentexperts.com has all the resources needed to help you submit remarkable solutions. The statistics assignment helpers working for us have ample industrial experience and are brilliant academic writers. They hold top academic certifications in statistics from prestigious colleges and universities in the UK, USA, and Australia.

Moreover, the probability distribution tutors associated with us are familiar with the concepts and formulas involved in this field. They can draft your assignment as per your guidelines and make sure that you attain good grades.

What is the probability distribution?

Our seasoned experts define probability distribution as a statistical function. Researchers and statistical analysts use this function to describe the possible values that a random variable can take within a particular range. In probability distribution, factors such as standard deviation, kurtosis, skewness, and the distribution’s average mean define where the possible value is to be plotted.

The Bell Curve or normal distribution is the most common probability distribution. However, several others are usually used depending on the data generating process of some phenomenon. Cumulative distributions functions (CDFs) can be created from distribution functions. CDF usually starts from 0 to 100% and adds up the probability of occurrences cumulatively.

In the business world, the probability distribution is used by financial analysts to determine a stock’s probability distribution. The information can be used to evaluate the expected returns that the stock may produce in the future.

Types of probability distributions

Probability distributions can be classified into various kinds. Some of these are the Chi-square, normal distribution, Poisson distribution, binomial distribution, etc. Each of these probability distributions serves a different purpose. They also represent different data generation processes.

For example:

  • Binomial distribution: The probability of an event occurring is evaluated several times. This is done while considering the event’s possibility in each trial and the given number of tests.
  • Normal distribution: Is frequently used in engineering, finance, science, and investment decision making. It is entirely defined by its standard deviation of 1.0 and a mean of zero. In a normal distribution, there is no skewness or kurtosis. When plotted, the curve is bell-shaped, and the distribution is symmetric.
  • Exponential Distribution: Sometimes called the negative exponential distribution. It is used to explain the time between events in a Poisson process.
  • Poisson distribution: It is named after a French mathematician, Simeon Denis Poisson. The Poisson distribution is used to model the number of events occurring within a given period. This process depends on the average number of times the event occurs over the selected time.
  • Discrete probability distribution: Describes the probability of each value of a discrete random variable. An example of a discrete random variable is a list of non-negative integers that has countable values.
  • Continuous Distribution: It models the probabilities of possible values of a continuous random variable. A continuous random variable has infinite and uncountable possible values.

Avail of our statistics assignment help to learn more about the types of probability distributions.

Popular probability distribution topics catered to by our experts

Do not hesitate to get in touch with us when you need probability distribution homework help with any of the following topics:

  • Fitting distributions to data

Our experts are familiar with all the techniques for fitting a given distribution type to the data. They can determine the best distribution type, which best reflects your data set.

  • Jarque-Bera Tests

This is a Lagrange multiplier test that is used to check normality. It checks skewness and kurtosis to see if there is a normal distribution.

  • One-sided and two-sided Kolmogorov-Smirnov Tests

This is a non-parametric test that determines whether two distributions differ or not. It can also reveal whether an underlying distribution differs from the hypothesized distribution. The Kolmogorov-Smirnov test is mostly used on two samples from different populations. It is preferred to Mann-Whitney and Wilcoxon test because it considers the distribution functions collectively. Our experts can also assist you with assignments related to this topic.

  • The Ansari- Bradley Tests

This is a non-parametric test that can be used instead of the two-sample F-test. Ansari-Bradley test assumes that the samples have equal medians. Do not struggle in silence with this type of assignment. Our statistics homework writing experts are just a few clicks of the mouse away.

  • Chi-Square goodness-of-fit tests

This is a non-parametric test that is used to explain why the observed value of a given event is significantly different from the expected value. The term goodness-of-fit provides a comparison between the expected distribution probability and the observed sample distribution.

  • Lilliefors Tests

Lilliefors tests are an improvement of the Kolmogorov-Smirnov tests. It is used to test for normality and can be used when the researcher does not know the standard deviation and mean of the population. The tests allow the researcher to estimate these parameters from the sample.

 

Other assignment topics that our experts have previously helped our clients with

  • Generation of random and quasi-random number streams from probability distributions
  • Use statistical plots to evaluate the goodness of fit.
  • Probability density functions and cumulative distribution functions
  • Negative log-likelihood functions
  • Inverse cumulative density functions
  • Cumulative density functions
  • Multivariate distributions: t, normal, copulas, and Wishart
  • Pearson and Johnson systems of distributions
  • Latin hypercube sampling
  • Sampling from finite populations

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