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What else can I help you with?
The binomial and Poisson distributions are examples of discrete probability distributions?
discrete & continuous
What are the pros and cons of discrete probability distribution?
Strictly speaking, there are no cons because they are defined for discrete variables only. The only con that I could think of is the difficulty evaluating the moments and other probabilities for some discrete distributions such as the negative binomial.
What are the conditions under which you can expect to be able to use a binomial distribution to model a probability distribution?
Two independent outcomes with constant probabilities.
Does this means that all symmetric distribution are normal Explain?
Don't know what "this" is, but all symmetric distributions are not normal. There are many distributions, discrete and continuous that are not normal. The uniform or binomial distributions are examples of discrete symmetric distibutions that are not normal. The uniform and the beta distribution with equal parameters are examples of a continuous distribution that is not normal. The uniform distribution can be discrete or continuous.
Will learning about Probability Binomial and Poisson Probability Distributions using Excel make our studies easier?
If you're studying a subject involving or related to statistics and probability, then it will. If you're not, then it won't.
The binomial and Poisson distributions are examples of discrete probability distributions?
discrete & continuous
What is the study of mathematical probabilities distributions and deviations?
It is "probability".
What is normal binomial distribution?
There is no such thing. The Normal (or Gaussian) and Binomial are two distributions.
Are outcomes in binomial distributions dependent on each other?
No.
What kind of distributions are the binomial and poisson distribution?
Discrete
How do you find binomial probabilities using a binomial table?
Read the instructions that accompany the table: they do not all have exactly the same layout.
What are the pros and cons of discrete probability distribution?
Strictly speaking, there are no cons because they are defined for discrete variables only. The only con that I could think of is the difficulty evaluating the moments and other probabilities for some discrete distributions such as the negative binomial.
What is difference between skew binomial and symmetric binomial distribution?
In a symmetric binomial distribution, the probabilities of success and failure are equal, resulting in a symmetric shape of the distribution. In a skewed binomial distribution, the probabilities of success and failure are not equal, leading to an asymmetric shape where the distribution is stretched towards one side.
What kinds of distributions are there?
There are several types of distributions in statistics, including normal, binomial, Poisson, uniform, and exponential distributions. The normal distribution is bell-shaped and commonly used due to the Central Limit Theorem. Binomial distributions deal with binary outcomes, while Poisson distributions model the number of events in a fixed interval. Uniform distributions have constant probability across a range, and exponential distributions often describe time until an event occurs.
Why do you need to study binomial theorem?
Studying the binomial theorem is essential because it provides a powerful method for expanding expressions of the form (a + b)^n, enabling efficient calculations in algebra and combinatorics. It lays the groundwork for understanding probabilities, as it relates to binomial distributions, which model various real-world scenarios. Additionally, the theorem enhances problem-solving skills and is applicable in calculus, making it a vital concept in higher mathematics.
How do you determine legitimate probability distributions?
Well... the probabilities should add up to exactly 1 and cannot be negative.
What is the shape of the binomial probability distribution in rolling a die?
The distribution depends on what the variable is. If the key outcome is the number on the top of the die, the distribution in multinomial (6-valued), not binomial. If the key outcome is the number of primes, composite or neither, the distribution is trinomial. If the key outcome is the number of sixes, the distribution is binomial with unequal probabilities of success and failure. If the key outcome is odd or even the distribution is binomial with equal probabilities for the two outcomes. Thus, depending on the outcome of interest the distribution may or may not be binomial and, even when it is binomial, it can have different parameters and therefore different shapes.
