is median
a chafractoristic of population
idon't know
It is the probability distribution function that is relevant for the experiment.
P(less than 5) represents the probability of a random variable taking on a value less than 5. To calculate this probability, you typically need to know the distribution of the variable (e.g., normal, binomial, etc.) and its parameters. Once the distribution is identified, you can compute the cumulative probability up to 5 using appropriate statistical methods or tables. Without specific context or data, it’s not possible to provide a numerical answer.
The answer depends on the level at which the student is expected to be. A 15-year old should know the probability of getting heads on the toss of a coin but even a mathematics graduate - who did not specialise in probability - would be expected to be able to prove the mathematical relationship between the Normal distribution and the F-distribution. If asked, most student would not even know what the second part of the sentence meant.
When you know for sure that the data you are trying to describe has a well-known theoretical probability distribution. For example, you 'know' from past experience that the heights of a certain age group in a school is normally distributed.
This depends on what information you have. If you know the success probability and the total number of observations, you can use the given formulas. Most of the time, this is the case. If you have data or experience which allow you to estimate the parameters, it may sometimes happen that you work like this. This mostly happens when n is very large and p very small which results in an approximation with the Poisson distribution.
idon't know
It is the probability distribution function that is relevant for the experiment.
Yes, except that if you know that the distribution is uniform there is little point in using the empirical rule.
P(less than 5) represents the probability of a random variable taking on a value less than 5. To calculate this probability, you typically need to know the distribution of the variable (e.g., normal, binomial, etc.) and its parameters. Once the distribution is identified, you can compute the cumulative probability up to 5 using appropriate statistical methods or tables. Without specific context or data, it’s not possible to provide a numerical answer.
The answer depends on the level at which the student is expected to be. A 15-year old should know the probability of getting heads on the toss of a coin but even a mathematics graduate - who did not specialise in probability - would be expected to be able to prove the mathematical relationship between the Normal distribution and the F-distribution. If asked, most student would not even know what the second part of the sentence meant.
The mean and standard deviation do not, by themselves, provide enough information to calculate probability. You also need to know the distribution of the variable in question.
When you know for sure that the data you are trying to describe has a well-known theoretical probability distribution. For example, you 'know' from past experience that the heights of a certain age group in a school is normally distributed.
I will give first the non-mathematical definition as given by Triola in Elementary Statistics: A random variable is a variable typicaly represented by x that has a a single numerical value, determined by chance for each outcome of a procedure. A probability distribution is a graph, table or formula that gives the probabability for each value of the random variable. A mathematical definition given by DeGroot in "Probability and Statistics" A real valued function that is defined in space S is called a random variable. For each random variable X and each set A of real numbers, we could calculate the probabilities. The collection of all of these probabilities is the distribution of X. Triola gets accross the idea of a collection as a table, graph or formula. Further to the definition is the types of distributions- discrete or continuous. Some well know distribution are the normal distribution, exponential, binomial, uniform, triangular and Poisson.
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.
You need to know the standard deviation or standard error to answer the question.
No, it's not a binomial. (x + 5) is an example of a binomial. A binomial has more than one term. Just look at the word binomial and you will know why. Bi means two.2xy is a monomial. Mono means one.