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You construct a 95% confidence interval for a parameter such as mean, variance etc. It is an interval in which you are 95 % certain (there is a 95 % probability) that the true unknown parameter lies. The concept of a 95% Confidence Interval (95% CI) is one that is somewhat elusive. This is primarily due to the fact that many students of statistics are simply required to memorize its definition without fully understanding its implications. Here we will try to cover both the definition as well as what the definition actually implies. The definition that students are required to memorize is: If the procedure for computing a 95% confidence interval is used over and over, 95% of the time the interval will contain the true parameter value. Students are then told that this definition does not mean that an interval has a 95% chance of containing the true parameter value. The reason that this is true, is because a 95% confidence interval will either contain the true parameter value of interest or it will not (thus, the probability of containing the true value is either 1 or 0). However, you have a 95% chance of creating one that does. In other words, this is similar to saying, "you have a 50% of getting a heads in a coin toss, however, once you toss the coin, you either have a head or a tail". Thus, you have a 95% chance of creating a 95% CI for a parameter that contains the true value. However, once you've done it, your CI either covers the parameter or it doesn't.
Confidence intervals represent an interval that is likely, at some confidence level, to contain the true population parameter of interest. Confidence interval is always qualified by a particular confidence level, expressed as a percentage. The end points of the confidence interval can also be referred to as confidence limits.
Statistics is primarily used either to make predictions based on the data available or to make conclusions about a population of interest when only sample data is available. In both cases statistics tries to make sense of the uncertainty in the available data. When making predictions statisticians determine if the difference in the data points are due to chance or if there is a systematic relationship. The more the systematic relationship that is observed the better the prediction a statistician can make. The more random error that is observed the more uncertain the prediction. Statisticians can provide a measure of the uncertainty to the prediction. When making inference about a population, the statistician is trying to estimate how good a summary statistic of a sample really is at estimating a population statistic. For example, a statistician may be asked to estimate the proportion of women who smoke in the US. This is a population statistic. The only data however may be a random sample of 1000 women. By estimating the proportion of women who smoke in the random sample of 1000, a statistician can determine how likely the sample proportion is close to the population proportion. A statistician would report the sample proportion and an interval around that sample proportion. The interval would indicate with 95% or 99% certainty that the population proportion is within that interval, assuming the sample is really random. School Grades, medical fields when determining whether something works, and marketing works
relative abundance:the number of organisms of a particular kind as a percentage of the total number of organisms of a given area or community; the number of birds of a particular species as a percentage of the total bird population of a given area percent:figured or expressed on the basis of a rate or proportion per hundred (used in combination with a number in expressing rates of interest, proportions, etc.)
Think you've got this backwards. The exponential probability distribution is a gamma probability distribution only when the first parameter, k is set to 1. Consistent with the link below, if random variable X is distributed gamma(k,theta), then for gamma(1, theta), the random variable is distributed exponentially. The gamma function in the denominator is equal to 1 when k=1. The denominator will reduce to theta when k = 1. The first term will be X0 = 1. using t to represent theta, we have f(x,t) = 1/t*exp(-x/t) or we can substitute L = 1/t, and write an equivalent function: f(x;L) = L*exp(-L*x) for x > 0 See: http://en.wikipedia.org/wiki/Gamma_distribution [edit] To the untrained eye the question might seem backwards after a quick google search, yet qouting wikipedia lacks deeper insight in to the question: What the question is referring to is a class of functions that factor into the following form: f(y;theta) = s(y)t(theta)exp[a(y)b(theta)] = exp[a(y)b(theta) + c(theta) + d(y)] where a(y), d(y) are functions only reliant on y and where b(theta) and c(theta) are answers only reliant on theta, an unkown parameter. if a(y) = y, the distribution is said to be in "canonical form" and b(theta) is often called the "natural parameter" So taking the gamma density function, where alpha is a known shape parameter and the parameter of interest is beta, the scale parameter. The density function follows as: f(y;beta) = {(beta^alpha)*[y^(alpha - 1)]*exp[-y*beta]}/gamma(alpha) where gamma(alpha) is defined as (alpha - 1)! Hence the gamma-density can be factored as follows: f(y;beta) = {(beta^alpha)*[y^(alpha - 1)]*exp[-y*beta]}/gamma(alpha) =exp[alpha*log(beta) + (alpha-1)*log(y) - y*beta - log[gamma(alpha)] from the above expression, the canonical form follows if: a(y) = y b(theta) = -beta c(theta) = alpha*log(beta) d(y) = (alpha - 1)*log(y) - log[gamma(alpha)] which is sufficient to prove that gamma distributions are part of the exponential family.
the significance is that the government profit from specific interest rates in an economy
A high proportion of fixed interest funding.
The population is a group of interest, such as the people who filled out a recent survey about their age. The parameter is the descriptive measure of that population. So in this example, a parameter could be the average age of the people who filled out the survey.
By getting an opposing topic for the two groups and conducting it by the interest of everyone who is debating.
The significance of the populism is to appeal to the interest and conceptions of the general people. Populism was a term that was used against politicians opponents.
Answer this question… Conducting lawsuits
to make the economy more effective and efficient
An equity interest definition in science refers to a proportion of ownership, typically via investment in a business. Stocks are also known as equities.
Increase the proportion of executive compensation that comes from stock options and reduce the proportion that is paid as cash salaries
When an expression is equated with some other parameter or value then it becomes an equation. for Example Principal * Time * Interest Rate /100 is an expression. Now when we equate this with Simple Interest and say SI = Principal * Time * Interest Rate /100 then this is an equation. Now in an equation like the one above, value of few parameters (SI, Principal, Time, Interest Rate all are parameters) will be known and value of some parameter will be unknown or may keep changing in any situation. These unknown or changing terms are called variables.
A pilgrimage is any long journey, usually to a place of religious significance or historical interest.
Don't know of an individual significance of interest to this name. It is of the language of Zaire (?) and means 'by the hand of the Creator'