What does the Central Limit Theorem say, in plain language? (b) What do we use the CLT for, in this class? (c) Why do we need con dence intervals? Isn't a sample mean good enough by itself?
Minimization
In mathematics, traditional optimization problems are typically expressed in terms of minimization. When we talk about minimizing or maximizing a function, we refer to the maximum and minimum possible values of that function. This can be expressed in terms of global or local range. The definition of minimization in the thesaurus is the process of reducing something to a small amount, value, or position. Minimization (noun) is an instance of belittling or disparagement.
Maxima and Minima
The extreme points of a function are the maximum and the minimum points of the function. A maximum is attained when the function takes the maximum value and a minimum is attained when the function takes the minimum value.
Derivatives
A derivative means a change. Geometrically it can be represented as a line with some steepness. Imagine climbing a mountain which is very steep and 500 meters high. Is it easier to climb? Definitely not! Suppose walking on the road for 500 meters. Which one would be easier? Walking on the road would be much easier than climbing a mountain.
Concavity
In calculus, concavity is a descriptor of mathematics that tells about the shape of the graph. It is the parameter that helps to estimate the maximum and minimum value of any of the functions and the concave nature using the graphical method. We use the first derivative test and second derivative test to understand the concave behavior of the function.
(a) What does the Central Limit Theorem say, in plain language?
(b) What do we use the CLT for, in this class?
(c) Why do we need con dence intervals? Isn't a sample
(a)
Central limit theorem states that for a sufficiently large sample size, the sampling distribution of the mean for a variable will approximate normal distribution regardless of what that variable's distribution in the population is.
In other words, as the sample size increases, the distribution of sample mean or the sample statistic becomes very much like the normal distribution irrespective of the underlying distribution.
If a sample of size is drawn from a normal population with mean and standard deviation , then the sampling distribution of sample mean is approximately normally distributed with mean and standard deviation .
As the sample size increases, the standard deviation of the sampling distribution becomes smaller because the square root of the sample size is in the denominator. In other words, the sampling distribution clusters more tightly around the mean as sample size increases.
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