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Normal Distribution Normal Distribution • Turning from discrete to continuous distributions, in this section we discuss the normal distribution. • This is the most important continuous distribution because in applications many random variables are normal random variables (that is they have a normal distribution) or they are approximately normal or can be transformed into normal random variables in a relatively simple fashion. • Furthermore, the normal distribution is a useful approximation of more complicated distributions and it also occurs in the proofs of various statistical tests. Normal Distribution • The normal distribution or Gauss Distribution is defined as the distribution with the density • Where exp is the exponentiel function with base 𝑒 = 2.718… Normal Distribution • This is simpler than it may at first look. F(x) has these features: • 1. 𝜇 is the mean and 𝜎 the standart deviation. • 2. 1/(𝜎 2𝜋) is a constant factor that makes the area under the curve of f(x) from -∞ to ∞ equal to 1. • 3. The curve of f(x) is symmetric with respect to x = 𝜇 because the exponent is quadratic. Hence for 𝜇 = 0 it is symmetric with respect to the y-axis x=0 (bell-shaped curves) • 4. The exponential function in (1) goes to zero very fast – the faster the smaller the standart deviation 𝜎 is. Normal Distribution Distribution Function F(x) • The normal distribution has the distribution function Distribution Function F(x) • Here we needed x as the upper limit of integration and wrote 𝑣 (instead of x) in the integrand. • For the corresponding standardized normal distribution with mean 0 and standart deviation 1 we denote F(x) by 𝛷(z). • Then we simply have from (2) Distribution Function F(x) • This integrand can not be integrated by one of the methods of calculus. But this is no seriou handicap because its values can be optained from a Table***. • These vaues are needed in working with the normal distribution. • The curve of 𝛷(z) is S-shaped. • It increases monotone from 0 to 1 and intersects the vertical axis at ½. Reltion beteen F(x) and 𝛷(z) • Although your table will give you the values of F(x) in (2) with any 𝜇 and 𝜎 directly, it is important to comprehand that and such an F(x) can be expressed in terms of the tabulated standart 𝛷(z), as follows Use of normal table • The distribution function F(x) of the normal distribution with any 𝜇 and 𝜎 is related to the standardized distribution function 𝛷(z) in (3) by the formula Proof • Comparing (2) and (3) we see that we should set Then 𝑣 gives x as the upper limit of inegration. Proof • Also 𝑣 - 𝜇 = 𝜎𝑢, thus d𝑣 = 𝜎 𝑑𝑢. • Together, since 𝜎 drops out, • Probabilities corresponding to intervals will be needed quite frequently in statistics. Normal Probabilities for Intervals • The probability that a normal random variable X with mean 𝜇 and standart deviation 𝜎 assume any value in an interval a < x ≦ b is Numeric Values • In practical work with the normal distribution is good to remember about 2/3 of all values of X to be observed will lie between 𝜇 ∓ 𝜎, about 95% between 𝜇 ∓ 2𝜎, and practivally all between the three sigma limits 𝜇 ∓ 3𝜎. More precisely, Numeric Values • The formulas in (6) Show that a value deviating from 𝜇 by more than 𝜎, 2𝜎 or 3𝜎 will occur in one of about 3,20 and 300 trials respectively. Numeric Values • In statistic tests we shall ask, conversly, for the intervals that correspond to certain given probabilities; practically most important are the probabilities of 95%, 99% and 99.9%. • For these tbale gives the answers 𝜇 ∓ 2𝜎, 𝜇 ∓ 2.6𝜎 and 𝜇 ∓ 3.3𝜎, respectively. Working with the Normal Tables • There are two normal tables A7 and A8. • If you want probabilities, use A7. • If probabilities are given and corresponding intervals or x-values are wanted, use A8. • The following examples are typical. Example 1 (Reading Entries form A7) • If X is standardized normal (so that 𝜇=0 and 𝜎=1), then Example 2 (Probabilities for Given Intervals,A7) • Let X be normal with mean 0.8 and variance 4(so that 𝜎=2). Then by (4) and (5) • Or if you like it better(similarly in the other case) Example 3 (Unknown Values c for Given Probabilties, A8) • Let X be normal with mean 5 and variance 0.04. (hence standart deviation 0.2). Find c or k corresponding to the given probability. Example 4 (Defectives) • In a production iron rods let the diameter X be normally distributed wit mean 2 in. And standart deviation 0.008 in. • (a) Wht percantage of defectives can we expert if we set the tolarance limits at 2 ± 0.02 in? • (b) How should we set the tolerance limits to allow for 4% defectives? Example 4 (Defectives) Solution 1 1 4 • (a) % because from (5) and A7we obtain for the complementary event the probability Example 4 (Defectives) Solution • (b) 2 ± 0.0164 because, for the complementary event, we have Or So that A8 gives Normal Approximation of the Binomial Distribution • The probability function of the binomial distribution is • If n is large, the binomial coefficents and power become very inconvenient. • It is of great practical (ad therotical) importance that, the normal distribution proveides a good approximation of the binomial distribution, according to the following theorem, one of the most important theorems in al probability theory. Limit Theorem of De Moivre and Laplace • For large n, • Here f is given by (8). The function Limit Theorem of De Moivre and Laplace • Is the density of the normal distribution with mean 𝜇 = np and the variance 𝜎 2 = npq (the mean and the variance of the binomial distributon). • The symbol ~ (read assimptotically equal) means that the ratio of both sides approaches 1 as n approaches ∞. Furthermore, for ny nonnegativ integer 𝑎 and b ( > 𝑎),