Normal Distribution It is important because of Central Limit Theorem (CTL)‚ the CTL said that Sum up a lot of i.i.d random variables the shape of the distribution will looks like Normal. Normal P.D.F Now we want to find c This integral has been proved that it cannot have close form solution. However‚ someone gives an idea that looks stupid but actually very brilliant by multiply two of them. reminds the function of circle which we can replace them to polar coordinate Thus Mean
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Normal Distribution:- A continuous random variable X is a normal distribution with the parameters mean and variance then the probability function can be written as f(x) = - < x < ‚ - < μ < ‚ σ > 0. When σ2 = 1‚ μ = 0 is called as standard normal. Normal distribution problems and solutions – Formulas: X < μ = 0.5 – Z X > μ = 0.5 + Z X = μ = 0.5 where‚ μ = mean σ = standard deviation X = normal random variable Normal Distribution Problems and Solutions – Example
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Business Statistics MGSC-372 Review Normal Distribution The Normal Distribution aka The Gaussian Distribution The Normal Distribution y 1 f ( x) e 2 1 x 2 2 x Areas under the Normal Distribution curve -3 -2 - 68% 95% 99.7% + +2 +3 X = N( ‚ 2 ) Determining Normal Probabilities Since each pair of values for and represents a different distribution‚ there are an infinite number of possible normal distributions. The number of statistical tables
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HOMEWORK 2 FROM CHAPTER 6 and 7‚ NORMAL DISTRIBUTION AND SAMPLING Instructor: Asiye Aydilek PART 1- Multiple Choice Questions ____ 1. For the standard normal probability distribution‚ the area to the left of the mean is a. –0.5 c. any value between 0 to 1 b. 0.5 d. 1 Answer: B The total area under the curve is 1. The area on the left is the half of 1 which is 0.5. ____ 2. Which of the following is not a characteristic of the normal probability distribution? a. The mean and median are
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The bell curve is literally‚ the symmetrical curve created on a graph when using a frequency distribution method for a set of data‚ splitting the mean symmetrically. There is a big difference between standard deviation and the bell curve! Standard deviation shows the difference in variation from the average; the bell curve‚ also normal distribution or Gaussian distribution‚ shows the standard deviation and is created by the normal or equal distribution of the mean among either half. The bell curve
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Marks: 1 Assume that X has a normal distribution‚ and find the indicated probability. The mean is μ = 60.0 and the standard deviation is σ = 4.0. Find the probability that X is less than 53.0. Choose one answer. a. 0.5589 b. 0.0401 c. 0.9599 d. 0.0802 Question2 Marks: 1 Use the given degree of confidence and sample data to find a confidence interval for the population standard deviation. Assume that the population has a normal distribution. Weights of eggs: 95% confidence;
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probably not attributable to chance is: (Points : 1) | Type I error Type II error Statistical significance In the semi-quartile range | 5. A score that is likely to fall into the middle 68% of scores of a normal distribution will fall inside these values: (Points : 1) | . +/- 3 standard deviations +/- 2 standard deviations +/- 1 standard deviation semi-quartile range | 6. It is important to assess the magnitude or strength
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Chapter 13: Chi-Square Applications SHORT ANSWER 1. When samples of size n are drawn from a normal population‚ the chi-square distribution is the sampling distribution of = ____________________‚ where s2 and are the sample and population variances‚ respectively. ANS: PTS: 1 OBJ: Section 13.2 2. Find the chi-square value for each of the right-tail areas below‚ given that the degrees of freedom are 7: A) 0.95 ____________________ B) 0.01 ____________________ C) 0.025 ____________________
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(2) Jan 2001 3) A fair six-sided die is rolled. The random variable Y represents the score on the uppermost‚ face. (a) Write down the probability function of Y. (b) State the name of the distribution of Y. (2) (1) Find the value of (c) E(6Y + 2)‚ (d) Var(4Y – 2).
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The Normal and Lognormal Distributions John Norstad j-norstad@northwestern.edu http://www.norstad.org February 2‚ 1999 Updated: November 3‚ 2011 Abstract The basic properties of the normal and lognormal distributions‚ with full proofs. We assume familiarity with elementary probability theory and with college-level calculus. 1 1 DEFINITIONS AND SUMMARY OF THE PROPOSITIONS 1 Definitions and Summary of the Propositions ∞ √ Proposition 1: −∞ 2 2 1 e−(x−µ) /2σ
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