1 / 33

Quant 3610

Quant 3610. Weber State University. Dr. Stephen Hays Russell. Part I Course Introduction. Course Introduction. Me You Approach in this course MINITAB The Syllabus No Stat Text required Definitions and refresher concepts.

shayna
Télécharger la présentation

Quant 3610

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Quant 3610 Weber State University Dr. Stephen Hays Russell

  2. Part I Course Introduction

  3. Course Introduction Me You Approach in this course MINITAB The Syllabus No Stat Text required Definitions and refresher concepts

  4. Definitions • Statistics (plural) • A set of data • Statistics (singular) • A science, a quantitative discipline concerned with the methods of collection and analysis of data to facilitate wise decision making in the face of uncertainty. • Elementary units • Persons or objects comprising a population of interest. • Population • All elementary units of a defined group • Census • An exhaustive survey on some characteristic of interest of every elementary unit in a population

  5. Frame • A complete listing of all elementary units • Parameter (usually Greek letters—like “ F” or “ ”) • A number that describes a characteristic of the population (“The Truth”) • Sample • Any chosen subset of a population • Simple Random Sample • A sample chosen in such a way that each elementary unit has an equal chance of being included • Statistic (usually Roman letters—like “x” or “b”) • A numeric measure from sample information of some characteristic of interest in a population

  6. Random Variable • Any variable that takes on numeric values on the basis of chance • Inferential statistics • Inferring the values of unknown population parameters from sample statistics • Unbiased Estimator • The expected value of the estimator equals the true value of the parameter being estimated • Sampling distribution • A listing of all possible values of a sample statistic along with the associated probabilities of their occurrence. • Sampling error • The difference between the sample statistic and the true parameter value

  7. Normal Probability Distribution • Bell shaped, symmetrical, uniquely defined by the mean and the standard deviation • Z Table (Standard Normal Table) • What proportion of normally distributed data lie within + or – one standard deviation of the mean? • Within + or – two standard deviations of the mean? • Within + or – three standard deviations of the mean?

  8. Standard Normal Distribution • Mean of Zero; Standard deviation of One • Any normally distributed data can be converted to a standard normal

  9. Practice problem • A manufacturer knows that the lifetime of its jet engine is normally distributed with a mean of 2,000 hours and a standard deviation of 100 hours. • What is the probability that a randomly chosen engine has a lifetime greater than 2,170 hours? Less than 1,840 hours? Between 2,000 and 2,075 hours? • Answers: .0446; .0548; .2734

  10. Another practice problem: • A social biologist has established that the heights of adult men in the U.S. are normally distributed with a mean of 68 inches and a standard deviation of 4 inches. • The tallest 10% of adult men are at least how tall? (or put differently, 90% of all men are ____ inches tall or less.) • Answer: 73.1 inches (6’ 1”)

  11. Power Point Slides on the Web: • http://faculty.weber.edu/srussell • CLICK Quant 3610, Part I (etc.)

  12. MINITAB Commands • CDF • Finds the area under the normal curve between “minus infinity” and a specified Z • Example: CDF for –l.96 • (= .025) • Example: CDF for 80; Normal 55, 6. • INVCDF • Associates a specific area between “minus infinity” and an unknown Z • Example: INVCDF for .025 • (= -1.96) • Example: INVCDF FOR .975 • (= +1.96) • Example: INVCDF for .9; Normal 68, 4.

  13. Two theorems in inferential statistics • Theorem One relates to populations that are normally distributed • Theorem Two—Called the Central Limit Theorem—relates to populations that are not normally distributed

  14. Theorem One If X (the mean of a simple random sample) is taken from a large population of X values and if the N population values are normally distributed, the sampling distribution of X is also normally distributed, regardless of sample size, n. Given a normally distributed population:

  15. Theorem Two • If X (the mean of a simple random sample) is taken from a large population of X values and if the N population values are not normally distributed, the sampling distribution of X nevertheless approaches a normal distribution as sample size, n, increases. • Any sampling distribution of X is considered normal provided n  30 and also n < .05 N. [Large sample assumption] [Large population assumption] Given a population that is not normally distributed, the Central Limit Theorem (CLT) states that:

  16. Central Limit Theorem

  17. Central Limit Theorem

  18. Central Limit Theorem

  19. The Central Limit Theorem • Perhaps the most important theorem in the entire field of statistical inference. • Consider what it says: • As long as we take random samples that are sufficiently large absolutely and fairly small relative to population size, we can consider the distribution of sample means to be a normal curve (and proceed to infer population parameters from sample statistics on that assumption). • We need not know or be concerned with the shape of the underlying population distribution (which is often unknowable given the problems with census taking).

  20. Sampling Distribution of the sample mean (X) The sampling distribution of the sample mean relates to the parent population like this:

  21. Sampling Distribution of the sample mean (X) When selections of sample elements are statistically independent events; typically referred to as "the large population case," because n < .05N Formula 1A Formula 1B The sampling distribution of the sample mean relates to the parent population like this for the large population case:

  22. Distribution of X

  23. Sampling Distribution of the sample mean (X) When selections of sample elements are statistically dependent events; typically referred to as "the small population case,” because n.05 N Formula 1C Formula 1D The sampling distribution of the sample mean relates to the parent population like this for the small population case:

  24. Standard Normal Deviate for X • When X follows a normal distribution, X can be converted to a standard normal:

  25. Practice Problem with X • Mercury makes the Laser XRi engine used in speed boats. The company claims the engine delivers an average 220 horsepower and that the standard deviation of power delivered is 15 horsepower. • A potential boat manufacturer tests 100 randomly chosen XRi engines for single tests. If the claim is true, what is the probability that the sample mean will be less than 217 horsepower? • Answer: .0228

  26. Sampling distribution of the sample proportion • The sample proportion is • The population proportion is B.

  27. Sampling distribution of the sample proportion • The sample proportion follows a normal distribution per the CLT if large samples are taken from a large population: If nB  5 and if n(1-B)  5 we have a large sample.

  28. Sampling Distribution of the sample proportion How the sampling distribution of the sample proportion (P) relates to the parent population:

  29. Sampling Distribution of the sample proportion When selections of sample elements are statistically independent events; typically referred to as "the large population case,” because n < .05N Formula 1E Formula 1F How the sampling distribution of the sample proportion (P) relates to the parent population large population case:

  30. Sampling Distribution of the sample proportion When selections of sample elements are statistically dependent events; typically referred to as "the small population case,” because n  .05 N Formula 1G Formula 1H How the sampling distribution of the sample proportion (P) relates to the parent population small population case:

  31. Standard Normal Deviate for P • When P follows a normal distribution, we can standardize this random variable:

  32. Sample problem for P • Honda claims that 20% of all prospective buyers of minivans of any brand will be interested in their new 2005 Odyssey people mover. • You survey 200 randomly selected prospective buyers of minivans. You have them watch a Honda-produced promotional video on the new Odyssey and find that 33 of the prospective buyers express interest. Do you believe Honda’s claim based upon statistical analysis? • Answer: The probability of getting the sample result we obtained (or something more extreme) if their claim is true is .108 Z = (.165 - .2)/.0282843 = -1.2374

  33. Homework assignment • Handout: Quant 3610 Minitab Practice Exercises • Problem Set 1

More Related