Tuesday, Week 10. Announcements:
|
|
- Asher Blair
- 5 years ago
- Views:
Transcription
1 Tuesday, Week 10 Announcements: Thursday, October 25, 2 nd midterm in class, covering Chapters 6-8 (Confidence intervals). Charissa Mikoski, the TA for our class, will be administering the exam (I will be at a conference). Tasks for Today 1. Collect GH4 and GH5 2. Finish lecturing on Chapter 8. Confidence intervals when σ is known and when σ is not known.
2 Which was harder for you to do. A. GH4 on Chapter 7, sampling distributions of means was harder B. GH5 on Chapter 8, confidence intervals was harder C. Both were very hard D. Both were pretty easy. If you wanted to include the middle 95 percent of a normal distribution, what Z-scores would mark the upper boundary of the middle 95 percent? A B C D
3 Chapter 8 Confidence Intervals Where is mu (the population mean)?
4 Where is mu, µ, the unknown population mean? Based on the phrase Hurlburt told you (in the lectlets) to memorize you say, I, but it is probably within about (a number) s of the sample mean, Xbar. A. 1 B. 2 C. 3 D.???? A. deviations B. standard deviations C. errors D. standard errors E.???????
5
6
7 From Chapter 7: For All Normally Shaped Sampling Distributions of Means: If 95 percent of all the sample means, X- bars, in a normally shaped sampling distribution of means fall within 1.96 standard errors of the population mean, mu, then if I take one large-n sample (e.g., 1000 cases in the sample) and calculate it s mean, X-bar, what is the chance that the unknown population mean, mu, is within 1.96 standard errors of the mean from X-bar? A. 2.5 percent B. 5 percent C percent D. 95 percent E. One cannot answer the question without more information.
8
9
10 Formula when σ, sigma, st. deviation of the population is known Foreshadowing the last part of this lecture: Formula when σ, sigma, st. deviation of the population is NOT known
11
12 1. In a normal distribution, the middle 95 percent of values lie between what two values of Z: and. A. 1 and 1 B and 1.96 C. -2 and 2 D. It is impossible to say without knowing the mean and standard deviation. 2. These two values of Z (described in the previous question are symbolized as and called values. A. sigma sub Xbar, standard error B. sigma, standard deviation C. Zsub Xbar, Z D. ZsubCV, critical
13 In the above formula for the confidence interval when sigma (population standard deviation) is known, Represents: A. the unknown population mean B. the sample mean C. the standard deviation of the sample D. the standard deviation of the sampling distr. of means, standard error Represents: A. the unknown population mean B. the sample mean C. the standard deviation of the sample D. the standard deviation of the sampling distr. of means, standard error
14 In the above formula for the confidence interval when sigma (population standard deviation) is known, the value of A. is determined by looking it up in a table. B. is determined by calculating it from the data and/or information given to you. C. Needs to be given to you in the problem D. is the value that you are trying to estimate in the problem. E. I don t know. A. is determined by looking it up in a table. B. is determined by calculating it from the data and/or information given to you. C. Needs to be given to you in the problem D. is the value that you are trying to estimate in the problem. E. I don t know. A. is determined by looking it up in a table. B. is determined by calculating it from the data and/or information given to you. C. Needs to be given to you in the problem D. is the value that you are trying to estimate in the problem. E. I don t know. A. is determined by looking it up in a table. B. is determined by calculating it from the data and/or information given to you. C. Needs to be given to you in the problem D. is the value that you are trying to estimate in the problem. E. I don t know.
15 In the above formula for the confidence interval when sigma (population standard deviation) is known, the following part of the formula: Is best described as: A. the lower limit of the 95 percent confidence interval. B. the upper limit of the 95 percent confidence interval. C. the probability that mu is between the upper and lower limits of the confidence interval. D. I don t know.
16 Student s home Known location 50 (how many) miles Campus location unknown
17 n = 16 Xbar = 102 IQ points Sigma, standard deviation of population of IQ scores = 16. Find lower and upper limit of 95 percent confidence interval Work with a partner and solve.
18 n = 16 Xbar = 102 IQ points Sigma, standard deviation of population of IQ scores = 16. Find lower and upper limit of 95 percent confidence interval
19
20
21 There is a different t-distribution for every different value of n (sample size) The bigger the sample size, the less error there is in estimating sigma from s And the more the t-distribution resembles the normal distribution
22
23 In the above formula for a 95 percent confidence interval for when sigma is unknown, the symbol: A. is determined by looking it up in a table. B. is determined by calculating it from the data and/or information given to you. C. Needs to be given to you in the problem D. is the value that you are trying to estimate in the problem. E. I don t know. A. is determined by looking it up in a table. B. is determined by calculating it from the data and/or information given to you. C. Needs to be given to you in the problem D. is the value that you are trying to estimate in the problem. E. I don t know. A. is determined by looking it up in a table. B. is determined by calculating it from the data and/or information given to you. C. Needs to be given to you in the problem D. is the value that you are trying to estimate in the problem. E. I don t know.
24
25 Find Critical Value for 95 percent confidence interval when n= 10
26 Find the correct critical value in the table for 95 percent confidence interval when n= 27 A B C D E. I don t know Find the correct critical value in the table for 95 percent confidence interval when n= 71 A B C. some value between 1.98 and 2.00 D. I don t know In this class, if the degrees of freedom you want is not in the table, use the bigger value for the critical value of t (which corresponds to a smaller degrees of freedom).
27 From Homework, low birth weight babies, σ is not known. Find lower limit and upper limit of 95 percent confidence interval. Work with partners, share the table. N =10
28 Need to estimate the standard error first. Don t know this When sigma is unknown, there is a 95% chance that the mean IQ for all low birth weight babies is between and IQ points.
Determining Sample Size. Slide 1 ˆ ˆ. p q n E = z α / 2. (solve for n by algebra) n = E 2
Determining Sample Size Slide 1 E = z α / 2 ˆ ˆ p q n (solve for n by algebra) n = ( zα α / 2) 2 p ˆ qˆ E 2 Sample Size for Estimating Proportion p When an estimate of ˆp is known: Slide 2 n = ˆ ˆ ( )
More informationMath 14, Homework 6.2 p. 337 # 3, 4, 9, 10, 15, 18, 19, 21, 22 Name
Name 3. Population in U.S. Jails The average daily jail population in the United States is 706,242. If the distribution is normal and the standard deviation is 52,145, find the probability that on a randomly
More informationApplications of Data Dispersions
1 Applications of Data Dispersions Key Definitions Standard Deviation: The standard deviation shows how far away each value is from the mean on average. Z-Scores: The distance between the mean and a given
More information1. Confidence Intervals (cont.)
Math 1125-Introductory Statistics Lecture 23 11/1/06 1. Confidence Intervals (cont.) Let s review. We re in a situation, where we don t know µ, but we have a number from a normal population, either an
More informationChapter 7. Confidence Intervals and Sample Sizes. Definition. Definition. Definition. Definition. Confidence Interval : CI. Point Estimate.
Chapter 7 Confidence Intervals and Sample Sizes 7. Estimating a Proportion p 7.3 Estimating a Mean µ (σ known) 7.4 Estimating a Mean µ (σ unknown) 7.5 Estimating a Standard Deviation σ In a recent poll,
More informationThe Normal Probability Distribution
1 The Normal Probability Distribution Key Definitions Probability Density Function: An equation used to compute probabilities for continuous random variables where the output value is greater than zero
More informationCH 5 Normal Probability Distributions Properties of the Normal Distribution
Properties of the Normal Distribution Example A friend that is always late. Let X represent the amount of minutes that pass from the moment you are suppose to meet your friend until the moment your friend
More information1/12/2011. Chapter 5: z-scores: Location of Scores and Standardized Distributions. Introduction to z-scores. Introduction to z-scores cont.
Chapter 5: z-scores: Location of Scores and Standardized Distributions Introduction to z-scores In the previous two chapters, we introduced the concepts of the mean and the standard deviation as methods
More informationMath 124: Module 8 (Normal Distribution) Normally Distributed Random Variables. Solving Normal Problems with Technology
( ( What we will do today ly Rom Stard ( David Meredith Department of Mathematics San Francisco State University October 6, 2009 ly Rom Stard 1 ly Rom 2 3 Stard 4 ( ( Rom ly Rom Stard A variable is a characteristic
More informationClass 16. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 16 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 013 by D.B. Rowe 1 Agenda: Recap Chapter 7. - 7.3 Lecture Chapter 8.1-8. Review Chapter 6. Problem Solving
More informationEstimation and Confidence Intervals
Estimation and Confidence Intervals Chapter 9-1/2 McGraw-Hill/Irwin Copyright 2011 by the McGraw-Hill Companies, Inc. All rights reserved. LEARNING OBJECTIVES LO1. Define a point estimate. LO2. Define
More informationMidterm Exam III Review
Midterm Exam III Review Dr. Joseph Brennan Math 148, BU Dr. Joseph Brennan (Math 148, BU) Midterm Exam III Review 1 / 25 Permutations and Combinations ORDER In order to count the number of possible ways
More informationSTAT Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model
STAT 203 - Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model In Chapter 5, we introduced a few measures of center and spread, and discussed how the mean and standard deviation are good
More informationSTAT Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model
STAT 203 - Chapter 6 The Standard Deviation (SD) as a Ruler and The Normal Model In Chapter 5, we introduced a few measures of center and spread, and discussed how the mean and standard deviation are good
More informationChapter 6.1 Confidence Intervals. Stat 226 Introduction to Business Statistics I. Chapter 6, Section 6.1
Stat 226 Introduction to Business Statistics I Spring 2009 Professor: Dr. Petrutza Caragea Section A Tuesdays and Thursdays 9:30-10:50 a.m. Chapter 6, Section 6.1 Confidence Intervals Confidence Intervals
More information* Point estimate for P is: x n
Estimation and Confidence Interval Estimation and Confidence Interval: Single Mean: To find the confidence intervals for a single mean: 1- X ± ( Z 1 σ n σ known S - X ± (t 1,n 1 n σ unknown Estimation
More informationMATH 10 INTRODUCTORY STATISTICS
MATH 10 INTRODUCTORY STATISTICS Tommy Khoo Your friendly neighbourhood graduate student. Midterm Exam ٩(^ᴗ^)۶ In class, next week, Thursday, 26 April. 1 hour, 45 minutes. 5 questions of varying lengths.
More informationElementary Statistics Triola, Elementary Statistics 11/e Unit 14 The Confidence Interval for Means, σ Unknown
Elementary Statistics We are now ready to begin our exploration of how we make estimates of the population mean. Before we get started, I want to emphasize the importance of having collected a representative
More informationAs you draw random samples of size n, as n increases, the sample means tend to be normally distributed.
The Central Limit Theorem The central limit theorem (clt for short) is one of the most powerful and useful ideas in all of statistics. The clt says that if we collect samples of size n with a "large enough
More informationNormality & confidence intervals. UNT Geog 3190, Wolverton
3190 Week 5 Normality & confidence intervals UNT Geog 3190, Wolverton 1 Source of confusion We keep hearing that with representative samples of n 30, normality can be assumed Why? UNT Geog 3190, Wolverton
More informationDensity curves. (James Madison University) February 4, / 20
Density curves Figure 6.2 p 230. A density curve is always on or above the horizontal axis, and has area exactly 1 underneath it. A density curve describes the overall pattern of a distribution. Example
More informationMeasure of Variation
Measure of Variation Variation is the spread of a data set. The simplest measure is the range. Range the difference between the maximum and minimum data entries in the set. To find the range, the data
More information5.1 Mean, Median, & Mode
5.1 Mean, Median, & Mode definitions Mean: Median: Mode: Example 1 The Blue Jays score these amounts of runs in their last 9 games: 4, 7, 2, 4, 10, 5, 6, 7, 7 Find the mean, median, and mode: Example 2
More informationLecture 8: Single Sample t test
Lecture 8: Single Sample t test Review: single sample z-test Compares the sample (after treatment) to the population (before treatment) You HAVE to know the populational mean & standard deviation to use
More informationMA 1125 Lecture 05 - Measures of Spread. Wednesday, September 6, Objectives: Introduce variance, standard deviation, range.
MA 115 Lecture 05 - Measures of Spread Wednesday, September 6, 017 Objectives: Introduce variance, standard deviation, range. 1. Measures of Spread In Lecture 04, we looked at several measures of central
More informationNumerical Descriptive Measures. Measures of Center: Mean and Median
Steve Sawin Statistics Numerical Descriptive Measures Having seen the shape of a distribution by looking at the histogram, the two most obvious questions to ask about the specific distribution is where
More informationClass 13. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 13 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 017 by D.B. Rowe 1 Agenda: Recap Chapter 6.3 6.5 Lecture Chapter 7.1 7. Review Chapter 5 for Eam 3.
More informationElementary Statistics
Chapter 7 Estimation Goal: To become familiar with how to use Excel 2010 for Estimation of Means. There is one Stat Tool in Excel that is used with estimation of means, T.INV.2T. Open Excel and click on
More informationStudy Ch. 7.3, # 63 71
GOALS: 1. Understand the distribution of the sample mean. 2. Understand that using the distribution of the sample mean with sufficiently large sample sizes enables us to use parametric statistics for distributions
More informationA continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x)
Section 6-2 I. Continuous Probability Distributions A continuous random variable is one that can theoretically take on any value on some line interval. We use f ( x) to represent a probability density
More informationECON 214 Elements of Statistics for Economists 2016/2017
ECON 214 Elements of Statistics for Economists 2016/2017 Topic The Normal Distribution Lecturer: Dr. Bernardin Senadza, Dept. of Economics bsenadza@ug.edu.gh College of Education School of Continuing and
More informationSTA 320 Fall Thursday, Dec 5. Sampling Distribution. STA Fall
STA 320 Fall 2013 Thursday, Dec 5 Sampling Distribution STA 320 - Fall 2013-1 Review We cannot tell what will happen in any given individual sample (just as we can not predict a single coin flip in advance).
More informationLecture 35 Section Wed, Mar 26, 2008
on Lecture 35 Section 10.2 Hampden-Sydney College Wed, Mar 26, 2008 Outline on 1 2 3 4 5 on 6 7 on We will familiarize ourselves with the t distribution. Then we will see how to use it to test a hypothesis
More informationStatistics 13 Elementary Statistics
Statistics 13 Elementary Statistics Summer Session I 2012 Lecture Notes 5: Estimation with Confidence intervals 1 Our goal is to estimate the value of an unknown population parameter, such as a population
More informationStandard Deviation. 1 Motivation 1
Standard Deviation Table of Contents 1 Motivation 1 2 Standard Deviation 2 3 Computing Standard Deviation 4 4 Calculator Instructions 7 5 Homework Problems 8 5.1 Instructions......................................
More informationHomework: Due Wed, Nov 3 rd Chapter 8, # 48a, 55c and 56 (count as 1), 67a
Homework: Due Wed, Nov 3 rd Chapter 8, # 48a, 55c and 56 (count as 1), 67a Announcements: There are some office hour changes for Nov 5, 8, 9 on website Week 5 quiz begins after class today and ends at
More informationName PID Section # (enrolled)
STT 315 - Lecture 3 Instructor: Aylin ALIN 04/02/2014 Midterm # 2 A Name PID Section # (enrolled) * The exam is closed book and 80 minutes. * You may use a calculator and the formula sheet that you brought
More informationLESSON 7 INTERVAL ESTIMATION SAMIE L.S. LY
LESSON 7 INTERVAL ESTIMATION SAMIE L.S. LY 1 THIS WEEK S PLAN Part I: Theory + Practice ( Interval Estimation ) Part II: Theory + Practice ( Interval Estimation ) z-based Confidence Intervals for a Population
More informationLecture 2 INTERVAL ESTIMATION II
Lecture 2 INTERVAL ESTIMATION II Recap Population of interest - want to say something about the population mean µ perhaps Take a random sample... Recap When our random sample follows a normal distribution,
More information6.1 Graphs of Normal Probability Distributions:
6.1 Graphs of Normal Probability Distributions: Normal Distribution one of the most important examples of a continuous probability distribution, studied by Abraham de Moivre (1667 1754) and Carl Friedrich
More informationSTAB22 section 1.3 and Chapter 1 exercises
STAB22 section 1.3 and Chapter 1 exercises 1.101 Go up and down two times the standard deviation from the mean. So 95% of scores will be between 572 (2)(51) = 470 and 572 + (2)(51) = 674. 1.102 Same idea
More informationHomework: Due Wed, Feb 20 th. Chapter 8, # 60a + 62a (count together as 1), 74, 82
Announcements: Week 5 quiz begins at 4pm today and ends at 3pm on Wed If you take more than 20 minutes to complete your quiz, you will only receive partial credit. (It doesn t cut you off.) Today: Sections
More informationSTT 315 Handout and Project on Correlation and Regression (Unit 11)
STT 315 Handout and Project on Correlation and Regression (Unit 11) This material is self contained. It is an introduction to regression that will help you in MSC 317 where you will study the subject in
More informationTwo Populations Hypothesis Testing
Two Populations Hypothesis Testing Two Proportions (Large Independent Samples) Two samples are said to be independent if the data from the first sample is not connected to the data from the second sample.
More information8.3 CI for μ, σ NOT known (old 8.4)
GOALS: 1. Learn the properties of the student t distribution and the t curve. 2. Understand how degrees of freedom, df, relate to t curves. 3. Recognize that t curves approach the SNC as df increases.
More informationCHAPTER 8. Confidence Interval Estimation Point and Interval Estimates
CHAPTER 8. Confidence Interval Estimation Point and Interval Estimates A point estimate is a single number, a confidence interval provides additional information about the variability of the estimate Lower
More informationVersion A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.
Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x
More informationStatistics for Managers Using Microsoft Excel 7 th Edition
Statistics for Managers Using Microsoft Excel 7 th Edition Chapter 7 Sampling Distributions Statistics for Managers Using Microsoft Excel 7e Copyright 2014 Pearson Education, Inc. Chap 7-1 Learning Objectives
More informationIOP 201-Q (Industrial Psychological Research) Tutorial 5
IOP 201-Q (Industrial Psychological Research) Tutorial 5 TRUE/FALSE [1 point each] Indicate whether the sentence or statement is true or false. 1. To establish a cause-and-effect relation between two variables,
More informationHypothesis Tests: One Sample Mean Cal State Northridge Ψ320 Andrew Ainsworth PhD
Hypothesis Tests: One Sample Mean Cal State Northridge Ψ320 Andrew Ainsworth PhD MAJOR POINTS Sampling distribution of the mean revisited Testing hypotheses: sigma known An example Testing hypotheses:
More informationLecture 9 - Sampling Distributions and the CLT. Mean. Margin of error. Sta102/BME102. February 6, Sample mean ( X ): x i
Lecture 9 - Sampling Distributions and the CLT Sta102/BME102 Colin Rundel February 6, 2015 http:// pewresearch.org/ pubs/ 2191/ young-adults-workers-labor-market-pay-careers-advancement-recession Sta102/BME102
More informationBIOL The Normal Distribution and the Central Limit Theorem
BIOL 300 - The Normal Distribution and the Central Limit Theorem In the first week of the course, we introduced a few measures of center and spread, and discussed how the mean and standard deviation are
More informationChapter Seven: Confidence Intervals and Sample Size
Chapter Seven: Confidence Intervals and Sample Size A point estimate is: The best point estimate of the population mean µ is the sample mean X. Three Properties of a Good Estimator 1. Unbiased 2. Consistent
More informationDescribing Data: One Quantitative Variable
STAT 250 Dr. Kari Lock Morgan The Big Picture Describing Data: One Quantitative Variable Population Sampling SECTIONS 2.2, 2.3 One quantitative variable (2.2, 2.3) Statistical Inference Sample Descriptive
More informationThe Central Limit Theorem for Sample Means (Averages)
The Central Limit Theorem for Sample Means (Averages) By: OpenStaxCollege Suppose X is a random variable with a distribution that may be known or unknown (it can be any distribution). Using a subscript
More informationSection 7-2 Estimating a Population Proportion
Section 7- Estimating a Population Proportion 1 Key Concept In this section we present methods for using a sample proportion to estimate the value of a population proportion. The sample proportion is the
More information. 13. The maximum error (margin of error) of the estimate for μ (based on known σ) is:
Statistics Sample Exam 3 Solution Chapters 6 & 7: Normal Probability Distributions & Estimates 1. What percent of normally distributed data value lie within 2 standard deviations to either side of the
More informationClass 12. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 12 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 2017 by D.B. Rowe 1 Agenda: Recap Chapter 6.1-6.2 Lecture Chapter 6.3-6.5 Problem Solving Session. 2
More information22.2 Shape, Center, and Spread
Name Class Date 22.2 Shape, Center, and Spread Essential Question: Which measures of center and spread are appropriate for a normal distribution, and which are appropriate for a skewed distribution? Eplore
More informationChapter 8 Statistical Intervals for a Single Sample
Chapter 8 Statistical Intervals for a Single Sample Part 1: Confidence intervals (CI) for population mean µ Section 8-1: CI for µ when σ 2 known & drawing from normal distribution Section 8-1.2: Sample
More informationECON 214 Elements of Statistics for Economists
ECON 214 Elements of Statistics for Economists Session 7 The Normal Distribution Part 1 Lecturer: Dr. Bernardin Senadza, Dept. of Economics Contact Information: bsenadza@ug.edu.gh College of Education
More informationLecture 9. Probability Distributions. Outline. Outline
Outline Lecture 9 Probability Distributions 6-1 Introduction 6- Probability Distributions 6-3 Mean, Variance, and Expectation 6-4 The Binomial Distribution Outline 7- Properties of the Normal Distribution
More informationSince his score is positive, he s above average. Since his score is not close to zero, his score is unusual.
Chapter 06: The Standard Deviation as a Ruler and the Normal Model This is the worst chapter title ever! This chapter is about the most important random variable distribution of them all the normal distribution.
More informationMath 140 Introductory Statistics
Math 140 Introductory Statistics Let s make our own sampling! If we use a random sample (a survey) or if we randomly assign treatments to subjects (an experiment) we can come up with proper, unbiased conclusions
More informationEconS Constrained Consumer Choice
EconS 305 - Constrained Consumer Choice Eric Dunaway Washington State University eric.dunaway@wsu.edu September 21, 2015 Eric Dunaway (WSU) EconS 305 - Lecture 12 September 21, 2015 1 / 49 Introduction
More informationStatistics Class 15 3/21/2012
Statistics Class 15 3/21/2012 Quiz 1. Cans of regular Pepsi are labeled to indicate that they contain 12 oz. Data Set 17 in Appendix B lists measured amounts for a sample of Pepsi cans. The same statistics
More informationLecture 9. Probability Distributions
Lecture 9 Probability Distributions Outline 6-1 Introduction 6-2 Probability Distributions 6-3 Mean, Variance, and Expectation 6-4 The Binomial Distribution Outline 7-2 Properties of the Normal Distribution
More informationLecture 16: Estimating Parameters (Confidence Interval Estimates of the Mean)
Statistics 16_est_parameters.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lecture 16: Estimating Parameters (Confidence Interval Estimates of the Mean) Some Common Sense Assumptions for Interval Estimates
More informationProb and Stats, Nov 7
Prob and Stats, Nov 7 The Standard Normal Distribution Book Sections: 7.1, 7.2 Essential Questions: What is the standard normal distribution, how is it related to all other normal distributions, and how
More informationDiscrete Random Variables
Discrete Random Variables In this chapter, we introduce a new concept that of a random variable or RV. A random variable is a model to help us describe the state of the world around us. Roughly, a RV can
More informationContents. 1 Introduction. Math 321 Chapter 5 Confidence Intervals. 1 Introduction 1
Math 321 Chapter 5 Confidence Intervals (draft version 2019/04/11-11:17:37) Contents 1 Introduction 1 2 Confidence interval for mean µ 2 2.1 Known variance................................. 2 2.2 Unknown
More informationBinomial Random Variable - The count X of successes in a binomial setting
6.3.1 Binomial Settings and Binomial Random Variables What do the following scenarios have in common? Toss a coin 5 times. Count the number of heads. Spin a roulette wheel 8 times. Record how many times
More informationCentral Limit Theorem
Central Limit Theorem Lots of Samples 1 Homework Read Sec 6-5. Discussion Question pg 329 Do Ex 6-5 8-15 2 Objective Use the Central Limit Theorem to solve problems involving sample means 3 Sample Means
More informationStatistics 511 Additional Materials
Discrete Random Variables In this section, we introduce the concept of a random variable or RV. A random variable is a model to help us describe the state of the world around us. Roughly, a RV can be thought
More informationLecture Slides. Elementary Statistics Twelfth Edition. by Mario F. Triola. and the Triola Statistics Series. Section 7.4-1
Lecture Slides Elementary Statistics Twelfth Edition and the Triola Statistics Series by Mario F. Triola Section 7.4-1 Chapter 7 Estimates and Sample Sizes 7-1 Review and Preview 7- Estimating a Population
More informationSTAT 201 Chapter 6. Distribution
STAT 201 Chapter 6 Distribution 1 Random Variable We know variable Random Variable: a numerical measurement of the outcome of a random phenomena Capital letter refer to the random variable Lower case letters
More informationChapter 4 Variability
Chapter 4 Variability PowerPoint Lecture Slides Essentials of Statistics for the Behavioral Sciences Seventh Edition by Frederick J Gravetter and Larry B. Wallnau Chapter 4 Learning Outcomes 1 2 3 4 5
More informationTop Incorrect Problems
What is the z-score for scores in the bottom 5%? a) -1.645 b) 1.645 c).4801 d) The score is not listed in the table. A professor grades 120 research papers and reports that the average score was an 80%.
More informationDescriptive Statistics (Devore Chapter One)
Descriptive Statistics (Devore Chapter One) 1016-345-01 Probability and Statistics for Engineers Winter 2010-2011 Contents 0 Perspective 1 1 Pictorial and Tabular Descriptions of Data 2 1.1 Stem-and-Leaf
More informationConfidence Intervals. σ unknown, small samples The t-statistic /22
Confidence Intervals σ unknown, small samples The t-statistic 1 /22 Homework Read Sec 7-3. Discussion Question pg 365 Do Ex 7-3 1-4, 6, 9, 12, 14, 15, 17 2/22 Objective find the confidence interval for
More information7.1 Comparing Two Population Means: Independent Sampling
University of California, Davis Department of Statistics Summer Session II Statistics 13 September 4, 01 Lecture 7: Comparing Population Means Date of latest update: August 9 7.1 Comparing Two Population
More informationDistribution. Lecture 34 Section Fri, Oct 31, Hampden-Sydney College. Student s t Distribution. Robb T. Koether.
Lecture 34 Section 10.2 Hampden-Sydney College Fri, Oct 31, 2008 Outline 1 2 3 4 5 6 7 8 Exercise 10.4, page 633. A psychologist is studying the distribution of IQ scores of girls at an alternative high
More informationSECTION 6.2 (DAY 1) TRANSFORMING RANDOM VARIABLES NOVEMBER 16 TH, 2017
SECTION 6.2 (DAY 1) TRANSFORMING RANDOM VARIABLES NOVEMBER 16 TH, 2017 TODAY S OBJECTIVES Describe the effects of transforming a random variable by: adding or subtracting a constant multiplying or dividing
More informationSTATISTICS and PROBABILITY
Introduction to Statistics Atatürk University STATISTICS and PROBABILITY LECTURE: SAMPLING DISTRIBUTIONS and POINT ESTIMATIONS Prof. Dr. İrfan KAYMAZ Atatürk University Engineering Faculty Department of
More informationThe Binomial Distribution
The Binomial Distribution January 31, 2018 Contents The Binomial Distribution The Normal Approximation to the Binomial The Binomial Hypothesis Test Computing Binomial Probabilities in R 30 Problems The
More informationA point estimate is a single value (statistic) used to estimate a population value (parameter).
Shahzad Bashir. 1 Chapter 9 Estimation & Confidence Interval Interval Estimation for Population Mean: σ Known Interval Estimation for Population Mean: σ Unknown Determining the Sample Size 2 A point estimate
More informationChapter 7 - Lecture 1 General concepts and criteria
Chapter 7 - Lecture 1 General concepts and criteria January 29th, 2010 Best estimator Mean Square error Unbiased estimators Example Unbiased estimators not unique Special case MVUE Bootstrap General Question
More informationChapter 6 Confidence Intervals Section 6-1 Confidence Intervals for the Mean (Large Samples) Estimating Population Parameters
Chapter 6 Confidence Intervals Section 6-1 Confidence Intervals for the Mean (Large Samples) Estimating Population Parameters VOCABULARY: Point Estimate a value for a parameter. The most point estimate
More informationCSC Advanced Scientific Programming, Spring Descriptive Statistics
CSC 223 - Advanced Scientific Programming, Spring 2018 Descriptive Statistics Overview Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions.
More informationGETTING STARTED. To OPEN MINITAB: Click Start>Programs>Minitab14>Minitab14 or Click Minitab 14 on your Desktop
Minitab 14 1 GETTING STARTED To OPEN MINITAB: Click Start>Programs>Minitab14>Minitab14 or Click Minitab 14 on your Desktop The Minitab session will come up like this 2 To SAVE FILE 1. Click File>Save Project
More information5.3 Interval Estimation
5.3 Interval Estimation Ulrich Hoensch Wednesday, March 13, 2013 Confidence Intervals Definition Let θ be an (unknown) population parameter. A confidence interval with confidence level C is an interval
More informationThe Binomial Distribution
The Binomial Distribution January 31, 2019 Contents The Binomial Distribution The Normal Approximation to the Binomial The Binomial Hypothesis Test Computing Binomial Probabilities in R 30 Problems The
More informationSec$on 6.1: Discrete and Con.nuous Random Variables. Tuesday, November 14 th, 2017
Sec$on 6.1: Discrete and Con.nuous Random Variables Tuesday, November 14 th, 2017 Discrete and Continuous Random Variables Learning Objectives After this section, you should be able to: ü COMPUTE probabilities
More informationStatistics vs. statistics
Statistics vs. statistics Question: What is Statistics (with a capital S)? Definition: Statistics is the science of collecting, organizing, summarizing and interpreting data. Note: There are 2 main ways
More informationMidterm Test 1 (Sample) Student Name (PRINT):... Student Signature:... Use pencil, so that you can erase and rewrite if necessary.
MA 180/418 Midterm Test 1 (Sample) Student Name (PRINT):............................................. Student Signature:................................................... Use pencil, so that you can erase
More informationData Analysis and Statistical Methods Statistics 651
Data Analysis and Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasini/teaching.html Lecture 13 (MWF) Designing the experiment: Margin of Error Suhasini Subba Rao Terminology: The population
More informationμ: ESTIMATES, CONFIDENCE INTERVALS, AND TESTS Business Statistics
μ: ESTIMATES, CONFIDENCE INTERVALS, AND TESTS Business Statistics CONTENTS Estimating parameters The sampling distribution Confidence intervals for μ Hypothesis tests for μ The t-distribution Comparison
More informationBoth the quizzes and exams are closed book. However, For quizzes: Formulas will be provided with quiz papers if there is any need.
Both the quizzes and exams are closed book. However, For quizzes: Formulas will be provided with quiz papers if there is any need. For exams (MD1, MD2, and Final): You may bring one 8.5 by 11 sheet of
More informationData Analysis and Statistical Methods Statistics 651
Data Analysis and Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasini/teaching.html Lecture 14 (MWF) The t-distribution Suhasini Subba Rao Review of previous lecture Often the precision
More information10/1/2012. PSY 511: Advanced Statistics for Psychological and Behavioral Research 1
PSY 511: Advanced Statistics for Psychological and Behavioral Research 1 Pivotal subject: distributions of statistics. Foundation linchpin important crucial You need sampling distributions to make inferences:
More information