8.2 Confidence Intervals for One Population Mean When σ is Known
|
|
|
- Brianne Harrington
- 7 years ago
- Views:
Transcription
1 8.2 Confidence Intervals for One Population Mean When σ is Known Tom Lewis Fall Term Confidence Intervals for One Population Mean When σ isfall Known Term / 6
2 Outline 1 An example 2 Finding general confidence intervals 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
3 An example A sample calculation The following data is drawn from a normal population with mean µ and standard deviation σ = 4. Find a 75% confidence interval. In other words, find an interval such that the chance that the true mean falls outside of it has chance Confidence Intervals for One Population Mean When σ isfall Known Term / 6
4 General confidence intervals Assume that you have collected a simple random sample from a normal population or you have collected a large sample and that the population standard deviation, σ, is known. To find a 1 α confidence interval for a population mean, µ, 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
5 General confidence intervals Assume that you have collected a simple random sample from a normal population or you have collected a large sample and that the population standard deviation, σ, is known. To find a 1 α confidence interval for a population mean, µ, calculate the mean, x; 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
6 General confidence intervals Assume that you have collected a simple random sample from a normal population or you have collected a large sample and that the population standard deviation, σ, is known. To find a 1 α confidence interval for a population mean, µ, calculate the mean, x; find z α/2 ; 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
7 General confidence intervals Assume that you have collected a simple random sample from a normal population or you have collected a large sample and that the population standard deviation, σ, is known. To find a 1 α confidence interval for a population mean, µ, calculate the mean, x; find z α/2 ; identify the confidence interval, [x z α/2 σ n, x + z α/2 σ n ] ; and, finally, 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
8 General confidence intervals Assume that you have collected a simple random sample from a normal population or you have collected a large sample and that the population standard deviation, σ, is known. To find a 1 α confidence interval for a population mean, µ, calculate the mean, x; find z α/2 ; identify the confidence interval, [x z α/2 σ n, x + z α/2 σ n ] ; and, finally, interpret the confidence interval: with probability 1 α, the true mean, µ, lies within the given interval. 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
9 Problem In each of the following, use the data set from the first slide. 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
10 Problem In each of the following, use the data set from the first slide. Find a 90% confidence interval for the data set. 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
11 Problem In each of the following, use the data set from the first slide. Find a 90% confidence interval for the data set. Find an 80% confidence interval for the data set. 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
12 Problem In each of the following, use the data set from the first slide. Find a 90% confidence interval for the data set. Find an 80% confidence interval for the data set. Precision and Confidence To gain precision, that is, to make the confidence interval more narrow you must either 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
13 Problem In each of the following, use the data set from the first slide. Find a 90% confidence interval for the data set. Find an 80% confidence interval for the data set. Precision and Confidence To gain precision, that is, to make the confidence interval more narrow you must either decrease your confidence level (say from 90% to 60%) or 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
14 Problem In each of the following, use the data set from the first slide. Find a 90% confidence interval for the data set. Find an 80% confidence interval for the data set. Precision and Confidence To gain precision, that is, to make the confidence interval more narrow you must either decrease your confidence level (say from 90% to 60%) or increase the size of your random sample. 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
15 Problem You want to estimate the mean of a normal population with a standard deviation of σ = 5 using a confidence interval of length 1. What size of random sample should you collect in order to have 90% confidence that the true mean lands within this interval? 8.2 Confidence Intervals for One Population Mean When σ isfall Known Term / 6
Confidence Intervals for One Standard Deviation Using Standard Deviation
Chapter 640 Confidence Intervals for One Standard Deviation Using Standard Deviation Introduction This routine calculates the sample size necessary to achieve a specified interval width or distance from
Point and Interval Estimates
Point and Interval Estimates Suppose we want to estimate a parameter, such as p or µ, based on a finite sample of data. There are two main methods: 1. Point estimate: Summarize the sample by a single number
Constructing and Interpreting Confidence Intervals
Constructing and Interpreting Confidence Intervals Confidence Intervals In this power point, you will learn: Why confidence intervals are important in evaluation research How to interpret a confidence
Week 4: Standard Error and Confidence Intervals
Health Sciences M.Sc. Programme Applied Biostatistics Week 4: Standard Error and Confidence Intervals Sampling Most research data come from subjects we think of as samples drawn from a larger population.
BA 275 Review Problems - Week 5 (10/23/06-10/27/06) CD Lessons: 48, 49, 50, 51, 52 Textbook: pp. 380-394
BA 275 Review Problems - Week 5 (10/23/06-10/27/06) CD Lessons: 48, 49, 50, 51, 52 Textbook: pp. 380-394 1. Does vigorous exercise affect concentration? In general, the time needed for people to complete
Population Mean (Known Variance)
Confidence Intervals Solutions STAT-UB.0103 Statistics for Business Control and Regression Models Population Mean (Known Variance) 1. A random sample of n measurements was selected from a population with
Confidence Intervals for Cpk
Chapter 297 Confidence Intervals for Cpk Introduction This routine calculates the sample size needed to obtain a specified width of a Cpk confidence interval at a stated confidence level. Cpk is a process
AMS 5 CHANCE VARIABILITY
AMS 5 CHANCE VARIABILITY The Law of Averages When tossing a fair coin the chances of tails and heads are the same: 50% and 50%. So if the coin is tossed a large number of times, the number of heads and
Good luck! BUSINESS STATISTICS FINAL EXAM INSTRUCTIONS. Name:
Glo bal Leadership M BA BUSINESS STATISTICS FINAL EXAM Name: INSTRUCTIONS 1. Do not open this exam until instructed to do so. 2. Be sure to fill in your name before starting the exam. 3. You have two hours
Confidence Intervals for Cp
Chapter 296 Confidence Intervals for Cp Introduction This routine calculates the sample size needed to obtain a specified width of a Cp confidence interval at a stated confidence level. Cp is a process
Math 201: Statistics November 30, 2006
Math 201: Statistics November 30, 2006 Fall 2006 MidTerm #2 Closed book & notes; only an A4-size formula sheet and a calculator allowed; 90 mins. No questions accepted! Instructions: There are eleven pages
Social Studies 201 Notes for November 19, 2003
1 Social Studies 201 Notes for November 19, 2003 Determining sample size for estimation of a population proportion Section 8.6.2, p. 541. As indicated in the notes for November 17, when sample size is
OPTIONS TRADING AS A BUSINESS UPDATE: Using ODDS Online to Find A Straddle s Exit Point
This is an update to the Exit Strategy in Don Fishback s Options Trading As A Business course. We re going to use the same example as in the course. That is, the AMZN trade: Buy the AMZN July 22.50 straddle
Northumberland Knowledge
Northumberland Knowledge Know Guide How to Analyse Data - November 2012 - This page has been left blank 2 About this guide The Know Guides are a suite of documents that provide useful information about
Normal distribution. ) 2 /2σ. 2π σ
Normal distribution The normal distribution is the most widely known and used of all distributions. Because the normal distribution approximates many natural phenomena so well, it has developed into a
Adverse Impact Ratio for Females (0/ 1) = 0 (5/ 17) = 0.2941 Adverse impact as defined by the 4/5ths rule was not found in the above data.
1 of 9 12/8/2014 12:57 PM (an On-Line Internet based application) Instructions: Please fill out the information into the form below. Once you have entered your data below, you may select the types of analysis
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a
The Standard Normal distribution
The Standard Normal distribution 21.2 Introduction Mass-produced items should conform to a specification. Usually, a mean is aimed for but due to random errors in the production process we set a tolerance
Using Excel for inferential statistics
FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied
Sample Size and Power in Clinical Trials
Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance
Confidence Intervals for the Difference Between Two Means
Chapter 47 Confidence Intervals for the Difference Between Two Means Introduction This procedure calculates the sample size necessary to achieve a specified distance from the difference in sample means
Analyzing Portfolio Expected Loss
Analyzing Portfolio Expected Loss In this white paper we discuss the methodologies that Visible Equity employs in the calculation of portfolio expected loss. Portfolio expected loss calculations combine
HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 1. used confidence intervals to answer questions such as...
HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 1 PREVIOUSLY used confidence intervals to answer questions such as... You know that 0.25% of women have red/green color blindness. You conduct a study of men
Introduction to Hypothesis Testing. Hypothesis Testing. Step 1: State the Hypotheses
Introduction to Hypothesis Testing 1 Hypothesis Testing A hypothesis test is a statistical procedure that uses sample data to evaluate a hypothesis about a population Hypothesis is stated in terms of the
Lesson 1: Comparison of Population Means Part c: Comparison of Two- Means
Lesson : Comparison of Population Means Part c: Comparison of Two- Means Welcome to lesson c. This third lesson of lesson will discuss hypothesis testing for two independent means. Steps in Hypothesis
The Central Limit Theorem
On August 9, 2011, SCOR SE, a global reinsurer with offices in more than 31 countries, acquired substantially all of the life reinsurance business, operations and staff of Transamerica Reinsurance, the
ESTIMATING COMPLETION RATES FROM SMALL SAMPLES USING BINOMIAL CONFIDENCE INTERVALS: COMPARISONS AND RECOMMENDATIONS
PROCEEDINGS of the HUMAN FACTORS AND ERGONOMICS SOCIETY 49th ANNUAL MEETING 200 2 ESTIMATING COMPLETION RATES FROM SMALL SAMPLES USING BINOMIAL CONFIDENCE INTERVALS: COMPARISONS AND RECOMMENDATIONS Jeff
Now we begin our discussion of exploratory data analysis.
Now we begin our discussion of exploratory data analysis. 1 Remember to keep in mind where we are in the big picture. For now, we will assume that the data we are given is a representative sample from
Market Size Forecasting Using the Monte Carlo Method. Multivariate Solutions
Market Size Forecasting Using the Monte Carlo Method Multivariate Solutions Applications and Goals The Monte Carlo market size forecast model is used primarily to determine the approximate size of product(s)
Confidence intervals
Confidence intervals Today, we re going to start talking about confidence intervals. We use confidence intervals as a tool in inferential statistics. What this means is that given some sample statistics,
SENSITIVITY ANALYSIS AND INFERENCE. Lecture 12
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
Chapter 8 Hypothesis Testing Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing
Chapter 8 Hypothesis Testing 1 Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim About a Proportion 8-5 Testing a Claim About a Mean: s Not Known 8-6 Testing
5.1 Identifying the Target Parameter
University of California, Davis Department of Statistics Summer Session II Statistics 13 August 20, 2012 Date of latest update: August 20 Lecture 5: Estimation with Confidence intervals 5.1 Identifying
Pr(X = x) = f(x) = λe λx
Old Business - variance/std. dev. of binomial distribution - mid-term (day, policies) - class strategies (problems, etc.) - exponential distributions New Business - Central Limit Theorem, standard error
Fixed-Effect Versus Random-Effects Models
CHAPTER 13 Fixed-Effect Versus Random-Effects Models Introduction Definition of a summary effect Estimating the summary effect Extreme effect size in a large study or a small study Confidence interval
Coefficient of Determination
Coefficient of Determination The coefficient of determination R 2 (or sometimes r 2 ) is another measure of how well the least squares equation ŷ = b 0 + b 1 x performs as a predictor of y. R 2 is computed
CONTENTS OF DAY 2. II. Why Random Sampling is Important 9 A myth, an urban legend, and the real reason NOTES FOR SUMMER STATISTICS INSTITUTE COURSE
1 2 CONTENTS OF DAY 2 I. More Precise Definition of Simple Random Sample 3 Connection with independent random variables 3 Problems with small populations 8 II. Why Random Sampling is Important 9 A myth,
Two-sample inference: Continuous data
Two-sample inference: Continuous data Patrick Breheny April 5 Patrick Breheny STA 580: Biostatistics I 1/32 Introduction Our next two lectures will deal with two-sample inference for continuous data As
NEW ZEALAND INJURY PREVENTION STRATEGY SERIOUS INJURY OUTCOME INDICATORS
NEW ZEALAND INJURY PREVENTION STRATEGY SERIOUS INJURY OUTCOME INDICATORS 168 MOTOR VEHICLE CRASHES IN NEW ZEALAND 213 CONTENTS TABLES Table 1 New Zealand Injury Prevention Strategy serious outcome indicators
MUHLENBERG COLLEGE /MORNING CALL. 2008 Presidential Tracking Poll
MUHLENBERG COLLEGE /MORNING CALL 2008 Presidential Tracking Poll RELEASE #20 October 16, 2008 FIELDING PERIOD October 11-15, 2008 SAMPLE 595 Likely Voters in Pennsylvania MARGIN OF ERROR - +/- 4.0% at
Charge Card Administration. Accessing the Travel Card Cardholder Training in the Learning Management System (LMS)
Accessing the Travel Card Cardholder Training in the Learning Management System (LMS) Purpose: Source: This procedure instructs the user how to access the Travel Card Cardholder Training available in the
Learning Objectives. Understand how to select the correct control chart for an application. Know how to fill out and maintain a control chart.
CONTROL CHARTS Learning Objectives Understand how to select the correct control chart for an application. Know how to fill out and maintain a control chart. Know how to interpret a control chart to determine
A) 0.1554 B) 0.0557 C) 0.0750 D) 0.0777
Math 210 - Exam 4 - Sample Exam 1) What is the p-value for testing H1: µ < 90 if the test statistic is t=-1.592 and n=8? A) 0.1554 B) 0.0557 C) 0.0750 D) 0.0777 2) The owner of a football team claims that
Unit 26 Estimation with Confidence Intervals
Unit 26 Estimation with Confidence Intervals Objectives: To see how confidence intervals are used to estimate a population proportion, a population mean, a difference in population proportions, or a difference
Test Reliability Indicates More than Just Consistency
Assessment Brief 015.03 Test Indicates More than Just Consistency by Dr. Timothy Vansickle April 015 Introduction is the extent to which an experiment, test, or measuring procedure yields the same results
Department of Civil Engineering-I.I.T. Delhi CEL 899: Environmental Risk Assessment Statistics and Probability Example Part 1
Department of Civil Engineering-I.I.T. Delhi CEL 899: Environmental Risk Assessment Statistics and Probability Example Part Note: Assume missing data (if any) and mention the same. Q. Suppose X has a normal
Hypothesis Testing for Beginners
Hypothesis Testing for Beginners Michele Piffer LSE August, 2011 Michele Piffer (LSE) Hypothesis Testing for Beginners August, 2011 1 / 53 One year ago a friend asked me to put down some easy-to-read notes
General Method: Difference of Means. 3. Calculate df: either Welch-Satterthwaite formula or simpler df = min(n 1, n 2 ) 1.
General Method: Difference of Means 1. Calculate x 1, x 2, SE 1, SE 2. 2. Combined SE = SE1 2 + SE2 2. ASSUMES INDEPENDENT SAMPLES. 3. Calculate df: either Welch-Satterthwaite formula or simpler df = min(n
WISE Power Tutorial All Exercises
ame Date Class WISE Power Tutorial All Exercises Power: The B.E.A.. Mnemonic Four interrelated features of power can be summarized using BEA B Beta Error (Power = 1 Beta Error): Beta error (or Type II
Projects Involving Statistics (& SPSS)
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
Hypothesis testing - Steps
Hypothesis testing - Steps Steps to do a two-tailed test of the hypothesis that β 1 0: 1. Set up the hypotheses: H 0 : β 1 = 0 H a : β 1 0. 2. Compute the test statistic: t = b 1 0 Std. error of b 1 =
Content Sheet 7-1: Overview of Quality Control for Quantitative Tests
Content Sheet 7-1: Overview of Quality Control for Quantitative Tests Role in quality management system Quality Control (QC) is a component of process control, and is a major element of the quality management
Measurement Systems Correlation MSC for Suppliers
Measurement Systems Correlation MSC for Suppliers Copyright 2003-2007 Raytheon Company. All rights reserved. R6σ is a Raytheon trademark registered in the United States and Europe. Raytheon Six Sigma is
COMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared. jn2@ecs.soton.ac.uk
COMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared jn2@ecs.soton.ac.uk Relationships between variables So far we have looked at ways of characterizing the distribution
Need for Sampling. Very large populations Destructive testing Continuous production process
Chapter 4 Sampling and Estimation Need for Sampling Very large populations Destructive testing Continuous production process The objective of sampling is to draw a valid inference about a population. 4-
Chapter 3 RANDOM VARIATE GENERATION
Chapter 3 RANDOM VARIATE GENERATION In order to do a Monte Carlo simulation either by hand or by computer, techniques must be developed for generating values of random variables having known distributions.
Best Practices for a Successful Retirement: A Case Study
Best Practices for a Successful Retirement: A Case Study "Contradictions do not exist. Whenever you think you are facing a contradiction, check your premises. You will find that one of them is wrong."
How To Check For Differences In The One Way Anova
MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way
NCC5010: Data Analytics and Modeling Spring 2015 Practice Exemption Exam
NCC5010: Data Analytics and Modeling Spring 2015 Practice Exemption Exam Do not look at other pages until instructed to do so. The time limit is two hours. This exam consists of 6 problems. Do all of your
Descriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
Objectives. 6.1, 7.1 Estimating with confidence (CIS: Chapter 10) CI)
Objectives 6.1, 7.1 Estimating with confidence (CIS: Chapter 10) Statistical confidence (CIS gives a good explanation of a 95% CI) Confidence intervals. Further reading http://onlinestatbook.com/2/estimation/confidence.html
Stat 411/511 THE RANDOMIZATION TEST. Charlotte Wickham. stat511.cwick.co.nz. Oct 16 2015
Stat 411/511 THE RANDOMIZATION TEST Oct 16 2015 Charlotte Wickham stat511.cwick.co.nz Today Review randomization model Conduct randomization test What about CIs? Using a t-distribution as an approximation
18.6.1 Terms concerned with internal quality control procedures
18.6.1 Terms concerned with internal quality control procedures Quality assurance in analytical laboratories Quality assurance is the essential organisational infrastructure that underlies all reliable
HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 1. used confidence intervals to answer questions such as...
HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 1 PREVIOUSLY used confidence intervals to answer questions such as... You know that 0.25% of women have red/green color blindness. You conduct a study of men
Roadmap to Data Analysis. Introduction to the Series, and I. Introduction to Statistical Thinking-A (Very) Short Introductory Course for Agencies
Roadmap to Data Analysis Introduction to the Series, and I. Introduction to Statistical Thinking-A (Very) Short Introductory Course for Agencies Objectives of the Series Roadmap to Data Analysis Provide
Information Technology Services will be updating the mark sense test scoring hardware and software on Monday, May 18, 2015. We will continue to score
Information Technology Services will be updating the mark sense test scoring hardware and software on Monday, May 18, 2015. We will continue to score all Spring term exams utilizing the current hardware
The Normal distribution
The Normal distribution The normal probability distribution is the most common model for relative frequencies of a quantitative variable. Bell-shaped and described by the function f(y) = 1 2σ π e{ 1 2σ
1) The table lists the smoking habits of a group of college students. Answer: 0.218
FINAL EXAM REVIEW Name ) The table lists the smoking habits of a group of college students. Sex Non-smoker Regular Smoker Heavy Smoker Total Man 5 52 5 92 Woman 8 2 2 220 Total 22 2 If a student is chosen
Capital Market Theory: An Overview. Return Measures
Capital Market Theory: An Overview (Text reference: Chapter 9) Topics return measures measuring index returns (not in text) holding period returns return statistics risk statistics AFM 271 - Capital Market
of course the mean is p. That is just saying the average sample would have 82% answering
Sampling Distribution for a Proportion Start with a population, adult Americans and a binary variable, whether they believe in God. The key parameter is the population proportion p. In this case let us
Getting Started with Statistics. Out of Control! ID: 10137
Out of Control! ID: 10137 By Michele Patrick Time required 35 minutes Activity Overview In this activity, students make XY Line Plots and scatter plots to create run charts and control charts (types of
A Basic Guide to Analyzing Individual Scores Data with SPSS
A Basic Guide to Analyzing Individual Scores Data with SPSS Step 1. Clean the data file Open the Excel file with your data. You may get the following message: If you get this message, click yes. Delete
Basic research methods. Basic research methods. Question: BRM.2. Question: BRM.1
BRM.1 The proportion of individuals with a particular disease who die from that condition is called... BRM.2 This study design examines factors that may contribute to a condition by comparing subjects
Experimental Uncertainty and Probability
02/04/07 PHY310: Statistical Data Analysis 1 PHY310: Lecture 03 Experimental Uncertainty and Probability Road Map The meaning of experimental uncertainty The fundamental concepts of probability 02/04/07
Confidence Intervals
Confidence Intervals I. Interval estimation. The particular value chosen as most likely for a population parameter is called the point estimate. Because of sampling error, we know the point estimate probably
Exact Confidence Intervals
Math 541: Statistical Theory II Instructor: Songfeng Zheng Exact Confidence Intervals Confidence intervals provide an alternative to using an estimator ˆθ when we wish to estimate an unknown parameter
STATISTICAL ANALYSIS AND INTERPRETATION OF DATA COMMONLY USED IN EMPLOYMENT LAW LITIGATION
STATISTICAL ANALYSIS AND INTERPRETATION OF DATA COMMONLY USED IN EMPLOYMENT LAW LITIGATION C. Paul Wazzan Kenneth D. Sulzer ABSTRACT In employment law litigation, statistical analysis of data from surveys,
Experimental Design. Power and Sample Size Determination. Proportions. Proportions. Confidence Interval for p. The Binomial Test
Experimental Design Power and Sample Size Determination Bret Hanlon and Bret Larget Department of Statistics University of Wisconsin Madison November 3 8, 2011 To this point in the semester, we have largely
INTERNATIONAL STANDARD ON AUDITING 530 AUDIT SAMPLING AND OTHER MEANS OF TESTING CONTENTS
INTERNATIONAL STANDARD ON AUDITING 530 AUDIT SAMPLING AND OTHER MEANS OF TESTING (Effective for audits of financial statements for periods beginning on or after December 15, 2004) CONTENTS Paragraph Introduction...
Statistical & Technical Team
Statistical & Technical Team A Practical Guide to Sampling This guide is brought to you by the Statistical and Technical Team, who form part of the VFM Development Team. They are responsible for advice
Sample Size Issues for Conjoint Analysis
Chapter 7 Sample Size Issues for Conjoint Analysis I m about to conduct a conjoint analysis study. How large a sample size do I need? What will be the margin of error of my estimates if I use a sample
Example: Find the expected value of the random variable X. X 2 4 6 7 P(X) 0.3 0.2 0.1 0.4
MATH 110 Test Three Outline of Test Material EXPECTED VALUE (8.5) Super easy ones (when the PDF is already given to you as a table and all you need to do is multiply down the columns and add across) Example:
Relationships Between Two Variables: Scatterplots and Correlation
Relationships Between Two Variables: Scatterplots and Correlation Example: Consider the population of cars manufactured in the U.S. What is the relationship (1) between engine size and horsepower? (2)
Lecture Notes Module 1
Lecture Notes Module 1 Study Populations A study population is a clearly defined collection of people, animals, plants, or objects. In psychological research, a study population usually consists of a specific
Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools
Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools Occam s razor.......................................................... 2 A look at data I.........................................................
Stats on the TI 83 and TI 84 Calculator
Stats on the TI 83 and TI 84 Calculator Entering the sample values STAT button Left bracket { Right bracket } Store (STO) List L1 Comma Enter Example: Sample data are {5, 10, 15, 20} 1. Press 2 ND and
STA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance
Principles of Statistics STA-201-TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis
Introduction to Hypothesis Testing OPRE 6301
Introduction to Hypothesis Testing OPRE 6301 Motivation... The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief, or hypothesis, about
QUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NON-PARAMETRIC TESTS
QUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NON-PARAMETRIC TESTS This booklet contains lecture notes for the nonparametric work in the QM course. This booklet may be online at http://users.ox.ac.uk/~grafen/qmnotes/index.html.
STATISTICS 8: CHAPTERS 7 TO 10, SAMPLE MULTIPLE CHOICE QUESTIONS
STATISTICS 8: CHAPTERS 7 TO 10, SAMPLE MULTIPLE CHOICE QUESTIONS 1. If two events (both with probability greater than 0) are mutually exclusive, then: A. They also must be independent. B. They also could
Fairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
Chapter Study Guide. Chapter 11 Confidence Intervals and Hypothesis Testing for Means
OPRE504 Chapter Study Guide Chapter 11 Confidence Intervals and Hypothesis Testing for Means I. Calculate Probability for A Sample Mean When Population σ Is Known 1. First of all, we need to find out the
NEW ZEALAND INJURY PREVENTION STRATEGY SERIOUS INJURY OUTCOME INDICATORS
NEW ZEALAND INJURY PREVENTION STRATEGY SERIOUS INJURY OUTCOME INDICATORS 166 MOTOR VEHICLE CRASHES IN NEW ZEALAND 2012 CONTENTS TABLES Table 1 New Zealand Injury Prevention Strategy serious outcome indicators
Welcome back to EDFR 6700. I m Jeff Oescher, and I ll be discussing quantitative research design with you for the next several lessons.
Welcome back to EDFR 6700. I m Jeff Oescher, and I ll be discussing quantitative research design with you for the next several lessons. I ll follow the text somewhat loosely, discussing some chapters out
Predicting The Risk Of Rheumatoid Arthritis
Predicting The Risk Of Rheumatoid Arthritis Modelling Genetic And Environmental Risk Factors Ian Scott Arthritis Research UK Clinical Research Fellow Declaration Of Interests: No Competing Interests Describe
Lecture 8. Confidence intervals and the central limit theorem
Lecture 8. Confidence intervals and the central limit theorem Mathematical Statistics and Discrete Mathematics November 25th, 2015 1 / 15 Central limit theorem Let X 1, X 2,... X n be a random sample of
TImath.com. Statistics. Areas in Intervals
Areas in Intervals ID: 9472 TImath.com Time required 30 minutes Activity Overview In this activity, students use several methods to determine the probability of a given normally distributed value being
Solution to HW - 1. Problem 1. [Points = 3] In September, Chapel Hill s daily high temperature has a mean
![Solution to HW - 1. Problem 1. [Points = 3] In September, Chapel Hill s daily high temperature has a mean](/thumbs/40/20950046.jpg "Solution to HW - 1. Problem 1. [Points = 3] In September, Chapel Hill s daily high temperature has a mean")
Problem 1. [Points = 3] In September, Chapel Hill s daily high temperature has a mean of 81 degree F and a standard deviation of 10 degree F. What is the mean, standard deviation and variance in terms
Analysis of academy school performance in GCSEs 2014
Analysis of academy school performance in GCSEs 2014 Final report Report Analysis of academy school performance in GCSEs 2013 1 Analysis of Academy School Performance in GCSEs 2014 Jack Worth Published