Confidence Intervals for One Standard Deviation Using Standard Deviation
|
|
|
- Isabella Dalton
- 7 years ago
- Views:
Transcription
1 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 the standard deviation to the confidence limit at a stated confidence level for a confidence interval about the standard deviation when the underlying data distribution is normal. Caution: This procedure assumes that the standard deviation of the future sample will be the same as the standard deviation that is specified. If the standard deviation to be used in the procedure is estimated from a previous sample or represents the population standard deviation, the Confidence Intervals for One Standard Deviation with Tolerance Probability procedure should be considered. That procedure controls the probability that the width or distance from the standard deviation to the confidence limit will be less than or equal to the value specified. The Confidence Intervals for One Standard Deviation using Relative Error controls the width or distance from the standard deviation to the limit by controlling the distance as a percent of the true standard deviation. Technical Details For a single standard deviation from a normal distribution with unknown mean, a two-sided, 100(1 α)% confidence interval is calculated by n 1 s χ1 /, n, s n 1 α 1 χ α /, n 1 A one-sided 100(1 α)% upper confidence limit is calculated by n 1 s χ, 1 α n 640-1
2 Similarly, the one-sided 100(1 α)% lower confidence limit is n 1 s χ1, 1 α n For two-sided intervals, the distance from the standard deviation to each of the limits is different. Thus, instead of specifying the distance to the limits we specify the width of the interval, W. The basic equation for determining sample size for a two-sided interval when W has been specified is 1 1 n n W = s s χ /, 1 1 /, 1 α n χ α n For one-sided intervals, the distance from the standard deviation to limits, D, is specified. The basic equation for determining sample size for a one-sided upper limit when D has been specified is D = s n 1 χ α /, n 1 The basic equation for determining sample size for a one-sided lower limit when D has been specified is s n 1 D = s s χ1 /, 1 α n These equations can be solved for any of the unknown quantities in terms of the others. Confidence Level The confidence level, 1 α, has the following interpretation. If thousands of samples of n items are drawn from a population using simple random sampling and a confidence interval is calculated for each sample, the proportion of those intervals that will include the true population standard deviation is 1 α. Procedure Options This section describes the options that are specific to this procedure. These are located on the Design tab. For more information about the options of other tabs, go to the Procedure Window chapter. Design Tab The Design tab contains most of the parameters and options that you will be concerned with. Solve For Solve For This option specifies the parameter to be solved for from the other parameters. 640-
3 One-Sided or Two-Sided Interval Interval Type Specify whether the interval to be used will be a two-sided confidence interval, an interval that has only an upper limit, or an interval that has only a lower limit. Confidence Confidence Level The confidence level, 1 α, has the following interpretation. If thousands of samples of n items are drawn from a population using simple random sampling and a confidence interval is calculated for each sample, the proportion of those intervals that will include the true population standard deviation is 1 α. Often, the values 0.95 or 0.99 are used. You can enter single values or a range of values such as 0.90, 0.95 or 0.90 to 0.99 by Sample Size N (Sample Size) Enter one or more values for the sample size. This is the number of individuals selected at random from the population to be in the study. You can enter a single value or a range of values. Precision Confidence Interval Width (Two-Sided) This is the distance from the lower confidence limit to the upper confidence limit. The distance from the standard deviation to the lower and upper limits is not equal. You can enter a single value or a list of values. The value(s) must be greater than zero. Distance from SD to Limit (One-Sided) This is the distance from the standard deviation to the lower or upper limit of the confidence interval, depending on whether the Interval Type is set to Lower Limit or Upper Limit. You can enter a single value or a list of values. The value(s) must be greater than zero. Standard Deviation S (Standard Deviation) Enter an estimate of the standard deviation (must be positive). The sample size and width calculations assume that the value entered here is the standard deviation estimate that is obtained from the sample. If the sample standard deviation is different from the one specified here, the width may be narrower or wider than specified. For controlling the probability that the width is less than the value specified, see the procedure 'Confidence Intervals for One Standard Deviation with Tolerance Probability'. For confidence intervals with widths that are specified in terms of a percentage of relative error, see the procedure 'Confidence Intervals for One Standard Deviation using Relative Error'. One common method for estimating the standard deviation is the range divided by 4, 5, or 6. You can enter a range of values such as 1 3 or 1 to 10 by 1. Press the Standard Deviation Estimator button to load the Standard Deviation Estimator window
4 Example 1 Calculating Sample Size Suppose a study is planned in which the researcher wishes to construct a two-sided 95% confidence interval for the standard deviation such that the width of the interval is no wider than 0 units. The confidence level is set at 0.95, but 0.99 is included for comparative purposes. The standard deviation estimate, based on the range of data values, is 34. Instead of examining only the interval width of 0, a series of widths from 16 to 4 will also be considered. The goal is to determine the necessary sample size. Setup This section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by expanding Variances, then clicking on One Standard Deviation, and then clicking on Confidence Intervals for One Standard Deviation using Standard Deviation. You may then make the appropriate entries as listed below, or open Example 1 by going to the File menu and choosing Open Example Template. Option Value Design Tab Solve For... Sample Size Interval Type... Two-Sided Confidence Level Confidence Interval Width (Two-Sided).. 16 to 4 by 1 S (Standard Deviation) Annotated Output Click the Calculate button to perform the calculations and generate the following output. Numeric Results Numeric Results for Two-Sided Confidence Intervals Sample Standard Confidence Size Target Actual Deviation Lower Upper Level (N) Width Width (S) Limit Limit References Hahn, G. J. and Meeker, W.Q Statistical Intervals. John Wiley & Sons. New York
5 Report Definitions Confidence level is the proportion of confidence intervals (constructed with this same confidence level, sample size, etc.) that would contain the population standard deviation. N is the size of the sample drawn from the population. Width is distance from the lower limit to the upper limit. Target Width is the value of the width that is entered into the procedure. Actual Width is the value of the width that is obtained from the procedure. Standard Deviation (S) is the assumed sample standard deviation. Lower Limit is the lower limit of the confidence interval. Upper Limit is the upper limit of the confidence interval. Summary Statements A sample size of 40 produces a two-sided 95% confidence interval with a width equal to when the standard deviation is This report shows the calculated sample size for each of the scenarios. Plots Section These plots show the sample size versus the confidence interval width for the two confidence levels
6 Example Validation using Hahn and Meeker Hahn and Meeker (1991) page 56 give an example of a calculation for a confidence interval on the standard deviation when the confidence level is 95%, the standard deviation is 1.31, and the interval width is The necessary sample size is 5. Setup This section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by expanding Variances, then clicking on One Standard Deviation, and then clicking on Confidence Intervals for One Standard Deviation using Standard Deviation. You may then make the appropriate entries as listed below, or open Example by going to the File menu and choosing Open Example Template. Option Value Design Tab Solve For... Sample Size Interval Type... Two-Sided Confidence Level Confidence Interval Width (Two-Sided) S (Standard Deviation) Output Click the Calculate button to perform the calculations and generate the following output. Numeric Results Sample Standard Confidence Size Target Actual Deviation Lower Upper Level (N) Width Width (S) Limit Limit PASS also calculated the necessary sample size to be
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
Confidence Intervals for Spearman s Rank Correlation
Chapter 808 Confidence Intervals for Spearman s Rank Correlation Introduction This routine calculates the sample size needed to obtain a specified width of Spearman s rank correlation coefficient confidence
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
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
Confidence Intervals for Exponential Reliability
Chapter 408 Confidence Intervals for Exponential Reliability Introduction This routine calculates the number of events needed to obtain a specified width of a confidence interval for the reliability (proportion
Pearson's Correlation Tests
Chapter 800 Pearson's Correlation Tests Introduction The correlation coefficient, ρ (rho), is a popular statistic for describing the strength of the relationship between two variables. The correlation
Two-Sample T-Tests Assuming Equal Variance (Enter Means)
Chapter 4 Two-Sample T-Tests Assuming Equal Variance (Enter Means) Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample t-tests when the variances of
Point Biserial Correlation Tests
Chapter 807 Point Biserial Correlation Tests Introduction The point biserial correlation coefficient (ρ in this chapter) is the product-moment correlation calculated between a continuous random variable
Two-Sample T-Tests Allowing Unequal Variance (Enter Difference)
Chapter 45 Two-Sample T-Tests Allowing Unequal Variance (Enter Difference) Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample t-tests when no assumption
Tests for Two Proportions
Chapter 200 Tests for Two Proportions Introduction This module computes power and sample size for hypothesis tests of the difference, ratio, or odds ratio of two independent proportions. The test statistics
Lin s Concordance Correlation Coefficient
NSS Statistical Software NSS.com hapter 30 Lin s oncordance orrelation oefficient Introduction This procedure calculates Lin s concordance correlation coefficient ( ) from a set of bivariate data. The
Tests for One Proportion
Chapter 100 Tests for One Proportion Introduction The One-Sample Proportion Test is used to assess whether a population proportion (P1) is significantly different from a hypothesized value (P0). This is
Tests for Two Survival Curves Using Cox s Proportional Hazards Model
Chapter 730 Tests for Two Survival Curves Using Cox s Proportional Hazards Model Introduction A clinical trial is often employed to test the equality of survival distributions of two treatment groups.
Two Correlated Proportions (McNemar Test)
Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with
Scatter Plots with Error Bars
Chapter 165 Scatter Plots with Error Bars Introduction The procedure extends the capability of the basic scatter plot by allowing you to plot the variability in Y and X corresponding to each point. Each
Non-Inferiority Tests for One Mean
Chapter 45 Non-Inferiority ests for One Mean Introduction his module computes power and sample size for non-inferiority tests in one-sample designs in which the outcome is distributed as a normal random
Data Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
Non-Inferiority Tests for Two Means using Differences
Chapter 450 on-inferiority Tests for Two Means using Differences Introduction This procedure computes power and sample size for non-inferiority tests in two-sample designs in which the outcome is a continuous
12.5: CHI-SQUARE GOODNESS OF FIT TESTS
125: Chi-Square Goodness of Fit Tests CD12-1 125: CHI-SQUARE GOODNESS OF FIT TESTS In this section, the χ 2 distribution is used for testing the goodness of fit of a set of data to a specific probability
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.
Standard Deviation Estimator
CSS.com Chapter 905 Standard Deviation Estimator Introduction Even though it is not of primary interest, an estimate of the standard deviation (SD) is needed when calculating the power or sample size of
NCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the
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
LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING
LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.
3.4 Statistical inference for 2 populations based on two samples
3.4 Statistical inference for 2 populations based on two samples Tests for a difference between two population means The first sample will be denoted as X 1, X 2,..., X m. The second sample will be denoted
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
STRUTS: Statistical Rules of Thumb. Seattle, WA
STRUTS: Statistical Rules of Thumb Gerald van Belle Departments of Environmental Health and Biostatistics University ofwashington Seattle, WA 98195-4691 Steven P. Millard Probability, Statistics and Information
NCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the
Using DOE with Tolerance Intervals to Verify Specifications
A Special Issue Article featuring a paper presented at 11 th Annual ENBIS Conference Using DOE with Tolerance Intervals to Verify Specifications By Patrick J. Whitcomb and Mark J. Anderson (mark@statease.com)
5.4 Solving Percent Problems Using the Percent Equation
5. Solving Percent Problems Using the Percent Equation In this section we will develop and use a more algebraic equation approach to solving percent equations. Recall the percent proportion from the last
Non-Inferiority Tests for Two Proportions
Chapter 0 Non-Inferiority Tests for Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority and superiority tests in twosample designs in which
Randomized Block Analysis of Variance
Chapter 565 Randomized Block Analysis of Variance Introduction This module analyzes a randomized block analysis of variance with up to two treatment factors and their interaction. It provides tables of
The normal approximation to the binomial
The normal approximation to the binomial In order for a continuous distribution (like the normal) to be used to approximate a discrete one (like the binomial), a continuity correction should be used. There
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
Confidence Intervals for One Mean
Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a
1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
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
BA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394-398, 404-408, 410-420
BA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394-398, 404-408, 410-420 1. Which of the following will increase the value of the power in a statistical test
Three-Stage Phase II Clinical Trials
Chapter 130 Three-Stage Phase II Clinical Trials Introduction Phase II clinical trials determine whether a drug or regimen has sufficient activity against disease to warrant more extensive study and development.
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
Chapter 2. Hypothesis testing in one population
Chapter 2. Hypothesis testing in one population Contents Introduction, the null and alternative hypotheses Hypothesis testing process Type I and Type II errors, power Test statistic, level of significance
One-Way ANOVA using SPSS 11.0. SPSS ANOVA procedures found in the Compare Means analyses. Specifically, we demonstrate
1 One-Way ANOVA using SPSS 11.0 This section covers steps for testing the difference between three or more group means using the SPSS ANOVA procedures found in the Compare Means analyses. Specifically,
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
Sampling Strategies for Error Rate Estimation and Quality Control
Project Number: JPA0703 Sampling Strategies for Error Rate Estimation and Quality Control A Major Qualifying Project Report Submitted to the faculty of the Worcester Polytechnic Institute in partial fulfillment
2 Precision-based sample size calculations
Statistics: An introduction to sample size calculations Rosie Cornish. 2006. 1 Introduction One crucial aspect of study design is deciding how big your sample should be. If you increase your sample size
Chapter 2 Probability Topics SPSS T tests
Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the One-Sample T test has been explained. In this handout, we also give the SPSS methods to perform
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
Means, standard deviations and. and standard errors
CHAPTER 4 Means, standard deviations and standard errors 4.1 Introduction Change of units 4.2 Mean, median and mode Coefficient of variation 4.3 Measures of variation 4.4 Calculating the mean and standard
Chapter 7 Notes - Inference for Single Samples. You know already for a large sample, you can invoke the CLT so:
Chapter 7 Notes - Inference for Single Samples You know already for a large sample, you can invoke the CLT so: X N(µ, ). Also for a large sample, you can replace an unknown σ by s. You know how to do a
MBA 611 STATISTICS AND QUANTITATIVE METHODS
MBA 611 STATISTICS AND QUANTITATIVE METHODS Part I. Review of Basic Statistics (Chapters 1-11) A. Introduction (Chapter 1) Uncertainty: Decisions are often based on incomplete information from uncertain
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 =
THE FIRST SET OF EXAMPLES USE SUMMARY DATA... EXAMPLE 7.2, PAGE 227 DESCRIBES A PROBLEM AND A HYPOTHESIS TEST IS PERFORMED IN EXAMPLE 7.
THERE ARE TWO WAYS TO DO HYPOTHESIS TESTING WITH STATCRUNCH: WITH SUMMARY DATA (AS IN EXAMPLE 7.17, PAGE 236, IN ROSNER); WITH THE ORIGINAL DATA (AS IN EXAMPLE 8.5, PAGE 301 IN ROSNER THAT USES DATA FROM
4. Continuous Random Variables, the Pareto and Normal Distributions
4. Continuous Random Variables, the Pareto and Normal Distributions A continuous random variable X can take any value in a given range (e.g. height, weight, age). The distribution of a continuous random
t Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon
t-tests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com www.excelmasterseries.com
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
KSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management
KSTAT MINI-MANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To
Gage Studies for Continuous Data
1 Gage Studies for Continuous Data Objectives Determine the adequacy of measurement systems. Calculate statistics to assess the linearity and bias of a measurement system. 1-1 Contents Contents Examples
1. Go to your programs menu and click on Microsoft Excel.
Elementary Statistics Computer Assignment 1 Using Microsoft EXCEL 2003, follow the steps below. For Microsoft EXCEL 2007 instructions, go to the next page. For Microsoft 2010 and 2007 instructions with
Using Stata for One Sample Tests
Using Stata for One Sample Tests All of the one sample problems we have discussed so far can be solved in Stata via either (a) statistical calculator functions, where you provide Stata with the necessary
Tutorial 5: Hypothesis Testing
Tutorial 5: Hypothesis Testing Rob Nicholls nicholls@mrc-lmb.cam.ac.uk MRC LMB Statistics Course 2014 Contents 1 Introduction................................ 1 2 Testing distributional assumptions....................
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
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
Odds ratio, Odds ratio test for independence, chi-squared statistic.
Odds ratio, Odds ratio test for independence, chi-squared statistic. Announcements: Assignment 5 is live on webpage. Due Wed Aug 1 at 4:30pm. (9 days, 1 hour, 58.5 minutes ) Final exam is Aug 9. Review
Independent t- Test (Comparing Two Means)
Independent t- Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent t-test when to use the independent t-test the use of SPSS to complete an independent
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
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
Part 2: Analysis of Relationship Between Two Variables
Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable
The normal approximation to the binomial
The normal approximation to the binomial The binomial probability function is not useful for calculating probabilities when the number of trials n is large, as it involves multiplying a potentially very
NCSS Statistical Software. One-Sample T-Test
Chapter 205 Introduction This procedure provides several reports for making inference about a population mean based on a single sample. These reports include confidence intervals of the mean or median,
Accounts Payable Expense Distribution Tables
Accounts Payable Expense Distribution Tables Use Expense Distribution Table Maintenance to set up tables with general ledger accounts and distribution percentages. The tables can then be selected in Invoice
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
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
Measuring Line Edge Roughness: Fluctuations in Uncertainty
Tutor6.doc: Version 5/6/08 T h e L i t h o g r a p h y E x p e r t (August 008) Measuring Line Edge Roughness: Fluctuations in Uncertainty Line edge roughness () is the deviation of a feature edge (as
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
CHAPTER THREE COMMON DESCRIPTIVE STATISTICS COMMON DESCRIPTIVE STATISTICS / 13
COMMON DESCRIPTIVE STATISTICS / 13 CHAPTER THREE COMMON DESCRIPTIVE STATISTICS The analysis of data begins with descriptive statistics such as the mean, median, mode, range, standard deviation, variance,
Chapter 6 INTERVALS Statement. Chapter Table of Contents
Chapter 6 INTERVALS Statement Chapter Table of Contents OVERVIEW...217 GETTING STARTED...218 ComputingStatisticalIntervals...218 ComputingOne-SidedLowerPredictionLimits...220 SYNTAX...222 SummaryofOptions...222
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
WEB APPENDIX. Calculating Beta Coefficients. b Beta Rise Run Y 7.1 1 8.92 X 10.0 0.0 16.0 10.0 1.6
WEB APPENDIX 8A Calculating Beta Coefficients The CAPM is an ex ante model, which means that all of the variables represent before-thefact, expected values. In particular, the beta coefficient used in
Learning by Doing: Portfolio Management Using the Bloomberg Professional Service. David S. Allen. Associate Professor of Finance
Learning by Doing: Portfolio Management Using the Bloomberg Professional Service David S. Allen Associate Professor of Finance The W. A. Franke College of Business Northern Arizona University P.O. Box
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
Premaster Statistics Tutorial 4 Full solutions
Premaster Statistics Tutorial 4 Full solutions Regression analysis Q1 (based on Doane & Seward, 4/E, 12.7) a. Interpret the slope of the fitted regression = 125,000 + 150. b. What is the prediction for
Stepwise Regression. Chapter 311. Introduction. Variable Selection Procedures. Forward (Step-Up) Selection
Chapter 311 Introduction Often, theory and experience give only general direction as to which of a pool of candidate variables (including transformed variables) should be included in the regression model.
Practice problems for Homework 12 - confidence intervals and hypothesis testing. Open the Homework Assignment 12 and solve the problems.
Practice problems for Homework 1 - confidence intervals and hypothesis testing. Read sections 10..3 and 10.3 of the text. Solve the practice problems below. Open the Homework Assignment 1 and solve the
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
HYPOTHESIS TESTING: POWER OF THE TEST
HYPOTHESIS TESTING: POWER OF THE TEST The first 6 steps of the 9-step test of hypothesis are called "the test". These steps are not dependent on the observed data values. When planning a research project,
Numerical Methods for Option Pricing
Chapter 9 Numerical Methods for Option Pricing Equation (8.26) provides a way to evaluate option prices. For some simple options, such as the European call and put options, one can integrate (8.26) directly
CONTINGENCY TABLES ARE NOT ALL THE SAME David C. Howell University of Vermont
CONTINGENCY TABLES ARE NOT ALL THE SAME David C. Howell University of Vermont To most people studying statistics a contingency table is a contingency table. We tend to forget, if we ever knew, that contingency
Variables Control Charts
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. Variables
Experiment #11: LRC Circuit (Power Amplifier, Voltage Sensor)
Experiment #11: LRC Circuit (Power Amplifier, Voltage Sensor) Concept: circuits Time: 30 m SW Interface: 750 Windows file: RLC.SWS EQUIPMENT NEEDED Science Workshop Interface Power Amplifier (2) Voltage
CHI-SQUARE: TESTING FOR GOODNESS OF FIT
CHI-SQUARE: TESTING FOR GOODNESS OF FIT In the previous chapter we discussed procedures for fitting a hypothesized function to a set of experimental data points. Such procedures involve minimizing a quantity
Chapter 23. Inferences for Regression
Chapter 23. Inferences for Regression Topics covered in this chapter: Simple Linear Regression Simple Linear Regression Example 23.1: Crying and IQ The Problem: Infants who cry easily may be more easily
Computational Statistics and Data Analysis
Computational Statistics and Data Analysis 53 (2008) 17 26 Contents lists available at ScienceDirect Computational Statistics and Data Analysis journal homepage: www.elsevier.com/locate/csda Coverage probability
Gamma Distribution Fitting
Chapter 552 Gamma Distribution Fitting Introduction This module fits the gamma probability distributions to a complete or censored set of individual or grouped data values. It outputs various statistics
Calculating P-Values. Parkland College. Isela Guerra Parkland College. Recommended Citation
Parkland College A with Honors Projects Honors Program 2014 Calculating P-Values Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating P-Values" (2014). A with Honors Projects.
Simple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
Learning by Doing: Portfolio Management Using the Bloomberg Professional Service
Learning by Doing: Portfolio Management Using the Bloomberg Professional Service David S. Allen Associate Professor of Finance The W. A. Franke College of Business Northern Arizona University P.O. Box
Binary Diagnostic Tests Two Independent Samples
Chapter 537 Binary Diagnostic Tests Two Independent Samples Introduction An important task in diagnostic medicine is to measure the accuracy of two diagnostic tests. This can be done by comparing summary
Journal of Statistical Software
JSS Journal of Statistical Software June 2009, Volume 30, Issue 13. http://www.jstatsoft.org/ An Excel Add-In for Statistical Process Control Charts Samuel E. Buttrey Naval Postgraduate School Abstract
Working with Spreadsheets
osborne books Working with Spreadsheets UPDATE SUPPLEMENT 2015 The AAT has recently updated its Study and Assessment Guide for the Spreadsheet Software Unit with some minor additions and clarifications.