Collaborative Statistics: Symbols and their Meanings

Size: px
Start display at page:

Download "Collaborative Statistics: Symbols and their Meanings"

Transcription

1 OpenStax-CNX module: m Collaborative Statistics: Symbols and their Meanings Susan Dean Barbara Illowsky, Ph.D. This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 2.0 Abstract This module denes symbols used throughout the Collaborative Statistics textbook. Symbols and their Meanings Chapter (1st used) Symbol Spoken Meaning Sampling and Data The square root of Sampling and Data π Pi (a specic number) Descriptive Statistics Q1 Quartile one the rst quartile Descriptive Statistics Q2 Quartile two the second quartile Descriptive Statistics Q3 Quartile three the third quartile Descriptive Statistics IQR inter-quartile range Q3-Q1=IQR Descriptive Statistics x x-bar sample mean Descriptive Statistics µ mu population mean Descriptive Statistics s s x sx s sample standard deviation Version 1.9: Mar 30, :24 pm

2 OpenStax-CNX module: m Descriptive Statistics s 2 s 2 x s-squared sample variance Descriptive Statistics σ σ x σx sigma population standard deviation Descriptive Statistics σ 2 σ 2 x sigma-squared population variance Descriptive Statistics Σ capital sigma sum Probability Topics {} brackets set notation Probability Topics S S sample space Probability Topics A Event A event A Probability Topics P (A) probability of A probability of A occurring Probability Topics P (A B) probability of A given B prob. of A occurring given B has occurred Probability Topics P (AorB) prob. of A or B prob. of A or B or both occurring Probability Topics P (AandB) prob. of A and B prob. of both A and B occurring ( time) Probability Topics A' A-prime, complement of A Probability Topics P (A') prob. of complement of A complement of A, not A Probability Topics G 1 green on rst pick Probability Topics P (G 1 ) prob. of green on rst pick PDF prob. distribution function X X the random variable X X the distribution of X B binomial distribution G geometric distribution

3 OpenStax-CNX module: m H hypergeometric dist. P Poisson dist. λ Lambda average of Poisson distribution greater than or equal to less than or equal to = equal to not equal to f (x) f of x function of x pdf prob. density function U uniform distribution Exp exponential distribution k k critical value f (x) = f of x equals m m decay rate (for exp. dist.) N normal distribution z z-score Z standard normal dist.

4 OpenStax-CNX module: m CLT Central Limit Theorem X X-bar the random variable X- bar µ x mean of X the average of X µ x mean of X-bar the average of X-bar σ x standard deviation of X σ x standard deviation of X- bar ΣX sum of X Σx sum of x Condence Intervals CL condence level Condence Intervals CI condence interval Condence Intervals EBM error bound for a mean Condence Intervals EBP error bound for a proportion Condence Intervals t student-t distribution Condence Intervals df degrees of freedom Condence Intervals t α 2 student-t with a/2 area in right tail Condence Intervals p' ^p p-prime; p-hat sample proportion of success Condence Intervals q' ^q q-prime; q-hat sample proportion of failure Hypothesis Testing H 0 H-naught, H-sub 0 null hypothesis Hypothesis Testing H a H-a, H-sub a alternate hypothesis Hypothesis Testing H 1 H-1, H-sub 1 alternate hypothesis

5 OpenStax-CNX module: m Hypothesis Testing α alpha probability of Type I error Hypothesis Testing β beta probability of Type II error Hypothesis Testing X1 X2 X1-bar minus X2-bar dierence in sample means µ 1 µ 2 mu-1 minus mu-2 dierence in population means P ' 1 P ' 2 P1-prime minus P2- prime dierence in sample proportions p 1 p 2 p1 minus p2 dierence in population proportions Chi-Square Distribution X 2 Ky-square Chi-square Linear Regression and Correlation O Observed Observed frequency E Expected Expected frequency y = a + bx y equals a plus b-x equation of a line ^y y-hat estimated value of y F-Distribution ANOVA and r correlation coecient ɛ error SSE Sum of Squared Errors 1.9s 1.9 times s cut-o value for outliers F F-ratio F ratio Table 1

Normal Distribution: Calculations of Probabilities

Normal Distribution: Calculations of Probabilities OpenStax-CNX module: m46212 1 Normal Distribution: Calculations of Probabilities Irene Mary Duranczyk Suzanne Loch Janet Stottlemyer Based on Normal Distribution: Calculations of Probabilities by Susan

More information

" M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2

 M A #M B. Standard deviation of the population (Greek lowercase letter sigma) σ 2 Notation and Equations for Final Exam Symbol Definition X The variable we measure in a scientific study n The size of the sample N The size of the population M The mean of the sample µ The mean of the

More information

FRANKLIN UNIVERSITY PROFICIENCY EXAM (FUPE) STUDY GUIDE

FRANKLIN UNIVERSITY PROFICIENCY EXAM (FUPE) STUDY GUIDE FRANKLIN UNIVERSITY PROFICIENCY EXAM (FUPE) STUDY GUIDE Course Title: Probability and Statistics (MATH 80) Recommended Textbook(s): Number & Type of Questions: Probability and Statistics for Engineers

More information

Probability Methods in Civil Engineering Prof. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur

Probability Methods in Civil Engineering Prof. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur Probability Methods in Civil Engineering Prof. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur Lecture No. # 12 Probability Distribution of Continuous RVs (Contd.)

More information

IT 403 Statistics and Data Analysis Final Review Guide

IT 403 Statistics and Data Analysis Final Review Guide IT 403 Statistics and Data Analysis Final Review Guide Exam Schedule and Format Date: 11/15 (Wed) for Section 702 (Loop); between 11/15 (Wed) and 11/18 (Sat) for Section 711 (Online). Location: CDM 224

More information

Statistics Handbook. All statistical tables were computed by the author.

Statistics Handbook. All statistical tables were computed by the author. Statistics Handbook Contents Page Wilcoxon rank-sum test (Mann-Whitney equivalent) Wilcoxon matched-pairs test 3 Normal Distribution 4 Z-test Related samples t-test 5 Unrelated samples t-test 6 Variance

More information

Continuous random variables

Continuous random variables Continuous random variables Can take on an uncountably infinite number of values Any value within an interval over which the variable is definied has some probability of occuring This is different from

More information

Data Science for Engineers Department of Computer Science and Engineering Indian Institute of Technology, Madras

Data Science for Engineers Department of Computer Science and Engineering Indian Institute of Technology, Madras Data Science for Engineers Department of Computer Science and Engineering Indian Institute of Technology, Madras Lecture 36 Simple Linear Regression Model Assessment So, welcome to the second lecture on

More information

Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology Kharagpur

Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology Kharagpur Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology Kharagpur Lecture No. #13 Probability Distribution of Continuous RVs (Contd

More information

AMS7: WEEK 7. CLASS 1. More on Hypothesis Testing Monday May 11th, 2015

AMS7: WEEK 7. CLASS 1. More on Hypothesis Testing Monday May 11th, 2015 AMS7: WEEK 7. CLASS 1 More on Hypothesis Testing Monday May 11th, 2015 Testing a Claim about a Standard Deviation or a Variance We want to test claims about or 2 Example: Newborn babies from mothers taking

More information

PASS Sample Size Software. Poisson Regression

PASS Sample Size Software. Poisson Regression Chapter 870 Introduction Poisson regression is used when the dependent variable is a count. Following the results of Signorini (99), this procedure calculates power and sample size for testing the hypothesis

More information

Algebraic Expressions and Equations: Classification of Expressions and Equations *

Algebraic Expressions and Equations: Classification of Expressions and Equations * OpenStax-CNX module: m21848 1 Algebraic Expressions and Equations: Classification of Expressions and Equations * Wade Ellis Denny Burzynski This work is produced by OpenStax-CNX and licensed under the

More information

Statistical Inference

Statistical Inference Statistical Inference Bernhard Klingenberg Institute of Statistics Graz University of Technology Steyrergasse 17/IV, 8010 Graz www.statistics.tugraz.at February 12, 2008 Outline Estimation: Review of concepts

More information

Factorising Cubic Polynomials - Grade 12 *

Factorising Cubic Polynomials - Grade 12 * OpenStax-CNX module: m32660 1 Factorising Cubic Polynomials - Grade 12 * Rory Adams Free High School Science Texts Project Sarah Blyth Heather Williams This work is produced by OpenStax-CNX and licensed

More information

Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z).

Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z). Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z). For example P(X 1.04) =.8508. For z < 0 subtract the value from

More information

Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z).

Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z). Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z). For example P(X.04) =.8508. For z < 0 subtract the value from,

More information

GROUPED DATA E.G. FOR SAMPLE OF RAW DATA (E.G. 4, 12, 7, 5, MEAN G x / n STANDARD DEVIATION MEDIAN AND QUARTILES STANDARD DEVIATION

GROUPED DATA E.G. FOR SAMPLE OF RAW DATA (E.G. 4, 12, 7, 5, MEAN G x / n STANDARD DEVIATION MEDIAN AND QUARTILES STANDARD DEVIATION FOR SAMPLE OF RAW DATA (E.G. 4, 1, 7, 5, 11, 6, 9, 7, 11, 5, 4, 7) BE ABLE TO COMPUTE MEAN G / STANDARD DEVIATION MEDIAN AND QUARTILES Σ ( Σ) / 1 GROUPED DATA E.G. AGE FREQ. 0-9 53 10-19 4...... 80-89

More information

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics Exploring Data: Distributions Look for overall pattern (shape, center, spread) and deviations (outliers). Mean (use a calculator): x = x 1 + x

More information

Review of Statistics 101

Review of Statistics 101 Review of Statistics 101 We review some important themes from the course 1. Introduction Statistics- Set of methods for collecting/analyzing data (the art and science of learning from data). Provides methods

More information

Review for the previous lecture

Review for the previous lecture Lecture 1 and 13 on BST 631: Statistical Theory I Kui Zhang, 09/8/006 Review for the previous lecture Definition: Several discrete distributions, including discrete uniform, hypergeometric, Bernoulli,

More information

Statistical Concepts. Distributions of Data

Statistical Concepts. Distributions of Data Module : Review of Basic Statistical Concepts. Understanding Probability Distributions, Parameters and Statistics A variable that can take on any value in a range is called a continuous variable. Example:

More information

Tables Table A Table B Table C Table D Table E 675

Tables Table A Table B Table C Table D Table E 675 BMTables.indd Page 675 11/15/11 4:25:16 PM user-s163 Tables Table A Standard Normal Probabilities Table B Random Digits Table C t Distribution Critical Values Table D Chi-square Distribution Critical Values

More information

Displacement * Albert Hall. Based on Displacement by OpenStax

Displacement * Albert Hall. Based on Displacement by OpenStax OpenStax-CNX module: m57711 1 Displacement * Albert Hall Based on Displacement by OpenStax This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 4.0 Abstract

More information

Algebraic Expressions and Equations: Solving Equations of the Form x+a=b and x-a=b

Algebraic Expressions and Equations: Solving Equations of the Form x+a=b and x-a=b OpenStax-CNX module: m35044 1 Algebraic Expressions and Equations: Solving Equations of the Form x+ab and x-ab Wade Ellis Denny Burzynski work is produced by OpenStax-CNX and licensed under the Creative

More information

Ch. 1: Data and Distributions

Ch. 1: Data and Distributions Ch. 1: Data and Distributions Populations vs. Samples How to graphically display data Histograms, dot plots, stem plots, etc Helps to show how samples are distributed Distributions of both continuous and

More information

Chapter 3 Common Families of Distributions

Chapter 3 Common Families of Distributions Lecture 9 on BST 631: Statistical Theory I Kui Zhang, 9/3/8 and 9/5/8 Review for the previous lecture Definition: Several commonly used discrete distributions, including discrete uniform, hypergeometric,

More information

Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institution of Technology, Kharagpur

Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institution of Technology, Kharagpur Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institution of Technology, Kharagpur Lecture No. # 36 Sampling Distribution and Parameter Estimation

More information

CHAPTER 10 HYPOTHESIS TESTING WITH TWO SAMPLES

CHAPTER 10 HYPOTHESIS TESTING WITH TWO SAMPLES CHAPTER 10 HYPOTHESIS TESTING WITH TWO SAMPLES In this chapter our hypothesis tests allow us to compare the means (or proportions) of two different populations using a sample from each population For example,

More information

Lecture 03 Positive Semidefinite (PSD) and Positive Definite (PD) Matrices and their Properties

Lecture 03 Positive Semidefinite (PSD) and Positive Definite (PD) Matrices and their Properties Applied Optimization for Wireless, Machine Learning, Big Data Prof. Aditya K. Jagannatham Department of Electrical Engineering Indian Institute of Technology, Kanpur Lecture 03 Positive Semidefinite (PSD)

More information

Binomial Distribution *

Binomial Distribution * OpenStax-CNX module: m11024 1 Binomial Distribution * David Lane This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 When you ip a coin, there are two

More information

Domain and range of exponential and logarithmic function *

Domain and range of exponential and logarithmic function * OpenStax-CNX module: m15461 1 Domain and range of exponential and logarithmic function * Sunil Kumar Singh This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License

More information

OHSU OGI Class ECE-580-DOE :Statistical Process Control and Design of Experiments Steve Brainerd Basic Statistics Sample size?

OHSU OGI Class ECE-580-DOE :Statistical Process Control and Design of Experiments Steve Brainerd Basic Statistics Sample size? ECE-580-DOE :Statistical Process Control and Design of Experiments Steve Basic Statistics Sample size? Sample size determination: text section 2-4-2 Page 41 section 3-7 Page 107 Website::http://www.stat.uiowa.edu/~rlenth/Power/

More information

Summary of Chapter 7 (Sections ) and Chapter 8 (Section 8.1)

Summary of Chapter 7 (Sections ) and Chapter 8 (Section 8.1) Summary of Chapter 7 (Sections 7.2-7.5) and Chapter 8 (Section 8.1) Chapter 7. Tests of Statistical Hypotheses 7.2. Tests about One Mean (1) Test about One Mean Case 1: σ is known. Assume that X N(µ, σ

More information

Dover- Sherborn High School Mathematics Curriculum Probability and Statistics

Dover- Sherborn High School Mathematics Curriculum Probability and Statistics Mathematics Curriculum A. DESCRIPTION This is a full year courses designed to introduce students to the basic elements of statistics and probability. Emphasis is placed on understanding terminology and

More information

N Utilization of Nursing Research in Advanced Practice, Summer 2008

N Utilization of Nursing Research in Advanced Practice, Summer 2008 University of Michigan Deep Blue deepblue.lib.umich.edu 2008-07 536 - Utilization of ursing Research in Advanced Practice, Summer 2008 Tzeng, Huey-Ming Tzeng, H. (2008, ctober 1). Utilization of ursing

More information

Review for Final. Chapter 1 Type of studies: anecdotal, observational, experimental Random sampling

Review for Final. Chapter 1 Type of studies: anecdotal, observational, experimental Random sampling Review for Final For a detailed review of Chapters 1 7, please see the review sheets for exam 1 and. The following only briefly covers these sections. The final exam could contain problems that are included

More information

Ch. 7: Estimates and Sample Sizes

Ch. 7: Estimates and Sample Sizes Ch. 7: Estimates and Sample Sizes Section Title Notes Pages Introduction to the Chapter 2 2 Estimating p in the Binomial Distribution 2 5 3 Estimating a Population Mean: Sigma Known 6 9 4 Estimating a

More information

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages:

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages: Glossary The ISI glossary of statistical terms provides definitions in a number of different languages: http://isi.cbs.nl/glossary/index.htm Adjusted r 2 Adjusted R squared measures the proportion of the

More information

Confidence Intervals, Testing and ANOVA Summary

Confidence Intervals, Testing and ANOVA Summary Confidence Intervals, Testing and ANOVA Summary 1 One Sample Tests 1.1 One Sample z test: Mean (σ known) Let X 1,, X n a r.s. from N(µ, σ) or n > 30. Let The test statistic is H 0 : µ = µ 0. z = x µ 0

More information

Practice Final Exam. December 14, 2009

Practice Final Exam. December 14, 2009 Practice Final Exam December 14, 29 1 New Material 1.1 ANOVA 1. A purication process for a chemical involves passing it, in solution, through a resin on which impurities are adsorbed. A chemical engineer

More information

Introduction to Business Statistics QM 220 Chapter 12

Introduction to Business Statistics QM 220 Chapter 12 Department of Quantitative Methods & Information Systems Introduction to Business Statistics QM 220 Chapter 12 Dr. Mohammad Zainal 12.1 The F distribution We already covered this topic in Ch. 10 QM-220,

More information

Probability Distributions Columns (a) through (d)

Probability Distributions Columns (a) through (d) Discrete Probability Distributions Columns (a) through (d) Probability Mass Distribution Description Notes Notation or Density Function --------------------(PMF or PDF)-------------------- (a) (b) (c)

More information

Formulas and Tables by Mario F. Triola

Formulas and Tables by Mario F. Triola Copyright 010 Pearson Education, Inc. Ch. 3: Descriptive Statistics x f # x x f Mean 1x - x s - 1 n 1 x - 1 x s 1n - 1 s B variance s Ch. 4: Probability Mean (frequency table) Standard deviation P1A or

More information

Can you tell the relationship between students SAT scores and their college grades?

Can you tell the relationship between students SAT scores and their college grades? Correlation One Challenge Can you tell the relationship between students SAT scores and their college grades? A: The higher SAT scores are, the better GPA may be. B: The higher SAT scores are, the lower

More information

Course ID May 2017 COURSE OUTLINE. Mathematics 130 Elementary & Intermediate Algebra for Statistics

Course ID May 2017 COURSE OUTLINE. Mathematics 130 Elementary & Intermediate Algebra for Statistics Non-Degree Applicable Glendale Community College Course ID 010238 May 2017 Catalog Statement COURSE OUTLINE Mathematics 130 Elementary & Intermediate Algebra for Statistics is a one-semester accelerated

More information

Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur

Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur Lecture No. # 33 Probability Models using Gamma and Extreme Value

More information

Applied Multivariate Statistical Modeling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur

Applied Multivariate Statistical Modeling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Applied Multivariate Statistical Modeling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 29 Multivariate Linear Regression- Model

More information

Mathematical Notation Math Introduction to Applied Statistics

Mathematical Notation Math Introduction to Applied Statistics Mathematical Notation Math 113 - Introduction to Applied Statistics Name : Use Word or WordPerfect to recreate the following documents. Each article is worth 10 points and should be emailed to the instructor

More information

Section 9.4. Notation. Requirements. Definition. Inferences About Two Means (Matched Pairs) Examples

Section 9.4. Notation. Requirements. Definition. Inferences About Two Means (Matched Pairs) Examples Objective Section 9.4 Inferences About Two Means (Matched Pairs) Compare of two matched-paired means using two samples from each population. Hypothesis Tests and Confidence Intervals of two dependent means

More information

Study Guide #3: OneWay ANALYSIS OF VARIANCE (ANOVA)

Study Guide #3: OneWay ANALYSIS OF VARIANCE (ANOVA) Study Guide #3: OneWay ANALYSIS OF VARIANCE (ANOVA) About the ANOVA Test In educational research, we are most often involved finding out whether there are differences between groups. For example, is there

More information

ECN221 Exam 1 VERSION B Fall 2017 (Modules 1-4), ASU-COX VERSION B

ECN221 Exam 1 VERSION B Fall 2017 (Modules 1-4), ASU-COX VERSION B ECN221 Exam 1 VERSION B Fall 2017 (Modules 1-4), ASU-COX VERSION B Choose the best answer. Do not write letters in the margin or communicate with other students in any way; if you do you will receive a

More information

CIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8

CIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8 CIVL - 7904/8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8 Chi-square Test How to determine the interval from a continuous distribution I = Range 1 + 3.322(logN) I-> Range of the class interval

More information

The Chi-Square Distributions

The Chi-Square Distributions MATH 03 The Chi-Square Distributions Dr. Neal, Spring 009 The chi-square distributions can be used in statistics to analyze the standard deviation of a normally distributed measurement and to test the

More information

Formulas and Tables. for Essentials of Statistics, by Mario F. Triola 2002 by Addison-Wesley. ˆp E p ˆp E Proportion.

Formulas and Tables. for Essentials of Statistics, by Mario F. Triola 2002 by Addison-Wesley. ˆp E p ˆp E Proportion. Formulas and Tables for Essentials of Statistics, by Mario F. Triola 2002 by Addison-Wesley. Ch. 2: Descriptive Statistics x Sf. x x Sf Mean S(x 2 x) 2 s Å n 2 1 n(sx 2 ) 2 (Sx) 2 s Å n(n 2 1) Mean (frequency

More information

Increasing and decreasing intervals *

Increasing and decreasing intervals * OpenStax-CNX module: m15474 1 Increasing and decreasing intervals * Sunil Kumar Singh This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 2.0 A function is

More information

The Chi-Square Distributions

The Chi-Square Distributions MATH 183 The Chi-Square Distributions Dr. Neal, WKU The chi-square distributions can be used in statistics to analyze the standard deviation σ of a normally distributed measurement and to test the goodness

More information

Sociology 6Z03 Review II

Sociology 6Z03 Review II Sociology 6Z03 Review II John Fox McMaster University Fall 2016 John Fox (McMaster University) Sociology 6Z03 Review II Fall 2016 1 / 35 Outline: Review II Probability Part I Sampling Distributions Probability

More information

Chapter 1 Statistical Inference

Chapter 1 Statistical Inference Chapter 1 Statistical Inference causal inference To infer causality, you need a randomized experiment (or a huge observational study and lots of outside information). inference to populations Generalizations

More information

Statistical Modeling and Analysis of Scientific Inquiry: The Basics of Hypothesis Testing

Statistical Modeling and Analysis of Scientific Inquiry: The Basics of Hypothesis Testing Statistical Modeling and Analysis of Scientific Inquiry: The Basics of Hypothesis Testing So, What is Statistics? Theory and techniques for learning from data How to collect How to analyze How to interpret

More information

Gravitational potential energy *

Gravitational potential energy * OpenStax-CNX module: m15090 1 Gravitational potential energy * Sunil Kumar Singh This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 2.0 The concept of potential

More information

Lecture 3: Inference in SLR

Lecture 3: Inference in SLR Lecture 3: Inference in SLR STAT 51 Spring 011 Background Reading KNNL:.1.6 3-1 Topic Overview This topic will cover: Review of hypothesis testing Inference about 1 Inference about 0 Confidence Intervals

More information

ST430 Exam 2 Solutions

ST430 Exam 2 Solutions ST430 Exam 2 Solutions Date: November 9, 2015 Name: Guideline: You may use one-page (front and back of a standard A4 paper) of notes. No laptop or textbook are permitted but you may use a calculator. Giving

More information

TABLE OF CONTENTS CHAPTER 1 COMBINATORIAL PROBABILITY 1

TABLE OF CONTENTS CHAPTER 1 COMBINATORIAL PROBABILITY 1 TABLE OF CONTENTS CHAPTER 1 COMBINATORIAL PROBABILITY 1 1.1 The Probability Model...1 1.2 Finite Discrete Models with Equally Likely Outcomes...5 1.2.1 Tree Diagrams...6 1.2.2 The Multiplication Principle...8

More information

EXAM IN TMA4255 EXPERIMENTAL DESIGN AND APPLIED STATISTICAL METHODS

EXAM IN TMA4255 EXPERIMENTAL DESIGN AND APPLIED STATISTICAL METHODS Norges teknisk naturvitenskapelige universitet Institutt for matematiske fag Side 1 av 8 Contact during exam: Bo Lindqvist Tel. 975 89 418 EXAM IN TMA4255 EXPERIMENTAL DESIGN AND APPLIED STATISTICAL METHODS

More information

Topic 2: Probability & Distributions. Road Map Probability & Distributions. ECO220Y5Y: Quantitative Methods in Economics. Dr.

Topic 2: Probability & Distributions. Road Map Probability & Distributions. ECO220Y5Y: Quantitative Methods in Economics. Dr. Topic 2: Probability & Distributions ECO220Y5Y: Quantitative Methods in Economics Dr. Nick Zammit University of Toronto Department of Economics Room KN3272 n.zammit utoronto.ca November 21, 2017 Dr. Nick

More information

Sampling distribution of t. 2. Sampling distribution of t. 3. Example: Gas mileage investigation. II. Inferential Statistics (8) t =

Sampling distribution of t. 2. Sampling distribution of t. 3. Example: Gas mileage investigation. II. Inferential Statistics (8) t = 2. The distribution of t values that would be obtained if a value of t were calculated for each sample mean for all possible random of a given size from a population _ t ratio: (X - µ hyp ) t s x The result

More information

Chapter 7 Comparison of two independent samples

Chapter 7 Comparison of two independent samples Chapter 7 Comparison of two independent samples 7.1 Introduction Population 1 µ σ 1 1 N 1 Sample 1 y s 1 1 n 1 Population µ σ N Sample y s n 1, : population means 1, : population standard deviations N

More information

Displacement. OpenStax College. This work is produced by The Connexions Project and licensed under the Creative Commons Attribution License

Displacement. OpenStax College. This work is produced by The Connexions Project and licensed under the Creative Commons Attribution License Connexions module: m42033 1 Displacement OpenStax College This work is produced by The Connexions Project and licensed under the Creative Commons Attribution License Abstract Dene position, displacement,

More information

Section 4.6 Simple Linear Regression

Section 4.6 Simple Linear Regression Section 4.6 Simple Linear Regression Objectives ˆ Basic philosophy of SLR and the regression assumptions ˆ Point & interval estimation of the model parameters, and how to make predictions ˆ Point and interval

More information

PSY 307 Statistics for the Behavioral Sciences. Chapter 20 Tests for Ranked Data, Choosing Statistical Tests

PSY 307 Statistics for the Behavioral Sciences. Chapter 20 Tests for Ranked Data, Choosing Statistical Tests PSY 307 Statistics for the Behavioral Sciences Chapter 20 Tests for Ranked Data, Choosing Statistical Tests What To Do with Non-normal Distributions Tranformations (pg 382): The shape of the distribution

More information

Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed)

Area1 Scaled Score (NAPLEX) .535 ** **.000 N. Sig. (2-tailed) Institutional Assessment Report Texas Southern University College of Pharmacy and Health Sciences "An Analysis of 2013 NAPLEX, P4-Comp. Exams and P3 courses The following analysis illustrates relationships

More information

Basic Statistics. Resources. Statistical Tables Murdoch & Barnes. Scientific Calculator. Minitab 17.

Basic Statistics. Resources. Statistical Tables Murdoch & Barnes. Scientific Calculator. Minitab 17. Basic Statistics Resources 1160 Statistical Tables Murdoch & Barnes Scientific Calculator Minitab 17 http://www.mathsisfun.com/data/ 1 Statistics 1161 The science of collection, analysis, interpretation

More information

Learning Objectives for Stat 225

Learning Objectives for Stat 225 Learning Objectives for Stat 225 08/20/12 Introduction to Probability: Get some general ideas about probability, and learn how to use sample space to compute the probability of a specific event. Set Theory:

More information

Formulas and Tables. for Elementary Statistics, Tenth Edition, by Mario F. Triola Copyright 2006 Pearson Education, Inc. ˆp E p ˆp E Proportion

Formulas and Tables. for Elementary Statistics, Tenth Edition, by Mario F. Triola Copyright 2006 Pearson Education, Inc. ˆp E p ˆp E Proportion Formulas and Tables for Elementary Statistics, Tenth Edition, by Mario F. Triola Copyright 2006 Pearson Education, Inc. Ch. 3: Descriptive Statistics x Sf. x x Sf Mean S(x 2 x) 2 s Å n 2 1 n(sx 2 ) 2 (Sx)

More information

Inferences for Regression

Inferences for Regression Inferences for Regression An Example: Body Fat and Waist Size Looking at the relationship between % body fat and waist size (in inches). Here is a scatterplot of our data set: Remembering Regression In

More information

Formulas and Tables for Elementary Statistics, Eighth Edition, by Mario F. Triola 2001 by Addison Wesley Longman Publishing Company, Inc.

Formulas and Tables for Elementary Statistics, Eighth Edition, by Mario F. Triola 2001 by Addison Wesley Longman Publishing Company, Inc. Formulas and Tables for Elementary Statistics, Eighth Edition, by Mario F. Triola 2001 by Addison Wesley Longman Publishing Company, Inc. Ch. 2: Descriptive Statistics x Sf. x x Sf Mean S(x 2 x) 2 s 2

More information

Mark Scheme (Results) Summer 2009

Mark Scheme (Results) Summer 2009 Mark (Results) Summer 009 GCE GCE Mathematics (6684/01) June 009 6684 Statistics S Mark Q1 [ X ~ B(0,0.15) ] P(X 6), = 0.8474 awrt 0.847 Y ~ B(60,0.15) Po(9) for using Po(9) P(Y < 1), = 0.8758 awrt 0.876

More information

STA1000F Summary. Mitch Myburgh MYBMIT001 May 28, Work Unit 1: Introducing Probability

STA1000F Summary. Mitch Myburgh MYBMIT001 May 28, Work Unit 1: Introducing Probability STA1000F Summary Mitch Myburgh MYBMIT001 May 28, 2015 1 Module 1: Probability 1.1 Work Unit 1: Introducing Probability 1.1.1 Definitions 1. Random Experiment: A procedure whose outcome (result) in a particular

More information

Lecture 12: Small Sample Intervals Based on a Normal Population Distribution

Lecture 12: Small Sample Intervals Based on a Normal Population Distribution Lecture 12: Small Sample Intervals Based on a Normal Population MSU-STT-351-Sum-17B (P. Vellaisamy: MSU-STT-351-Sum-17B) Probability & Statistics for Engineers 1 / 24 In this lecture, we will discuss (i)

More information

Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual

Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual Mock Exam - 2 hours - use of basic (non-programmable) calculator is allowed - all exercises carry the same marks - exam is strictly individual Question 1. Suppose you want to estimate the percentage of

More information

Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur

Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur Probability Methods in Civil Engineering Prof. Dr. Rajib Maity Department of Civil Engineering Indian Institute of Technology, Kharagpur Lecture No. # 38 Goodness - of fit tests Hello and welcome to this

More information

GEOMETRIC -discrete A discrete random variable R counts number of times needed before an event occurs

GEOMETRIC -discrete A discrete random variable R counts number of times needed before an event occurs STATISTICS 4 Summary Notes. Geometric and Exponential Distributions GEOMETRIC -discrete A discrete random variable R counts number of times needed before an event occurs P(X = x) = ( p) x p x =,, 3,...

More information

10/4/2013. Hypothesis Testing & z-test. Hypothesis Testing. Hypothesis Testing

10/4/2013. Hypothesis Testing & z-test. Hypothesis Testing. Hypothesis Testing & z-test Lecture Set 11 We have a coin and are trying to determine if it is biased or unbiased What should we assume? Why? Flip coin n = 100 times E(Heads) = 50 Why? Assume we count 53 Heads... What could

More information

Survey of Smoking Behavior. Survey of Smoking Behavior. Survey of Smoking Behavior

Survey of Smoking Behavior. Survey of Smoking Behavior. Survey of Smoking Behavior Sample HH from Frame HH One-Stage Cluster Survey Population Frame Sample Elements N =, N =, n = population smokes Sample HH from Frame HH Elementary units are different from sampling units Sampled HH but

More information

Lecture 3. Biostatistics in Veterinary Science. Feb 2, Jung-Jin Lee Drexel University. Biostatistics in Veterinary Science Lecture 3

Lecture 3. Biostatistics in Veterinary Science. Feb 2, Jung-Jin Lee Drexel University. Biostatistics in Veterinary Science Lecture 3 Lecture 3 Biostatistics in Veterinary Science Jung-Jin Lee Drexel University Feb 2, 2015 Review Let S be the sample space and A, B be events. Then 1 P (S) = 1, P ( ) = 0. 2 If A B, then P (A) P (B). In

More information

Exam details. Final Review Session. Things to Review

Exam details. Final Review Session. Things to Review Exam details Final Review Session Short answer, similar to book problems Formulae and tables will be given You CAN use a calculator Date and Time: Dec. 7, 006, 1-1:30 pm Location: Osborne Centre, Unit

More information

The Use of R Language in the Teaching of Central Limit Theorem

The Use of R Language in the Teaching of Central Limit Theorem The Use of R Language in the Teaching of Central Limit Theorem Cheang Wai Kwong waikwong.cheang@nie.edu.sg National Institute of Education Nanyang Technological University Singapore Abstract: The Central

More information

Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z).

Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z). Table of z values and probabilities for the standard normal distribution. z is the first column plus the top row. Each cell shows P(X z). For example P(X 1.04) =.8508. For z < 0 subtract the value from

More information

STA 101 Final Review

STA 101 Final Review STA 101 Final Review Statistics 101 Thomas Leininger June 24, 2013 Announcements All work (besides projects) should be returned to you and should be entered on Sakai. Office Hour: 2 3pm today (Old Chem

More information

DETAILED CONTENTS PART I INTRODUCTION AND DESCRIPTIVE STATISTICS. 1. Introduction to Statistics

DETAILED CONTENTS PART I INTRODUCTION AND DESCRIPTIVE STATISTICS. 1. Introduction to Statistics DETAILED CONTENTS About the Author Preface to the Instructor To the Student How to Use SPSS With This Book PART I INTRODUCTION AND DESCRIPTIVE STATISTICS 1. Introduction to Statistics 1.1 Descriptive and

More information

Generalized Linear Models 1

Generalized Linear Models 1 Generalized Linear Models 1 STA 2101/442: Fall 2012 1 See last slide for copyright information. 1 / 24 Suggested Reading: Davison s Statistical models Exponential families of distributions Sec. 5.2 Chapter

More information

Time-Delay Estimation *

Time-Delay Estimation * OpenStax-CNX module: m1143 1 Time-Delay stimation * Don Johnson This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 1. An important signal parameter estimation

More information

1 Hypothesis testing for a single mean

1 Hypothesis testing for a single mean 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

More information

p(z)

p(z) Chapter Statistics. Introduction This lecture is a quick review of basic statistical concepts; probabilities, mean, variance, covariance, correlation, linear regression, probability density functions and

More information

One-Way Analysis of Variance (ANOVA) Paul K. Strode, Ph.D.

One-Way Analysis of Variance (ANOVA) Paul K. Strode, Ph.D. One-Way Analysis of Variance (ANOVA) Paul K. Strode, Ph.D. Purpose While the T-test is useful to compare the means of two samples, many biology experiments involve the parallel measurement of three or

More information

Business Statistics. Lecture 10: Course Review

Business Statistics. Lecture 10: Course Review Business Statistics Lecture 10: Course Review 1 Descriptive Statistics for Continuous Data Numerical Summaries Location: mean, median Spread or variability: variance, standard deviation, range, percentiles,

More information

Chapter 10: Analysis of variance (ANOVA)

Chapter 10: Analysis of variance (ANOVA) Chapter 10: Analysis of variance (ANOVA) ANOVA (Analysis of variance) is a collection of techniques for dealing with more general experiments than the previous one-sample or two-sample tests. We first

More information

Module 7 Practice problem and Homework answers

Module 7 Practice problem and Homework answers Module 7 Practice problem and Homework answers Practice problem, page 1 Is the research hypothesis one-tailed or two-tailed? Answer: one tailed In the set up for the problem, we predicted a specific outcome

More information

Comparing Measures of Central Tendency *

Comparing Measures of Central Tendency * OpenStax-CNX module: m11011 1 Comparing Measures of Central Tendency * David Lane This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 1.0 1 Comparing Measures

More information

Vector (cross) product *

Vector (cross) product * OpenStax-CNX module: m13603 1 Vector (cross) product * Sunil Kumar Singh This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 2.0 Abstract Vector multiplication

More information