Tutor/(or Student) Guide to: Tutor-led Tutorials

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Tutor/(or Student) Guide to: Tutor-led Tutorials"

Transcription

1 Tutor/(or Student) Guide to: Tutor-led Tutorials (Module Code: Stat10050) Tutor Name: Module Co-ordinator: Dr. Patrick Murphy

2 Description of Tutorials Introduction to Statistical Modelling Tutorials: Aim of the tutorials is for the students to get solutions to their homework and ask questions related to their homework; indeed they are like mini lectures. The solutions are worked through by the tutor on the board. The students get extra questions to do in the tutorial INDIVIDUALLY (not all tutors correct the homework corresponding the tutorials they give). The exam-like materials are covered in some tutorials (towards the end of the semester). The tutorials are set aside to help the students, especially those struggling with the course materials, to get more help. The tutors should be ready to answer students questions. In fact, the tutors should be a link between the lecturers and the students; which means that the students are free to approach the tutors (their fellow student), and the tutors are to communicate with the lecturers the feelings of the students. Module Descriptor Lecture Topic Topic Code 1, A Introduction and Motivation,3,4 B Statistical Concepts: Samples and Populations, Variability, Seven Critical Components of a Study, Bias in Questions and Bias in Sampling, Types of Variables and Data 4,5,6 C Collecting Data 1: Sampling and surveys 5,6 D Collecting Data : Experiments and Observational Studies Learning Goals The students will know the following: the difference between sample and population; spread in data set; how to word questions correctly in questionnaire; how to critically analyse a study conducted by other people, how to avoid mistakes in sampling; the different types of variables and data. The students will discover the different types of sampling. They will also know how to carry out surveys in a correct manner, and identify when there is misleading information from surveys in the newspapers. The students should identify the difference between observational studies and experimental studies, and the various different methods used in each of these.

3 7,8 E Summarising Data 1 Numerical Summary Measures Mean, Median, Mode, Trimmed Means, Quartiles, Percentiles, Range, Variance, Standard Deviation, Inter-quartile Range 7,8,9 F Summarising Data Graphical Techniques for Displaying Data Stem and Leaf Plots, Histograms, Box Plots The students will know how to, calculate these quantities, and be able to use them to make statements about a data set. The students will know how to, construct these various graphical techniques, and display data visually. They will be able to identify when each is used. 10,11 G Probability Concepts: Probability of Events, Probability for Discrete and Continuous Random Variables (probability distribution function, probability density function) The students will familiarise themselves with different axioms of probability. They will know the difference between discrete and Continuous Random Variables, and how to calculate their respective probabilities. 1,13 H Particular Probability Distributions: Bernoulli, Binomial, Geometric, Negative Binomial and Normal Distributions. The students will discover how to, identify the different distributions, and calculate their respective probabilities. 14 I Sampling Distributions and the Central Limit Theorem. The students will discover how to form a distribution, and the different issues with small and large sample. 15,16,17,18 J Margin of Error, Confidence Interval Estimation for population mean and proportion using a single sample. The students will discover how to calculate these quantities. They will also know how to interpret them. 19,0,1,,3 K Hypothesis testing: Principle of a Hypothesis Test, Errors in The students will know the following: different steps and errors involved in

4 Hypothesis Testing, P-values, Tests for population means and proportions in a single sample Hypothesis Testing; how to calculate P-values from the statistical tables; when to use the t-distribution or normal Distribution function, how to calculate the t or z statistic; how to carry out the Tests for population means and proportions in a single sample. 4 L Summary Schedule Hours Lectures 4 Small Group 11 Labs 6 Totals 41 Recommended Text book: Introduction to Statistics and Data Analysis 3 rd Edition by Roxy Peck, Chris Olsen and Jay Devore.

5 Tutor-led Tutorial Schedule: Weeks Lectures Homework Tutorials Labs 1 1(A) (B) 3(C) 4(C,D) 1(B,C) *Introductory session Introduction 3 5(D) 6(E) (D) 1(B,C) 1(B,C,D) 4 7(E,F) 8(F) 3(E,F) (D) 5 9(G) 10(G) 4(G) 3(E,F) (E,F) 6 11(H) 1(H) 5(H) 4(G) 7 13(I) 14(I,J) 6(I,+) 5(H) 3(G,H) 8 15(J) 16(J) 7(J) 6(I,+) 9 17(J) 18(J,K) 8(J) 7(J) 4(I,J) 10 19(K) 0(K) 9(K) 8(J) 11 1(K) (K) 10(K) 9(K) 5(K) 1 3(K) 4(L) 10(K) Note: A L: Topic codes; +: Summary of the first six weeks

6 Lectures (n=147) Hours Specified Learning Activities (online, etc ) Submission of Assignment Tutorials (n= 30-50) Laboratories Week 1 Week Topics A,B Topics C,D Topics B,C Problem to Introduction Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Topics D,E Topic G Topic D Topic G Topic H Topic H Topics I,J Topic I Topic J Topic J Topics J,K Topic J Introductory session Topics B,C Topic D Topic G Topic H Topic I Topic J Topics B,C,D Topics G,H Topics I,J

7 Week 10 Week 11 Week 1 Week 13,L Revision Topic J Weeks Exam material from lectures, labs.

Statistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013

Statistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013 Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.1-1.6) Objectives

More information

Fairfield Public Schools

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

More information

International College of Economics and Finance Syllabus Probability Theory and Introductory Statistics

International College of Economics and Finance Syllabus Probability Theory and Introductory Statistics International College of Economics and Finance Syllabus Probability Theory and Introductory Statistics Lecturer: Mikhail Zhitlukhin. 1. Course description Probability Theory and Introductory Statistics

More information

STAT 360 Probability and Statistics. Fall 2012

STAT 360 Probability and Statistics. Fall 2012 STAT 360 Probability and Statistics Fall 2012 1) General information: Crosslisted course offered as STAT 360, MATH 360 Semester: Fall 2012, Aug 20--Dec 07 Course name: Probability and Statistics Number

More information

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in

More information

The Big 50 Revision Guidelines for S1

The Big 50 Revision Guidelines for S1 The Big 50 Revision Guidelines for S1 If you can understand all of these you ll do very well 1. Know what is meant by a statistical model and the Modelling cycle of continuous refinement 2. Understand

More information

MTH 140 Statistics Videos

MTH 140 Statistics Videos MTH 140 Statistics Videos Chapter 1 Picturing Distributions with Graphs Individuals and Variables Categorical Variables: Pie Charts and Bar Graphs Categorical Variables: Pie Charts and Bar Graphs Quantitative

More information

AP Statistics: Syllabus 1

AP Statistics: Syllabus 1 AP Statistics: Syllabus 1 Scoring Components SC1 The course provides instruction in exploring data. 4 SC2 The course provides instruction in sampling. 5 SC3 The course provides instruction in experimentation.

More information

4. Introduction to Statistics

4. Introduction to Statistics Statistics for Engineers 4-1 4. Introduction to Statistics Descriptive Statistics Types of data A variate or random variable is a quantity or attribute whose value may vary from one unit of investigation

More information

430 Statistics and Financial Mathematics for Business

430 Statistics and Financial Mathematics for Business Prescription: 430 Statistics and Financial Mathematics for Business Elective prescription Level 4 Credit 20 Version 2 Aim Students will be able to summarise, analyse, interpret and present data, make predictions

More information

MAT 12O ELEMENTARY STATISTICS I

MAT 12O ELEMENTARY STATISTICS I LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 12O ELEMENTARY STATISTICS I 3 Lecture Hours, 1 Lab Hour, 3 Credits Pre-Requisite:

More information

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics

Course Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This

More information

! x sum of the entries

! x sum of the entries 3.1 Measures of Central Tendency (Page 1 of 16) 3.1 Measures of Central Tendency Mean, Median and Mode! x sum of the entries a. mean, x = = n number of entries Example 1 Find the mean of 26, 18, 12, 31,

More information

Summary of Formulas and Concepts. Descriptive Statistics (Ch. 1-4)

Summary of Formulas and Concepts. Descriptive Statistics (Ch. 1-4) Summary of Formulas and Concepts Descriptive Statistics (Ch. 1-4) Definitions Population: The complete set of numerical information on a particular quantity in which an investigator is interested. We assume

More information

2. A is a subset of the population. 3. Construct a frequency distribution for the data of the grades of 25 students taking Math 11 last

2. A is a subset of the population. 3. Construct a frequency distribution for the data of the grades of 25 students taking Math 11 last Math 111 Chapter 12 Practice Test 1. If I wanted to survey 50 Cabrini College students about where they prefer to eat on campus, which would be the most appropriate way to conduct my survey? a. Find 50

More information

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.

Business Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics. Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing

More information

Chapter 2: Exploring Data with Graphs and Numerical Summaries. Graphical Measures- Graphs are used to describe the shape of a data set.

Chapter 2: Exploring Data with Graphs and Numerical Summaries. Graphical Measures- Graphs are used to describe the shape of a data set. Page 1 of 16 Chapter 2: Exploring Data with Graphs and Numerical Summaries Graphical Measures- Graphs are used to describe the shape of a data set. Section 1: Types of Variables In general, variable can

More information

Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010

Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 2009-2010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different

More information

A Correlation of. to the. South Carolina Data Analysis and Probability Standards

A Correlation of. to the. South Carolina Data Analysis and Probability Standards A Correlation of to the South Carolina Data Analysis and Probability Standards INTRODUCTION This document demonstrates how Stats in Your World 2012 meets the indicators of the South Carolina Academic Standards

More information

Seminar paper Statistics

Seminar paper Statistics Seminar paper Statistics The seminar paper must contain: - the title page - the characterization of the data (origin, reason why you have chosen this analysis,...) - the list of the data (in the table)

More information

Governors State University College of Business and Public Administration. Course: STAT 361-03 Statistics for Management I (Online Course)

Governors State University College of Business and Public Administration. Course: STAT 361-03 Statistics for Management I (Online Course) Governors State University College of Business and Public Administration Course: STAT 361-03 Statistics for Management I (Online Course) Instructor: Kevin M. Riordan, M.A. Session: Fall Semester 2011 Prerequisite:

More information

Economic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007)

Economic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007) COURSE DESCRIPTION Title Code Level Semester Credits 3 Prerequisites Post requisites Introduction to Statistics ECON1005 (EC160) I I None Economic Statistics (ECON2006), Statistics and Research Design

More information

STAT 155 Introductory Statistics. Lecture 5: Density Curves and Normal Distributions (I)

STAT 155 Introductory Statistics. Lecture 5: Density Curves and Normal Distributions (I) The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL STAT 155 Introductory Statistics Lecture 5: Density Curves and Normal Distributions (I) 9/12/06 Lecture 5 1 A problem about Standard Deviation A variable

More information

Texas A&M University Central Texas Math 311 Probability and Statistics Online

Texas A&M University Central Texas Math 311 Probability and Statistics Online Texas A&M University Central Texas Math 311 Probability and Statistics Online Instructor: Mienie de Kock (Ph.D) Office: Warrior Hall Room 412- B Phone: (903) 705-9703 Email: dekock@tamuct.edu Office Hours:

More information

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012 Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts

More information

Lecture 2: Descriptive Statistics and Exploratory Data Analysis

Lecture 2: Descriptive Statistics and Exploratory Data Analysis Lecture 2: Descriptive Statistics and Exploratory Data Analysis Further Thoughts on Experimental Design 16 Individuals (8 each from two populations) with replicates Pop 1 Pop 2 Randomly sample 4 individuals

More information

GCSE HIGHER Statistics Key Facts

GCSE HIGHER Statistics Key Facts GCSE HIGHER Statistics Key Facts Collecting Data When writing questions for questionnaires, always ensure that: 1. the question is worded so that it will allow the recipient to give you the information

More information

1. 2. 3. 4. Find the mean and median. 5. 1, 2, 87 6. 3, 2, 1, 10. Bellwork 3-23-15 Simplify each expression.

1. 2. 3. 4. Find the mean and median. 5. 1, 2, 87 6. 3, 2, 1, 10. Bellwork 3-23-15 Simplify each expression. Bellwork 3-23-15 Simplify each expression. 1. 2. 3. 4. Find the mean and median. 5. 1, 2, 87 6. 3, 2, 1, 10 1 Objectives Find measures of central tendency and measures of variation for statistical data.

More information

Section 3.1 Measures of Central Tendency: Mode, Median, and Mean

Section 3.1 Measures of Central Tendency: Mode, Median, and Mean Section 3.1 Measures of Central Tendency: Mode, Median, and Mean One number can be used to describe the entire sample or population. Such a number is called an average. There are many ways to compute averages,

More information

STAT 2300: BUSINESS STATISTICS Section 002, Summer Semester 2009

STAT 2300: BUSINESS STATISTICS Section 002, Summer Semester 2009 STAT 2300: BUSINESS STATISTICS Section 002, Summer Semester 2009 Instructor: Bill Welbourn Office: Lund 117 Email: bill.welbourn@aggiemail.usu.edu Lectures: MWF 7:30AM 9:40AM in ENGR 104 Office Hours:

More information

GCSE Statistics Revision notes

GCSE Statistics Revision notes GCSE Statistics Revision notes Collecting data Sample This is when data is collected from part of the population. There are different methods for sampling Random sampling, Stratified sampling, Systematic

More information

BNG 202 Biomechanics Lab. Descriptive statistics and probability distributions I

BNG 202 Biomechanics Lab. Descriptive statistics and probability distributions I BNG 202 Biomechanics Lab Descriptive statistics and probability distributions I Overview The overall goal of this short course in statistics is to provide an introduction to descriptive and inferential

More information

Description. Textbook. Grading. Objective

Description. Textbook. Grading. Objective EC151.02 Statistics for Business and Economics (MWF 8:00-8:50) Instructor: Chiu Yu Ko Office: 462D, 21 Campenalla Way Phone: 2-6093 Email: kocb@bc.edu Office Hours: by appointment Description This course

More information

Chapter 2: Data quantifiers: sample mean, sample variance, sample standard deviation Quartiles, percentiles, median, interquartile range Dot diagrams

Chapter 2: Data quantifiers: sample mean, sample variance, sample standard deviation Quartiles, percentiles, median, interquartile range Dot diagrams Review for Final Chapter 2: Data quantifiers: sample mean, sample variance, sample standard deviation Quartiles, percentiles, median, interquartile range Dot diagrams Histogram Boxplots Chapter 3: Set

More information

Statistics revision. Dr. Inna Namestnikova. Statistics revision p. 1/8

Statistics revision. Dr. Inna Namestnikova. Statistics revision p. 1/8 Statistics revision Dr. Inna Namestnikova inna.namestnikova@brunel.ac.uk Statistics revision p. 1/8 Introduction Statistics is the science of collecting, analyzing and drawing conclusions from data. Statistics

More information

THE RE-DESIGN OF A STATISTICS COURSE. Laurie Huffman Georgia College CBX 17 Milledgeville, GA 31061

THE RE-DESIGN OF A STATISTICS COURSE. Laurie Huffman Georgia College CBX 17 Milledgeville, GA 31061 THE RE-DESIGN OF A STATISTICS COURSE Laurie Huffman Georgia College CBX 17 Milledgeville, GA 31061 laurie.huffman@gcsu.edu Abstract This project describes a re-thinking of a traditional Probability and

More information

Introduction to Hypothesis Testing. Point estimation and confidence intervals are useful statistical inference procedures.

Introduction to Hypothesis Testing. Point estimation and confidence intervals are useful statistical inference procedures. Introduction to Hypothesis Testing Point estimation and confidence intervals are useful statistical inference procedures. Another type of inference is used frequently used concerns tests of hypotheses.

More information

Course Syllabus MATH 110 Introduction to Statistics 3 credits

Course Syllabus MATH 110 Introduction to Statistics 3 credits Course Syllabus MATH 110 Introduction to Statistics 3 credits Prerequisites: Algebra proficiency is required, as demonstrated by successful completion of high school algebra, by completion of a college

More information

List of Examples. Examples 319

List of Examples. Examples 319 Examples 319 List of Examples DiMaggio and Mantle. 6 Weed seeds. 6, 23, 37, 38 Vole reproduction. 7, 24, 37 Wooly bear caterpillar cocoons. 7 Homophone confusion and Alzheimer s disease. 8 Gear tooth strength.

More information

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo Readings: Ha and Ha Textbook - Chapters 1 8 Appendix D & E (online) Plous - Chapters 10, 11, 12 and 14 Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability

More information

Less Stress More Success Maths Leaving Cert Higher Level Paper 2

Less Stress More Success Maths Leaving Cert Higher Level Paper 2 Less Stress More Success Maths Leaving Cert Higher Level Paper 2 Revised pages for Chapter 13 Statistics IV: The Normal Curve, z -Scores, Hypothesis Testing and Simulation 228 LESS STRESS MORE SUCCESS

More information

STAT 200: Course Aims and Objectives

STAT 200: Course Aims and Objectives STAT 200: Course Aims and Objectives Attitudinal aims In addition to specific learning outcomes, the course aims to shape the attitudes of learners regarding the field of Statistics. Specifically, the

More information

Descriptive Statistics

Descriptive Statistics Y520 Robert S Michael Goal: Learn to calculate indicators and construct graphs that summarize and describe a large quantity of values. Using the textbook readings and other resources listed on the web

More information

Chapter 6 Random Variables

Chapter 6 Random Variables Chapter 6 Random Variables Day 1: 6.1 Discrete Random Variables Read 340-344 What is a random variable? Give some examples. A numerical variable that describes the outcomes of a chance process. Examples:

More information

NEW YORK CITY COLLEGE OF TECHNOLOGY The City University of New York

NEW YORK CITY COLLEGE OF TECHNOLOGY The City University of New York NEW YORK CITY COLLEGE OF TECHNOLOGY The City University of New York DEPARTMENT: Mathematics COURSE: MAT 1272/ MA 272 TITLE: DESCRIPTION: TEXT: Statistics An introduction to statistical methods and statistical

More information

Exploratory Data Analysis

Exploratory Data Analysis Exploratory Data Analysis Johannes Schauer johannes.schauer@tugraz.at Institute of Statistics Graz University of Technology Steyrergasse 17/IV, 8010 Graz www.statistics.tugraz.at February 12, 2008 Introduction

More information

Quantitative Methods for Finance

Quantitative Methods for Finance Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain

More information

MAT 282 STATISTICS 3 cr. (3-0) (online sections)

MAT 282 STATISTICS 3 cr. (3-0) (online sections) JOHN A. LOGAN COLLEGE J. Dethrow SM 11 MAT 282 STATISTICS 3 cr. (3-0) (online sections) COURSE DESCRIPTION: MAT 282 is designed to meet the needs of students requiring a statistics course with a college

More information

Elementary Statistics

Elementary Statistics Elementary Statistics MATH 1342-42073 Syllabus Instructor: Scott Tyson E-mail: styson@austincc.edu Office: TBA Office Hours: TBA Meeting time: TTH 2:50pm-4:05pm Room: SAC 1301 1 The Course 1.1 Course Description

More information

Truman College-Mathematics Department Math 125-CD: Introductory Statistics Course Syllabus Fall 2012

Truman College-Mathematics Department Math 125-CD: Introductory Statistics Course Syllabus Fall 2012 Instructor: Dr. Abdallah Shuaibi Office #: 3816 Email: ashuaibi1@ccc.edu URL: http://faculty.ccc.edu/ashuaibi/ Phone #: (773)907-4085 Office Hours: Truman College-Mathematics Department Math 125-CD: Introductory

More information

Box plots & t-tests. Example

Box plots & t-tests. Example Box plots & t-tests Box Plots Box plots are a graphical representation of your sample (easy to visualize descriptive statistics); they are also known as box-and-whisker diagrams. Any data that you can

More information

F. Farrokhyar, MPhil, PhD, PDoc

F. Farrokhyar, MPhil, PhD, PDoc Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How

More information

Descriptive Statistics. Understanding Data: Categorical Variables. Descriptive Statistics. Dataset: Shellfish Contamination

Descriptive Statistics. Understanding Data: Categorical Variables. Descriptive Statistics. Dataset: Shellfish Contamination Descriptive Statistics Understanding Data: Dataset: Shellfish Contamination Location Year Species Species2 Method Metals Cadmium (mg kg - ) Chromium (mg kg - ) Copper (mg kg - ) Lead (mg kg - ) Mercury

More information

LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE

LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre-

More information

Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini

Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building

More information

Versions 1a Page 1 of 17

Versions 1a Page 1 of 17 Note to Students: This practice exam is intended to give you an idea of the type of questions the instructor asks and the approximate length of the exam. It does NOT indicate the exact questions or the

More information

Introductory Statistics Notes

Introductory Statistics Notes Introductory Statistics Notes Jamie DeCoster Department of Psychology University of Alabama 348 Gordon Palmer Hall Box 870348 Tuscaloosa, AL 35487-0348 Phone: (205) 348-4431 Fax: (205) 348-8648 August

More information

Sample Exam #1 Elementary Statistics

Sample Exam #1 Elementary Statistics Sample Exam #1 Elementary Statistics Instructions. No books, notes, or calculators are allowed. 1. Some variables that were recorded while studying diets of sharks are given below. Which of the variables

More information

1 Measures for location and dispersion of a sample

1 Measures for location and dispersion of a sample Statistical Geophysics WS 2008/09 7..2008 Christian Heumann und Helmut Küchenhoff Measures for location and dispersion of a sample Measures for location and dispersion of a sample In the following: Variable

More information

Math 1342 STATISTICS Course Syllabus

Math 1342 STATISTICS Course Syllabus Math 1342 STATISTICS Course Syllabus Instructor: Mahmoud Basharat; E-mail: Please use email within Eagle-Online Alternating email: basharatah@hotmail.com or mahmoud.basharat@hccs.edu. (Please use only

More information

UNIT 1: COLLECTING DATA

UNIT 1: COLLECTING DATA Core Probability and Statistics Probability and Statistics provides a curriculum focused on understanding key data analysis and probabilistic concepts, calculations, and relevance to real-world applications.

More information

Diablo Valley College Catalog 2014-2015

Diablo Valley College Catalog 2014-2015 Mathematics MATH Michael Norris, Interim Dean Math and Computer Science Division Math Building, Room 267 Possible career opportunities Mathematicians work in a variety of fields, among them statistics,

More information

Exploratory data analysis (Chapter 2) Fall 2011

Exploratory data analysis (Chapter 2) Fall 2011 Exploratory data analysis (Chapter 2) Fall 2011 Data Examples Example 1: Survey Data 1 Data collected from a Stat 371 class in Fall 2005 2 They answered questions about their: gender, major, year in school,

More information

Opgaven Onderzoeksmethoden, Onderdeel Statistiek

Opgaven Onderzoeksmethoden, Onderdeel Statistiek Opgaven Onderzoeksmethoden, Onderdeel Statistiek 1. What is the measurement scale of the following variables? a Shoe size b Religion c Car brand d Score in a tennis game e Number of work hours per week

More information

PROBLEM SET 1. For the first three answer true or false and explain your answer. A picture is often helpful.

PROBLEM SET 1. For the first three answer true or false and explain your answer. A picture is often helpful. PROBLEM SET 1 For the first three answer true or false and explain your answer. A picture is often helpful. 1. Suppose the significance level of a hypothesis test is α=0.05. If the p-value of the test

More information

Descriptive Statistics. Frequency Distributions and Their Graphs 2.1. Frequency Distributions. Chapter 2

Descriptive Statistics. Frequency Distributions and Their Graphs 2.1. Frequency Distributions. Chapter 2 Chapter Descriptive Statistics.1 Frequency Distributions and Their Graphs Frequency Distributions A frequency distribution is a table that shows classes or intervals of data with a count of the number

More information

PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050

PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050 PELLISSIPPI STATE COMMUNITY COLLEGE MASTER SYLLABUS INTRODUCTION TO STATISTICS MATH 2050 Class Hours: 2.0 Credit Hours: 3.0 Laboratory Hours: 2.0 Date Revised: Fall 2013 Catalog Course Description: Descriptive

More information

fifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson

fifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson fifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson Contents What Are These Demos About? How to Use These Demos If This Is Your First Time Using Fathom Tutorial: An Extended Example

More information

Unit 2, Quiz 1 (Practice Quiz)

Unit 2, Quiz 1 (Practice Quiz) Name: Class: Date: Unit 2, Quiz 1 (Practice Quiz) 1. Researchers randomly choose two groups from 15 volunteers. Over a period of 9 weeks, one group watches television before going to sleep, and the other

More information

Data Mining Part 2. Data Understanding and Preparation 2.1 Data Understanding Spring 2010

Data Mining Part 2. Data Understanding and Preparation 2.1 Data Understanding Spring 2010 Data Mining Part 2. and Preparation 2.1 Spring 2010 Instructor: Dr. Masoud Yaghini Introduction Outline Introduction Measuring the Central Tendency Measuring the Dispersion of Data Graphic Displays References

More information

Final Exam Practice Problem Answers

Final Exam Practice Problem Answers Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal

More information

I ~ 14J... <r ku...6l &J&!J--=-O--

I ~ 14J... <r ku...6l &J&!J--=-O-- City College of San Francisco Technology-Mediated Course Proposal Course Outline Addendum I. GENERAL DESCRIPTION A. Date B. Department C. Course Identifier D. Course Title E. Addendum Preparer F. Chairperson

More information

PCHS ALGEBRA PLACEMENT TEST

PCHS ALGEBRA PLACEMENT TEST MATHEMATICS Students must pass all math courses with a C or better to advance to the next math level. Only classes passed with a C or better will count towards meeting college entrance requirements. If

More information

Moraine Valley Community College Course Syllabus

Moraine Valley Community College Course Syllabus Moraine Valley Community College Course Syllabus Course Title: Business Statistics Course Number: MTH 212 Semester: Fall 2006 I Faculty Information A. Instructor: Kevin M. Riordan, M.A. B. Office Hours:

More information

THE BINOMIAL DISTRIBUTION & PROBABILITY

THE BINOMIAL DISTRIBUTION & PROBABILITY REVISION SHEET STATISTICS 1 (MEI) THE BINOMIAL DISTRIBUTION & PROBABILITY The main ideas in this chapter are Probabilities based on selecting or arranging objects Probabilities based on the binomial distribution

More information

Homework 3. Part 1. Name: Score: / null

Homework 3. Part 1. Name: Score: / null Name: Score: / Homework 3 Part 1 null 1 For the following sample of scores, the standard deviation is. Scores: 7, 2, 4, 6, 4, 7, 3, 7 Answer Key: 2 2 For any set of data, the sum of the deviation scores

More information

Econometrics and Data Analysis I

Econometrics and Data Analysis I Econometrics and Data Analysis I Yale University ECON S131 (ONLINE) Summer Session A, 2014 June 2 July 4 Instructor: Doug McKee (douglas.mckee@yale.edu) Teaching Fellow: Yu Liu (dav.yu.liu@yale.edu) Classroom:

More information

vs. relative cumulative frequency

vs. relative cumulative frequency Variable - what we are measuring Quantitative - numerical where mathematical operations make sense. These have UNITS Categorical - puts individuals into categories Numbers don't always mean Quantitative...

More information

Technology Step-by-Step Using StatCrunch

Technology Step-by-Step Using StatCrunch Technology Step-by-Step Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate

More information

MATH 140 HYBRID INTRODUCTORY STATISTICS COURSE SYLLABUS

MATH 140 HYBRID INTRODUCTORY STATISTICS COURSE SYLLABUS MATH 140 HYBRID INTRODUCTORY STATISTICS COURSE SYLLABUS Instructor: Mark Schilling Email: mark.schilling@csun.edu (Note: If your CSUN email address is not one you use regularly, be sure to set up automatic

More information

AP Statistics 2001 Solutions and Scoring Guidelines

AP Statistics 2001 Solutions and Scoring Guidelines AP Statistics 2001 Solutions and Scoring Guidelines The materials included in these files are intended for non-commercial use by AP teachers for course and exam preparation; permission for any other use

More information

Hypothesis Testing. April 21, 2009

Hypothesis Testing. April 21, 2009 Hypothesis Testing April 21, 2009 Your Claim is Just a Hypothesis I ve never made a mistake. Once I thought I did, but I was wrong. Your Claim is Just a Hypothesis Confidence intervals quantify how sure

More information

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010

UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 COURSE: POM 500 Statistical Analysis, ONLINE EDITION, Fall 2010 Prerequisite: Finite Math

More information

EXAM #1 (Example) Instructor: Ela Jackiewicz. Relax and good luck!

EXAM #1 (Example) Instructor: Ela Jackiewicz. Relax and good luck! STP 231 EXAM #1 (Example) Instructor: Ela Jackiewicz Honor Statement: I have neither given nor received information regarding this exam, and I will not do so until all exams have been graded and returned.

More information

Lecture I. Definition 1. Statistics is the science of collecting, organizing, summarizing and analyzing the information in order to draw conclusions.

Lecture I. Definition 1. Statistics is the science of collecting, organizing, summarizing and analyzing the information in order to draw conclusions. Lecture 1 1 Lecture I Definition 1. Statistics is the science of collecting, organizing, summarizing and analyzing the information in order to draw conclusions. It is a process consisting of 3 parts. Lecture

More information

Semester 2 Statistics Short courses

Semester 2 Statistics Short courses Semester 2 Statistics Short courses Course: STAA0001 - Basic Statistics Blackboard Site: STAA0001 Dates: Sat 10 th Sept and 22 Oct 2016 (9 am 5 pm) Room EN409 Assumed Knowledge: None Day 1: Exploratory

More information

LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING

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.

More information

Name: Date: Use the following to answer questions 3-4:

Name: Date: Use the following to answer questions 3-4: Name: Date: 1. Determine whether each of the following statements is true or false. A) The margin of error for a 95% confidence interval for the mean increases as the sample size increases. B) The margin

More information

Northumberland Knowledge

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

More information

Chapter 1: Looking at Data Section 1.1: Displaying Distributions with Graphs

Chapter 1: Looking at Data Section 1.1: Displaying Distributions with Graphs Types of Variables Chapter 1: Looking at Data Section 1.1: Displaying Distributions with Graphs Quantitative (numerical)variables: take numerical values for which arithmetic operations make sense (addition/averaging)

More information

INTRODUCING THE NORMAL DISTRIBUTION IN A DATA ANALYSIS COURSE: SPECIFIC MEANING CONTRIBUTED BY THE USE OF COMPUTERS

INTRODUCING THE NORMAL DISTRIBUTION IN A DATA ANALYSIS COURSE: SPECIFIC MEANING CONTRIBUTED BY THE USE OF COMPUTERS INTRODUCING THE NORMAL DISTRIBUTION IN A DATA ANALYSIS COURSE: SPECIFIC MEANING CONTRIBUTED BY THE USE OF COMPUTERS Liliana Tauber Universidad Nacional del Litoral Argentina Victoria Sánchez Universidad

More information

Mean = (sum of the values / the number of the value) if probabilities are equal

Mean = (sum of the values / the number of the value) if probabilities are equal Population Mean Mean = (sum of the values / the number of the value) if probabilities are equal Compute the population mean Population/Sample mean: 1. Collect the data 2. sum all the values in the population/sample.

More information

1 SAMPLE SIGN TEST. Non-Parametric Univariate Tests: 1 Sample Sign Test 1. A non-parametric equivalent of the 1 SAMPLE T-TEST.

1 SAMPLE SIGN TEST. Non-Parametric Univariate Tests: 1 Sample Sign Test 1. A non-parametric equivalent of the 1 SAMPLE T-TEST. Non-Parametric Univariate Tests: 1 Sample Sign Test 1 1 SAMPLE SIGN TEST A non-parametric equivalent of the 1 SAMPLE T-TEST. ASSUMPTIONS: Data is non-normally distributed, even after log transforming.

More information

Descriptive statistics Statistical inference statistical inference, statistical induction and inferential statistics

Descriptive statistics Statistical inference statistical inference, statistical induction and inferential statistics Descriptive statistics is the discipline of quantitatively describing the main features of a collection of data. Descriptive statistics are distinguished from inferential statistics (or inductive statistics),

More information

UCLA STAT 13 Statistical Methods - Final Exam Review Solutions Chapter 7 Sampling Distributions of Estimates

UCLA STAT 13 Statistical Methods - Final Exam Review Solutions Chapter 7 Sampling Distributions of Estimates UCLA STAT 13 Statistical Methods - Final Exam Review Solutions Chapter 7 Sampling Distributions of Estimates 1. (a) (i) µ µ (ii) σ σ n is exactly Normally distributed. (c) (i) is approximately Normally

More information

Algebra I Pacing Guide Days Units Notes 9 Chapter 1 ( , )

Algebra I Pacing Guide Days Units Notes 9 Chapter 1 ( , ) Algebra I Pacing Guide Days Units Notes 9 Chapter 1 (1.1-1.4, 1.6-1.7) Expressions, Equations and Functions Differentiate between and write expressions, equations and inequalities as well as applying order

More information

Chapter 3 Descriptive Statistics: Numerical Measures. Learning objectives

Chapter 3 Descriptive Statistics: Numerical Measures. Learning objectives Chapter 3 Descriptive Statistics: Numerical Measures Slide 1 Learning objectives 1. Single variable Part I (Basic) 1.1. How to calculate and use the measures of location 1.. How to calculate and use the

More information

Lean Six Sigma Training/Certification Book: Volume 1

Lean Six Sigma Training/Certification Book: Volume 1 Lean Six Sigma Training/Certification Book: Volume 1 Six Sigma Quality: Concepts & Cases Volume I (Statistical Tools in Six Sigma DMAIC process with MINITAB Applications Chapter 1 Introduction to Six Sigma,

More information

Business Statistics MBA 2010. Course Outline

Business Statistics MBA 2010. Course Outline Business Statistics MBA 2010 Course Outline Lecturer: Catalina Stefanescu A305, ext 8846, cstefanescu@london.edu Secretary: Kate Pelling S347, ext 8844, kpelling@london.edu Overview The objective of this

More information

2.0 Lesson Plan. Answer Questions. Summary Statistics. Histograms. The Normal Distribution. Using the Standard Normal Table

2.0 Lesson Plan. Answer Questions. Summary Statistics. Histograms. The Normal Distribution. Using the Standard Normal Table 2.0 Lesson Plan Answer Questions 1 Summary Statistics Histograms The Normal Distribution Using the Standard Normal Table 2. Summary Statistics Given a collection of data, one needs to find representations

More information