Announcements. Final exam, Saturday 9AM to Noon, usual classroom cheat sheet (1 page, front&back) + calculator
|
|
- Jasmine Pierce
- 5 years ago
- Views:
Transcription
1 Announcements Announcements FINAL REVIEW: UNITS 1-7 STATISTICS 101 Nicole Dalzell August 7, 2014 Final exam, Saturday 9AM to Noon, usual classroom cheat sheet (1 page, front&back) + calculator Check grades on Sakai over the weekend and let me know if anything is missing/wrong before the final, no grade changes after the final exam Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Which of the following is true? Modeling ( response) Which of the following is the best visualization for evaluating the relationship between two variables? Modeling ( response) (a) If the sample size is large enough, conclusions can be generalized to the population. (b) If subjects are randomly assigned to treatments, conclusions can be generalized to the population. (c) Blocking in experiments serves a similar purpose as stratifying in observational studies. (d) Representative samples allow us to make causal conclusions. (e) Statistical inference requires normal distribution of the response variable. (a) side-by-side box plots (b) mosaic plot (c) pie chart (d) segmented frequency bar plot (e) relative frequency histogram Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23
2 Which of the following is false? Modeling ( response) Which of the following is false? data Inference Modeling ( response) (a) Box plots are useful for highlighting outliers, but we cannot determine skew based on a box plot. (b) Median and IQR are more robust statistics than mean and SD, respectively, since they are not affected by outliers or extreme skewness. (c) When the response variable is extremely right skewed, it may be useful to apply a log transformation to obtain a more symmetric distribution, and model the logged data. (d) Segmented frequency bar plots are good enough for evaluating the relationship between two variables if the sample sizes are the same for various levels of the explanatory variable. (a) If A and B are independent, then having information on A does not tell us anything about B. (b) If A and B are disjoint, then knowing that A occurs tells us that B cannot occur. (c) Disjoint (mutually exclusive) events are always dependent since if one event occurs we know the other one cannot. (d) If A and B are independent, then P(A and B) = P(A) + P(B). (e) If A and B are not disjoint, then P(A or B) = P(A) + P(B) - P(A and B). Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Which of the following is the least useful method for assessing if the data follow a normal distribution? Modeling ( response) Two students in an introductory statistics class choose to conduct similar studies estimating the proportion of smokers at their school. Student A collects data from 100 students, and student B collects data from 50 students. How will the standard errors used by the two students compare? Assume both are simple random samples. Modeling ( response) (a) Check if 68% of the data are within 1 SD of the mean, 95% of data are within 2 SDs of the mean, and 99.7% of data are within 3 SDs of the mean. (b) Check if the points are on a straight line on a normal probability plot. (c) Check if the mean and median are equal. (d) Check if the distribution is unimodal and symmetric. (e) Generate normally distributed random data with same mean and standard deviation as the observed data, overlay the plots of the generated and observed data, and check if they line up. (a) SE used by Student A < SE used as Student B. (b) SE used by Student A > SE used as Student B. (c) SE used by Student A = SE used as Student B. (d) SE used by Student A SE used as Student B. (e) Cannot tell without knowing the true proportion of smokers at this school. Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23
3 Application exercise: Test the hypothesis H 0 : µ = 10 vs. H A : µ > 10 for the following 6 samples. Assume σ = 2. x n = 30 R Jacob and the R-gonauts Rdolla p value ArrStudio Kool Kucumbers Star-tisticians Belle Curve Mighty Ducks Supa Hot Fire Can We Phone a Friend? MJRJ Team 2 Group 4.0 Mu Chi Sigma The 99% n = 5000 The Alternative Hypotheses The Database The Unexpected Errors The Big Three The Outliers The X Factor The Confounding Variables The Pythons Very-Ables The Control Group The Standard Deviants What are the Odds Which of the following is the best method for evaluating the relationship between two variables? (a) chi-square test of independence (b) chi-square test of goodness of fit (c) anova (d) linear regression (e) t-test Modeling ( response) p value The Critical Values The Statletes Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Inference for data: Which of the following is the best method for evaluating the relationship between a and a variable with many levels? (a) z-test (b) chi-square test of goodness of fit (c) anova (d) linear regression (e) t-test Modeling ( response) One : Parameter of interest: µ n 30 Z, n < 30 T One vs. one (with 2 levels): Parameter of interest: µ 1 µ 2 n 1 and n 2 30 Z, n 1 or n 2 < 30 T If samples are dependent (paired), first find differences between paired observations One vs. one (with > 2 levels) - mean: Parameter of interest: NA ANOVA HT only For all other parameters of interest: simulation Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23
4 Inference for data - binary outcome: Inference for data - > 2 outcomes: Binary outcome: One : Parameter of interest: p S/F condition met Z, if not simulation One vs. one, each with only 2 outcomes: Parameter of interest: p 1 p 2 S/F condition met Z, if not simulation S/F: use obs. S and F for CIs and exp. for HT > 2 outcomes: One, compared to hypothetical distribution: Parameter of interest: NA At least 5 exp. successes in each cell χ 2 GOF, if not simulation HT only One vs. one, either with > 2 outcomes: Parameter of interest: NA At least 5 exp. successes in each cell χ 2 Independence, if not simulation HT only Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Data are collected at a bank on 6 tellers randomly sampled transactions. Do average transaction times vary by teller? Modeling ( response) Response variable:, Explanatory variable: ANOVA Summary statistics: n_1 = 14, mean_1 = , sd_1 = n_2 = 23, mean_2 = , sd_2 = n_3 = 15, mean_3 = 82.66, sd_3 = n_4 = 15, mean_4 = , sd_4 = n_5 = 44, mean_5 = , sd_5 = n_6 = 29, mean_6 = , sd_6 = H_0: All means are equal. H_A: At least one mean is different. Analysis of Variance Table Response: data Df Sum Sq Mean Sq F value Pr(>F) group Residuals Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23
5 Application exercise: ANOVA Data are collected on download times at three different times during the day. We want to evaluate whether average download times vary by time of day. Fill in the??s in the ANOVA output below. Modeling ( response) What is the result of the ANOVA? Response variable:, Explanatory variable: Summary statistics: n_early (7AM) = 16, mean_early (7AM) = , sd_early (7AM) = n_eve (5 PM) = 16, mean_eve (5 PM) = , sd_eve (5 PM) = n_late (12 AM) = 16, mean_late (12 AM) = , sd_late (12 AM) = Analysis of Variance Table Response: data Df Sum Sq Mean Sq F value Pr(>F) group???????? 1.306e-11 Residuals?? ?? Total?? Early (7AM) Evening (5 PM) Late Night (12 AM) Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Application exercise: ANOVA (cont.) The next step is to evaluate the pairwise tests. There are 3 pairs of times of day 1 Early vs. Evening R, ArrStudio, Belle Curve, Can We Phone a Friend?, Group 4.0, Jacob and the R-gonauts, Kool Kucumbers, Mighty Ducks, MJRJ, Mu Chi Sigma 2 Evening vs. Late Night Rdolla, Star-tisticians, Supa Hot Fire, Team 2, The 99%, The Alternative Hypotheses, The Big Three, The Confounding Variables, The Control Group, The Critical Values 3 Early vs. Late Night The Database, The Outliers, The Pythons, The Standard Deviants, The Statletes, The Unexpected Errors The X Factor, Very-Ables, What are the Odds? Determine the appropriate significance level for these tests, and then complete the test assigned to your team. Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23
6 What percent of variability in download times is explained by time of day? Modeling ( response) n = 50 and ˆp = Hypotheses: H 0 : p = 0.82; H A : p < We use a randomization test because the sample size isn t large enough for ˆp to be distributed nearly normally ( = 41 < 10; = 9 < 10) Which of the following is the correct set up for this hypothesis test? Red: success, blue: failure, ˆp sim = proportion of reds in simulated samples. Modeling ( response) Response: data Df Sum Sq Mean Sq F value Pr(>F) group e-11 Residuals (a) (b) (c) (d) (a) Place 80 red and 20 blue chips in a bag. Sample, with replacement, 50 chips proportion of simulations where ˆp sim (b) Place 82 red and 18 blue chips in a bag. Sample, without replacement, 50 chips proportion of simulations where ˆp sim (c) Place 82 red and 18 blue chips in a bag. Sample, with replacement, 50 chips proportion of simulations where ˆp sim (d) Place 82 red and 18 blue chips in a bag. Sample, with replacement, 100 chips proportion of simulations where ˆp sim Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Statistics 101 ( Nicole Dalzell ) Final August 7, / 23 Randomization distribution What is the center of the randomization distribution? What is the result of the hypothesis test? observed randomization statistic Statistics 101 ( Nicole Dalzell ) Final August 7, / 23
Announcements. Final Review: Units 1-7
Announcements Announcements Final : Units 1-7 Statistics 104 Mine Çetinkaya-Rundel June 24, 2013 Final on Wed: cheat sheet (one sheet, front and back) and calculator Must have webcam + audio on at all
More informationAnnoucements. MT2 - Review. one variable. two variables
Housekeeping Annoucements MT2 - Review Statistics 101 Dr. Çetinkaya-Rundel November 4, 2014 Peer evals for projects by Thursday - Qualtrics email will come later this evening Additional MT review session
More informationUnit5: Inferenceforcategoricaldata. 4. MT2 Review. Sta Fall Duke University, Department of Statistical Science
Unit5: Inferenceforcategoricaldata 4. MT2 Review Sta 101 - Fall 2015 Duke University, Department of Statistical Science Dr. Çetinkaya-Rundel Slides posted at http://bit.ly/sta101_f15 Outline 1. Housekeeping
More informationFinalExamReview. Sta Fall Provided: Z, t and χ 2 tables
Final Exam FinalExamReview Sta 101 - Fall 2017 Duke University, Department of Statistical Science When: Wednesday, December 13 from 9:00am-12:00pm What to bring: Scientific calculator (graphing calculator
More informationSTA 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 informationSTATISTICS 141 Final Review
STATISTICS 141 Final Review Bin Zou bzou@ualberta.ca Department of Mathematical & Statistical Sciences University of Alberta Winter 2015 Bin Zou (bzou@ualberta.ca) STAT 141 Final Review Winter 2015 1 /
More informationSets and Set notation. Algebra 2 Unit 8 Notes
Sets and Set notation Section 11-2 Probability Experimental Probability experimental probability of an event: Theoretical Probability number of time the event occurs P(event) = number of trials Sample
More informationNicole Dalzell. July 2, 2014
UNIT 1: INTRODUCTION TO DATA LECTURE 3: EDA (CONT.) AND INTRODUCTION TO STATISTICAL INFERENCE VIA SIMULATION STATISTICS 101 Nicole Dalzell July 2, 2014 Teams and Announcements Team1 = Houdan Sai Cui Huanqi
More informationMATH 1150 Chapter 2 Notation and Terminology
MATH 1150 Chapter 2 Notation and Terminology Categorical Data The following is a dataset for 30 randomly selected adults in the U.S., showing the values of two categorical variables: whether or not the
More informationSociology 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 informationReview 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 informationDETAILED 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 informationBusiness 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" 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 informationAn introduction to biostatistics: part 1
An introduction to biostatistics: part 1 Cavan Reilly September 6, 2017 Table of contents Introduction to data analysis Uncertainty Probability Conditional probability Random variables Discrete random
More informationAP Statistics Cumulative AP Exam Study Guide
AP Statistics Cumulative AP Eam Study Guide Chapters & 3 - Graphs Statistics the science of collecting, analyzing, and drawing conclusions from data. Descriptive methods of organizing and summarizing statistics
More informationDover- 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 informationLecture 5: ANOVA and Correlation
Lecture 5: ANOVA and Correlation Ani Manichaikul amanicha@jhsph.edu 23 April 2007 1 / 62 Comparing Multiple Groups Continous data: comparing means Analysis of variance Binary data: comparing proportions
More informationSMAM 314 Practice Final Examination Winter 2003
SMAM 314 Practice Final Examination Winter 2003 You may use your textbook, one page of notes and a calculator. Please hand in the notes with your exam. 1. Mark the following statements True T or False
More informationThis document contains 3 sets of practice problems.
P RACTICE PROBLEMS This document contains 3 sets of practice problems. Correlation: 3 problems Regression: 4 problems ANOVA: 8 problems You should print a copy of these practice problems and bring them
More informationMr. Stein s Words of Wisdom
Mr. Stein s Words of Wisdom I am writing this review essay for two tests the AP Stat exam and the Applied Stat BFT. The topics are more or less the same, so reviewing for the two tests should be a similar
More informationREVIEW: Midterm Exam. Spring 2012
REVIEW: Midterm Exam Spring 2012 Introduction Important Definitions: - Data - Statistics - A Population - A census - A sample Types of Data Parameter (Describing a characteristic of the Population) Statistic
More informationCh. 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 informationNature vs. nurture? Lecture 18 - Regression: Inference, Outliers, and Intervals. Regression Output. Conditions for inference.
Understanding regression output from software Nature vs. nurture? Lecture 18 - Regression: Inference, Outliers, and Intervals In 1966 Cyril Burt published a paper called The genetic determination of differences
More informationSTA220H1F Term Test Oct 26, Last Name: First Name: Student #: TA s Name: or Tutorial Room:
STA0HF Term Test Oct 6, 005 Last Name: First Name: Student #: TA s Name: or Tutorial Room: Time allowed: hour and 45 minutes. Aids: one sided handwritten aid sheet + non-programmable calculator Statistical
More informationMathematical 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 informationConfidence 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 informationANOVA: Analysis of Variation
ANOVA: Analysis of Variation The basic ANOVA situation Two variables: 1 Categorical, 1 Quantitative Main Question: Do the (means of) the quantitative variables depend on which group (given by categorical
More informationContents. Acknowledgments. xix
Table of Preface Acknowledgments page xv xix 1 Introduction 1 The Role of the Computer in Data Analysis 1 Statistics: Descriptive and Inferential 2 Variables and Constants 3 The Measurement of Variables
More informationExam 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 informationChapter 23. Inferences About Means. Monday, May 6, 13. Copyright 2009 Pearson Education, Inc.
Chapter 23 Inferences About Means Sampling Distributions of Means Now that we know how to create confidence intervals and test hypotheses about proportions, we do the same for means. Just as we did before,
More informationTables 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 informationECON Semester 1 PASS Mock Mid-Semester Exam ANSWERS
ECON1310 2006 Semester 1 PASS Mock Mid-Semester Exam ANSWERS MULTIPLE CHOICE QUESTIONS 1. Unemployment rates are an example of: a. Cross-sectional, quantitative, continuous data b. Time-series, quantitative,
More informationFinal Exam STAT On a Pareto chart, the frequency should be represented on the A) X-axis B) regression C) Y-axis D) none of the above
King Abdul Aziz University Faculty of Sciences Statistics Department Final Exam STAT 0 First Term 49-430 A 40 Name No ID: Section: You have 40 questions in 9 pages. You have 90 minutes to solve the exam.
More informationInterpret Standard Deviation. Outlier Rule. Describe the Distribution OR Compare the Distributions. Linear Transformations SOCS. Interpret a z score
Interpret Standard Deviation Outlier Rule Linear Transformations Describe the Distribution OR Compare the Distributions SOCS Using Normalcdf and Invnorm (Calculator Tips) Interpret a z score What is an
More informationDetermining the Spread of a Distribution
Determining the Spread of a Distribution 1.3-1.5 Cathy Poliak, Ph.D. cathy@math.uh.edu Department of Mathematics University of Houston Lecture 3-2311 Lecture 3-2311 1 / 58 Outline 1 Describing Quantitative
More informationDetermining the Spread of a Distribution
Determining the Spread of a Distribution 1.3-1.5 Cathy Poliak, Ph.D. cathy@math.uh.edu Department of Mathematics University of Houston Lecture 3-2311 Lecture 3-2311 1 / 58 Outline 1 Describing Quantitative
More information:the actual population proportion are equal to the hypothesized sample proportions 2. H a
AP Statistics Chapter 14 Chi- Square Distribution Procedures I. Chi- Square Distribution ( χ 2 ) The chi- square test is used when comparing categorical data or multiple proportions. a. Family of only
More informationFRANKLIN 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 informationdates given in your syllabus.
Slide 2-1 For exams (MD1, MD2, and Final): You may bring one 8.5 by 11 sheet of paper with formulas and notes written or typed on both sides to each exam. For the rest of the quizzes, you will take your
More informationChapter 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 informationINFERENCE FOR REGRESSION
CHAPTER 3 INFERENCE FOR REGRESSION OVERVIEW In Chapter 5 of the textbook, we first encountered regression. The assumptions that describe the regression model we use in this chapter are the following. We
More informationChapter 3. Measuring data
Chapter 3 Measuring data 1 Measuring data versus presenting data We present data to help us draw meaning from it But pictures of data are subjective They re also not susceptible to rigorous inference Measuring
More informationBusiness 320, Fall 1999, Final
Business 320, Fall 1999, Final name You may use a calculator and two cheat sheets. You have 3 hours. I pledge my honor that I have not violated the Honor Code during this examination. Obvioiusly, you may
More informationTABLES 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 informationMacomb Community College Department of Mathematics. Review for the Math 1340 Final Exam
Macomb Community College Department of Mathematics Review for the Math 0 Final Exam WINTER 0 MATH 0 Practice Final Exam WI0 Math0PF/lm Page of MATH 0 Practice Final Exam MATH 0 DEPARTMENT REVIEW FOR THE
More informationChapter 2: Tools for Exploring Univariate Data
Stats 11 (Fall 2004) Lecture Note Introduction to Statistical Methods for Business and Economics Instructor: Hongquan Xu Chapter 2: Tools for Exploring Univariate Data Section 2.1: Introduction What is
More informationOverview. INFOWO Statistics lecture S1: Descriptive statistics. Detailed Overview of the Statistics track. Definition
Overview INFOWO Statistics lecture S1: Descriptive statistics Peter de Waal Introduction to statistics Descriptive statistics Department of Information and Computing Sciences Faculty of Science, Universiteit
More informationIndex I-1. in one variable, solution set of, 474 solving by factoring, 473 cubic function definition, 394 graphs of, 394 x-intercepts on, 474
Index A Absolute value explanation of, 40, 81 82 of slope of lines, 453 addition applications involving, 43 associative law for, 506 508, 570 commutative law for, 238, 505 509, 570 English phrases for,
More informationStatistics 1. Edexcel Notes S1. Mathematical Model. A mathematical model is a simplification of a real world problem.
Statistics 1 Mathematical Model A mathematical model is a simplification of a real world problem. 1. A real world problem is observed. 2. A mathematical model is thought up. 3. The model is used to make
More information1-Way ANOVA MATH 143. Spring Department of Mathematics and Statistics Calvin College
1-Way ANOVA MATH 143 Department of Mathematics and Statistics Calvin College Spring 2010 The basic ANOVA situation Two variables: 1 Categorical, 1 Quantitative Main Question: Do the (means of) the quantitative
More informationappstats27.notebook April 06, 2017
Chapter 27 Objective Students will conduct inference on regression and analyze data to write a conclusion. Inferences for Regression An Example: Body Fat and Waist Size pg 634 Our chapter example revolves
More informationAnnouncements. Lecture 1 - Data and Data Summaries. Data. Numerical Data. all variables. continuous discrete. Homework 1 - Out 1/15, due 1/22
Announcements Announcements Lecture 1 - Data and Data Summaries Statistics 102 Colin Rundel January 13, 2013 Homework 1 - Out 1/15, due 1/22 Lab 1 - Tomorrow RStudio accounts created this evening Try logging
More informationCollaborative Statistics: Symbols and their Meanings
OpenStax-CNX module: m16302 1 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
More informationare the objects described by a set of data. They may be people, animals or things.
( c ) E p s t e i n, C a r t e r a n d B o l l i n g e r 2016 C h a p t e r 5 : E x p l o r i n g D a t a : D i s t r i b u t i o n s P a g e 1 CHAPTER 5: EXPLORING DATA DISTRIBUTIONS 5.1 Creating Histograms
More informationSTAT 4385 Topic 01: Introduction & Review
STAT 4385 Topic 01: Introduction & Review Xiaogang Su, Ph.D. Department of Mathematical Science University of Texas at El Paso xsu@utep.edu Spring, 2016 Outline Welcome What is Regression Analysis? Basics
More informationChapter 27 Summary Inferences for Regression
Chapter 7 Summary Inferences for Regression What have we learned? We have now applied inference to regression models. Like in all inference situations, there are conditions that we must check. We can test
More informationCHAPTER 5: EXPLORING DATA DISTRIBUTIONS. Individuals are the objects described by a set of data. These individuals may be people, animals or things.
(c) Epstein 2013 Chapter 5: Exploring Data Distributions Page 1 CHAPTER 5: EXPLORING DATA DISTRIBUTIONS 5.1 Creating Histograms Individuals are the objects described by a set of data. These individuals
More informationIT 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 informationGLOSSARY. a n + n. a n 1 b + + n. a n r b r + + n C 1. C r. C n
GLOSSARY A absolute cell referencing A spreadsheet feature that blocks automatic adjustment of cell references when formulas are moved or copied. References preceded by a dollar sign $A$1, for example
More informationDescribing Distributions
Describing Distributions With Numbers April 18, 2012 Summary Statistics. Measures of Center. Percentiles. Measures of Spread. A Summary Statement. Choosing Numerical Summaries. 1.0 What Are Summary Statistics?
More informationAIM HIGH SCHOOL. Curriculum Map W. 12 Mile Road Farmington Hills, MI (248)
AIM HIGH SCHOOL Curriculum Map 2923 W. 12 Mile Road Farmington Hills, MI 48334 (248) 702-6922 www.aimhighschool.com COURSE TITLE: Statistics DESCRIPTION OF COURSE: PREREQUISITES: Algebra 2 Students will
More informationLecture 3B: Chapter 4, Section 2 Quantitative Variables (Displays, Begin Summaries)
Lecture 3B: Chapter 4, Section 2 Quantitative Variables (Displays, Begin Summaries) Summarize with Shape, Center, Spread Displays: Stemplots, Histograms Five Number Summary, Outliers, Boxplots Mean vs.
More informationCorrelation and Regression
Correlation and Regression Dr. Bob Gee Dean Scott Bonney Professor William G. Journigan American Meridian University 1 Learning Objectives Upon successful completion of this module, the student should
More informationFundamentals to Biostatistics. Prof. Chandan Chakraborty Associate Professor School of Medical Science & Technology IIT Kharagpur
Fundamentals to Biostatistics Prof. Chandan Chakraborty Associate Professor School of Medical Science & Technology IIT Kharagpur Statistics collection, analysis, interpretation of data development of new
More informationReview 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 informationNemours Biomedical Research Statistics Course. Li Xie Nemours Biostatistics Core October 14, 2014
Nemours Biomedical Research Statistics Course Li Xie Nemours Biostatistics Core October 14, 2014 Outline Recap Introduction to Logistic Regression Recap Descriptive statistics Variable type Example of
More informationChapter 4.notebook. August 30, 2017
Sep 1 7:53 AM Sep 1 8:21 AM Sep 1 8:21 AM 1 Sep 1 8:23 AM Sep 1 8:23 AM Sep 1 8:23 AM SOCS When describing a distribution, make sure to always tell about three things: shape, outliers, center, and spread
More informationConditions for Regression Inference:
AP Statistics Chapter Notes. Inference for Linear Regression We can fit a least-squares line to any data relating two quantitative variables, but the results are useful only if the scatterplot shows a
More informationElementary Statistics
Elementary Statistics Q: What is data? Q: What does the data look like? Q: What conclusions can we draw from the data? Q: Where is the middle of the data? Q: Why is the spread of the data important? Q:
More informationStatistical 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 informationHarvard University. Rigorous Research in Engineering Education
Statistical Inference Kari Lock Harvard University Department of Statistics Rigorous Research in Engineering Education 12/3/09 Statistical Inference You have a sample and want to use the data collected
More informationIntroduction to Statistics
Introduction to Statistics Data and Statistics Data consists of information coming from observations, counts, measurements, or responses. Statistics is the science of collecting, organizing, analyzing,
More informationChapter 6 The Normal Distribution
Chapter 6 The Normal PSY 395 Oswald Outline s and area The normal distribution The standard normal distribution Setting probable limits on a score/observation Measures related to 2 s and Area The idea
More informationSTP 420 INTRODUCTION TO APPLIED STATISTICS NOTES
INTRODUCTION TO APPLIED STATISTICS NOTES PART - DATA CHAPTER LOOKING AT DATA - DISTRIBUTIONS Individuals objects described by a set of data (people, animals, things) - all the data for one individual make
More informationUNIVERSITY OF TORONTO. Faculty of Arts and Science APRIL - MAY 2005 EXAMINATIONS STA 248 H1S. Duration - 3 hours. Aids Allowed: Calculator
UNIVERSITY OF TORONTO Faculty of Arts and Science APRIL - MAY 2005 EXAMINATIONS STA 248 H1S Duration - 3 hours Aids Allowed: Calculator LAST NAME: FIRST NAME: STUDENT NUMBER: There are 17 pages including
More informationComparing Several Means
Comparing Several Means Some slides from R. Pruim STA303/STA1002: Methods of Data Analysis II, Summer 2016 Michael Guerzhoy The Dating World of Swordtail Fish In some species of swordtail fish, males develop
More informationPractical Statistics for the Analytical Scientist Table of Contents
Practical Statistics for the Analytical Scientist Table of Contents Chapter 1 Introduction - Choosing the Correct Statistics 1.1 Introduction 1.2 Choosing the Right Statistical Procedures 1.2.1 Planning
More informationStat 412/512 REVIEW OF SIMPLE LINEAR REGRESSION. Jan Charlotte Wickham. stat512.cwick.co.nz
Stat 412/512 REVIEW OF SIMPLE LINEAR REGRESSION Jan 7 2015 Charlotte Wickham stat512.cwick.co.nz Announcements TA's Katie 2pm lab Ben 5pm lab Joe noon & 1pm lab TA office hours Kidder M111 Katie Tues 2-3pm
More information13.4 Probabilities of Compound Events.notebook May 29, I can calculate probabilities of compound events.
13.4 Date: LT: I can calculate probabilities of compound events. nbp.13 Compound event = Combining two or more events, using the word and or the word or. or = Mutually exclusive events = Overlapping events
More informationSTOCKHOLM UNIVERSITY Department of Economics Course name: Empirical Methods Course code: EC40 Examiner: Lena Nekby Number of credits: 7,5 credits Date of exam: Friday, June 5, 009 Examination time: 3 hours
More informationStat 2300 International, Fall 2006 Sample Midterm. Friday, October 20, Your Name: A Number:
Stat 2300 International, Fall 2006 Sample Midterm Friday, October 20, 2006 Your Name: A Number: The Midterm consists of 35 questions: 20 multiple-choice questions (with exactly 1 correct answer) and 15
More informationSampling, Frequency Distributions, and Graphs (12.1)
1 Sampling, Frequency Distributions, and Graphs (1.1) Design: Plan how to obtain the data. What are typical Statistical Methods? Collect the data, which is then subjected to statistical analysis, which
More informationChi-square tests. Unit 6: Simple Linear Regression Lecture 1: Introduction to SLR. Statistics 101. Poverty vs. HS graduate rate
Review and Comments Chi-square tests Unit : Simple Linear Regression Lecture 1: Introduction to SLR Statistics 1 Monika Jingchen Hu June, 20 Chi-square test of GOF k χ 2 (O E) 2 = E i=1 where k = total
More informationInferences 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 informationExtra Exam Empirical Methods VU University Amsterdam, Faculty of Exact Sciences , July 2, 2015
Extra Exam Empirical Methods VU University Amsterdam, Faculty of Exact Sciences 12.00 14.45, July 2, 2015 Also hand in this exam and your scrap paper. Always motivate your answers. Write your answers in
More informationAP Final Review II Exploring Data (20% 30%)
AP Final Review II Exploring Data (20% 30%) Quantitative vs Categorical Variables Quantitative variables are numerical values for which arithmetic operations such as means make sense. It is usually a measure
More informationy = a + bx 12.1: Inference for Linear Regression Review: General Form of Linear Regression Equation Review: Interpreting Computer Regression Output
12.1: Inference for Linear Regression Review: General Form of Linear Regression Equation y = a + bx y = dependent variable a = intercept b = slope x = independent variable Section 12.1 Inference for Linear
More informationMarquette University Executive MBA Program Statistics Review Class Notes Summer 2018
Marquette University Executive MBA Program Statistics Review Class Notes Summer 2018 Chapter One: Data and Statistics Statistics A collection of procedures and principles
More informationTopic 3: Introduction to Statistics. Algebra 1. Collecting Data. Table of Contents. Categorical or Quantitative? What is the Study of Statistics?!
Topic 3: Introduction to Statistics Collecting Data We collect data through observation, surveys and experiments. We can collect two different types of data: Categorical Quantitative Algebra 1 Table of
More informationMGEC11H3Y L01 Introduction to Regression Analysis Term Test Friday July 5, PM Instructor: Victor Yu
Last Name (Print): Solution First Name (Print): Student Number: MGECHY L Introduction to Regression Analysis Term Test Friday July, PM Instructor: Victor Yu Aids allowed: Time allowed: Calculator and one
More informationChapter 1 - Lecture 3 Measures of Location
Chapter 1 - Lecture 3 of Location August 31st, 2009 Chapter 1 - Lecture 3 of Location General Types of measures Median Skewness Chapter 1 - Lecture 3 of Location Outline General Types of measures What
More informationDescriptive Univariate Statistics and Bivariate Correlation
ESC 100 Exploring Engineering Descriptive Univariate Statistics and Bivariate Correlation Instructor: Sudhir Khetan, Ph.D. Wednesday/Friday, October 17/19, 2012 The Central Dogma of Statistics used to
More information2. Outliers and inference for regression
Unit6: Introductiontolinearregression 2. Outliers and inference for regression Sta 101 - Spring 2016 Duke University, Department of Statistical Science Dr. Çetinkaya-Rundel Slides posted at http://bit.ly/sta101_s16
More informationSociology 6Z03 Review I
Sociology 6Z03 Review I John Fox McMaster University Fall 2016 John Fox (McMaster University) Sociology 6Z03 Review I Fall 2016 1 / 19 Outline: Review I Introduction Displaying Distributions Describing
More informationBasic 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 informationClass 24. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 4 Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science Copyright 013 by D.B. Rowe 1 Agenda: Recap Chapter 9. and 9.3 Lecture Chapter 10.1-10.3 Review Exam 6 Problem Solving
More informationSTATISTICS 1 REVISION NOTES
STATISTICS 1 REVISION NOTES Statistical Model Representing and summarising Sample Data Key words: Quantitative Data This is data in NUMERICAL FORM such as shoe size, height etc. Qualitative Data This is
More informationTable 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 informationM & M Project. Think! Crunch those numbers! Answer!
M & M Project Think! Crunch those numbers! Answer! Chapters 1-2 Exploring Data and Describing Location in a Distribution Univariate Data: Length Stemplot and Frequency Table Stem (Units Digit) 0 1 1 Leaf
More information