Lampiran 1. Surat Uji Coba Penelitian dari Fakultas. Lampiran 2. Expert Judgement

Size: px
Start display at page:

Download "Lampiran 1. Surat Uji Coba Penelitian dari Fakultas. Lampiran 2. Expert Judgement"

Transcription

1 57

2 Lampiran 1. Surat Uji Coba Penelitian dari Fakultas Lampiran 2. Expert Judgement 58

3 59

4 Lanjutan Lampiran 2. 60

5 Lanjutan Lampiran 2. 61

6 Lanjutan lampiran 2. 62

7 Lanjutan lampiran 2. 63

8 Lanjutan lampiran 2. 64

9 Lampiran 3. Surat Ijin Penelitian dari Fakultas 65

10 Lampiran 4. Lembar Pengesahan 66

11 Lampiran 5. Surat Ijin Penelitian dari UKM UNY 67

12 Lampiran 6. Lembar Tera Meteran 68

13 Lanjutan Lampiran 6. 69

14 Lampiran 7. Uji Coba Penelitian TEST KOORDINASI MATA TANGAN NO TEST 1 TEST 2 Jumlah Scale Mean if Item Deleted Validitas Item-Total Statistics Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Tes Tes Jumlah r tabel (df= 13) = Tes 1 Tes 2 Pearson Correlation Reliabilitas Correlations Tes 1 Tes ** Sig. (2-tailed).000 N Pearson Correlation Sig. (2-tailed) ** 1 N **. Correlation is significant at the 0.01 level (2-tailed). 70

15 Lanjutan Lampiran 7. TEST FLEKSIBILITAS PERGELANGAN TANGAN NO TEST 1 TEST 2 Jumlah Scale Mean if Item Deleted Validitas Item-Total Statistics Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Tes Tes Jumlah R table (df= 13) = Tes 1 Tes 2 Pearson Correlation Reliabilitas Correlations Tes 1 Tes ** Sig. (2-tailed).000 N Pearson Correlation Sig. (2-tailed) ** 1 N **. Correlation is significant at the 0.01 level (2-tailed). 71

16 Lanjutan Lampiran 7. TEST KEKUATAN OTOT LENGAN TRICEP NO TEST 1 TEST 2 Jumlah Scale Mean if Item Deleted Validitas Item-Total Statistics Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Tes Tes Jumlah r table (df= 13) = Tes 1 Tes 2 Pearson Correlation Reliabilitas Correlations Tes 1 Tes ** Sig. (2-tailed).000 N Pearson Correlation Sig. (2-tailed) ** 1 N **. Correlation is significant at the 0.01 level (2-tailed). 72

17 Lanjutan Lampiran 7. TEST KEKUATAN OTOT TUNGKAI NO TEST 1 TEST 2 Jumlah Scale Mean if Item Deleted Validitas Item-Total Statistics Scale Variance if Item Deleted 73 Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Tes Tes Jumlah Tes 1 Tes 2 r table (df= 13) = Pearson Correlation Reliabilitas Correlations Tes 1 Tes ** Sig. (2-tailed).000 N Pearson Correlation Sig. (2-tailed) ** 1 N **. Correlation is significant at the 0.01 level (2-tailed).

18 Lanjutan Lampiran 7. TEST JUMP SHOOT NO TEST 1 TEST 2 Jumlah Scale Mean if Item Deleted Validitas Item-Total Statistics Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Tes Tes Jumlah r table (df= 13) = Reliabilitas Correlations Tes 1 Tes 2 Tes 1 Pearson Correlation ** Sig. (2-tailed).002 N Tes 2 Pearson Correlation.783 ** 1 Sig. (2-tailed).002 N **. Correlation is significant at the 0.01 level (2-tailed). 74

19 Lampiran 8. Data Penelitian TEST KOORDINASI MATA TANGAN (X1) NO NAMA TES 1 TES 2 JUMLAH 1 Yohannes dannis P Dwi budimandani Hendri jaya utama Lukman prasojo Dimas afrian. P Adhimas mauldian Ryan tri. P Muhammad ikhwan Gilang ramadhan Denny sanjaya Rahmat hardiyanto Rahmat arif s Haris prasetyo b Cahyo Risdi Irfan Adi Salafi Amingga Angga

20 Lanjutan Lampiran 8. TEST FLEKSIBILITAS PERGELANGAN TANGAN (X2) NO NAMA TES 1 TES 2 Tes 3 Rata-rata 1 Yohannes dannis P Dwi budimandani Hendri jaya utama Lukman prasojo Dimas afrian. P Adhimas mauldian Ryan tri. P Muhammad ikhwan Gilang ramadhan Denny sanjaya Rahmat hardiyanto Rahmat arif s Haris prasetyo b Cahyo Risdi Irfan Adi Salafi Amingga Angga

21 Lanjutan Lampiran 8. TEST POWER LENGAN TRICEP (X3) NO NAMA TES 1 TES 2 TERBAIK 1 Yohannes dannis P Dwi budimandani Hendri jaya utama Lukman prasojo Dimas afrian. P Adhimas mauldian Ryan tri. P Muhammad ikhwan Gilang ramadhan Denny sanjaya Rahmat hardiyanto Rahmat arif s Haris prasetyo b Cahyo Risdi Irfan Adi Salafi Amingga Angga

22 Lanjutan Lampiran 8. TEST POWER TUNGKAI (X4) NO NAMA Selisih Tinggi Loncatan dengan Tinggi Raihan TERBAIK TES 1 TES 2 TES 3 1 Yohannes Dannis P Dwi Budimandani Hendri Lukman Dimas A Adhimas Ryan tri Muhammad Ikhwan Gilang Ramadhan Denny Sanjaya Rahmat Hardiyanto Rahmat Arif Haris Prasetyo Cahyo Risdi Irfan Adi Salafi Amingga Angga

23 Lanjutan Lampiran 8. TEST JUMP SHOOT (Y) NO NAMA TES 1 TES 2 TERBAIK 1 Yohannes dannis P Dwi budimandani hendri lukman Dimas A Adhimas Ryan tri Muhammad ikhwan Gilang ramadhan Denny sanjaya Rahmat hardiyanto Rahmat arif Haris prasetyo cahyo risdi Irfan Adi Salafi Amingga Angga

24 Lanjutan Lampiran 8. RANGKUMAN HASIL PENELITIAN NO X1 X2 X3 X4 Y

25 Lampiran 9. Deskripsi Statistik Koordinasi Mata Tangan Statistics Fleksibilitas Pergelangan Tangan Kekuatan Triceps Power Tungkai Tembakan Jump Shoot N Valid Missing Mean Median Mode a a a 4.00 Std. Deviation Minimum Maximum a. Multiple modes exist. The smallest value is shown Koordinasi Mata Tangan Frequency Percent Valid Percent Cumulative Percent Valid Total Kekuatan Triceps Frequency Percent Valid Percent Cumulative Percent Valid Total

26 Fleksibilitas Pergelangan Tangan Frequency Percent Valid Percent Cumulative Percent Valid Total Power Tungkai Frequency Percent Valid Percent Cumulative Percent Valid Total

27 Tembakan Jump Shoot Frequency Percent Valid Percent Cumulative Percent Valid Total

28 Lampiran 10. Uji Normalitas One-Sample Kolmogorov-Smirnov Test Fleksibilitas Koordinasi Pergelangan Kekuatan Power Tembakan Mata Tangan Tangan Triceps Tungkai Jump Shoot N Normal Mean Parameters a Std Deviation Most Extreme Differences Absolute Positive Negative Kolmogorov-Smirnov Z Asymp. Sig. (2-tailed) a. Test distribution is Normal. 84

29 Lampiran 11. Uji Liniearitas Tembakan Jump Shoot * Koordinasi Mata Tangan Between Groups ANOVA Table Sum of Squares df Mean Square F Sig. (Combined) Linearity Deviation from Linearity Within Groups Total Tembakan Jump Shoot * Fleksibilitas Pergelangan Tangan Between Groups ANOVA Table Sum of Squares df Mean Square F Sig. (Combined) Linearity Deviation from Linearity Within Groups Total Tembakan Jump Shoot * Kekuatan Triceps Between Groups ANOVA Table Sum of Squares df Mean Square F Sig. (Combined) Linearity Deviation from Linearity Within Groups Total Tembakan Jump Shoot * Power Tungkai Between Groups ANOVA Table Sum of Squares df Mean Square F Sig. (Combined) Linearity Deviation from Linearity Within Groups Total

30 Lampiran 12. Uji Regresi Uji Regresi Antara X1, X2, X3, X4 dengan Y Variables Entered/Removed b Model Variables Entered 1 Power Tungkai, Koordinasi Mata Tangan, Fleksibilitas Pergelangan Tangan, Kekuatan Triceps a a. All requested variables entered. b. Dependent Variable: Tembakan Jump Shoot Variables Removed. Enter Method Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), Power Tungkai, Koordinasi Mata Tangan, Fleksibilitas Pergelangan Tangan, Kekuatan Triceps ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression a Residual Total a. Predictors: (Constant), Power Tungkai, Koordinasi Mata Tangan, Fleksibilitas Pergelangan Tangan, Kekuatan Triceps b. Dependent Variable: Tembakan Jump Shoot Model 1 (Constant) Coefficients a Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig Koordinasi Mata Tangan Fleksibilitas Pergelangan Tangan Kekuatan Triceps Power Tungkai a. Dependent Variable: Tembakan Jump Shoot 86

31 Uji Regresi Koordinasi Mata Tangan (X1) dengan Jump Shoot (Y) Model R R Square Model Summary Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), X1 Uji Regresi Fleksibilitas Pergelangan Tangan (X2) dengan Jump Shoot (Y) Model R R Square Model Summary Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), X2 Uji Regresi Kekuatan Otot Lengan Tricep (X3) dengan Jump Shoot (Y) Model R R Square Model Summary Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), X3 Uji Regresi Kekuatan Otot Tungkai (X4) dengan Jump Shoot (Y) Model R R Square Model Summary Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), X4 87

32 Lampiran 13. Hitungan Mencari SE dan SR Koordinasi Mata Tangan Fleksibilitas Pergelangan Tangan Kekuatan Triceps Power Tungkai Tembakan Jump Shoot Pearson Correlation Koordinasi Mata Tangan Correlations Fleksibilitas Pergelangan Tangan Kekuatan Triceps Power Tungkai Tembakan Jump Shoot *.657 **.543 *.780 ** Sig. (2-tailed) Sum of Squares and Crossproducts Covariance N Pearson Correlation.509 * **.654 **.767 ** Sig. (2-tailed) Sum of Squares and Crossproducts Covariance N Pearson Correlation.657 **.663 ** **.820 ** Sig. (2-tailed) Sum of Squares and Crossproducts Covariance N Pearson Correlation.543 *.654 **.617 ** ** Sig. (2-tailed) Sum of Squares and Crossproducts Covariance N Pearson Correlation.780 **.767 **.820 **.762 ** 1 Sig. (2-tailed) Sum of Squares and Crossproducts Covariance N *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2- tailed). 88

33 Model R R Square Model Summary Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), Power Tungkai, Koordinasi Mata Tangan, Fleksibilitas Pergelangan Tangan, Kekuatan Triceps ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression a Residual Total a. Predictors: (Constant), Power Tungkai, Koordinasi Mata Tangan, Fleksibilitas Pergelangan Tangan, Kekuatan Triceps b. Dependent Variable: Tembakan Jump Shoot Model 1 (Constant) Coefficients a Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig Koordinasi Mata Tangan Fleksibilitas Pergelangan Tangan Kekuatan Triceps Power Tungkai a. Dependent Variable: Tembakan Jump Shoot Variabel b Cross-product Regresion R 2 X X X X

34 = = = SE X1 = 25.96% (Koordinasi Mata Tangan) 2 = = SE X2 = 19.25% (Fleksibilitas Pergelangan Tangan) 3 = = SE X3 = 23.46% (Kekuatan Otot Lengan Tricep) 4 = = SE X4 = 18.45% (Kekuatan Otot Tungkai) = 2 100% 1. = % 1. SR X1 = 29.80% 2. = % 2. 90

35 SR X2 = 22.10% 3. = % 3. SR X3 = 26.93% 4. = % 4. SR X4 = 21.18% NO VARIABEL SE SR 1 Koordinasi Mata Tangan 25.96% 29.80% 2 Fleksubilitas Pergelangan Tangan 19.25% 22.10% 3 Kekuatan Otot Lengan Tricep 23.46% 26.93% 4 Kekuatan Otot Tungkai 18.45% 21.18% Jumlah 87.1% 100% 91

36 Lampiran 14. Tabel r pada α 5% Tabel r pada α 5% df r df r df r df r

37 Lampiran 15. Tabel Distribusi F untuk Alpha 5% v2/v

38 Lampiran 16. Dokumentasi Penelitian 94

39 95

40 95

LAMPIRAN A UJI VALIDITAS DAN RELIABILITAS

LAMPIRAN A UJI VALIDITAS DAN RELIABILITAS LAMPIRAN A UJI VALIDITAS DAN RELIABILITAS Validitas Komitmen Karyawan Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item- Total Correlation Cronbach's Alpha if Item Deleted Item01

More information

Daftar Perusahaan Otomotif yang Terdatar di Bursa Efek Indonesia(Periode )

Daftar Perusahaan Otomotif yang Terdatar di Bursa Efek Indonesia(Periode ) 114 Lampiran 1: Populasi Penelitian Daftar Perusahaan Otomotif yang Terdatar di Bursa Efek Indonesia(Periode 2006 2012) 89 1 PT. Astra Internasional Tbk. ASII 2 PT. Astra Otoparts Tbk. AUTO 3 PT. Indo

More information

Universitas Sumatera Utara

Universitas Sumatera Utara Crosstabs Kelompok Usia (thn) * Hiperplasia Crosstabulation Hiperplasia Simpleks Kompleks Total Kelompok Usia (thn) 40 Count 12 17 29 54,5% 77,3% 65,9% Total Count

More information

DISMAS Evaluation: Dr. Elizabeth C. McMullan. Grambling State University

DISMAS Evaluation: Dr. Elizabeth C. McMullan. Grambling State University DISMAS Evaluation 1 Running head: Project Dismas Evaluation DISMAS Evaluation: 2007 2008 Dr. Elizabeth C. McMullan Grambling State University DISMAS Evaluation 2 Abstract An offender notification project

More information

Case Processing Summary. Cases Valid Missing Total N Percent N Percent N Percent % 0 0.0% % % 0 0.0%

Case Processing Summary. Cases Valid Missing Total N Percent N Percent N Percent % 0 0.0% % % 0 0.0% GET FILE='C:\Users\acantrell\Desktop\demo5.sav'. DATASET NAME DataSet1 WINDOW=FRONT. EXAMINE VARIABLES=PASSYDSPG RUSHYDSPG /PLOT BOXPLOT HISTOGRAM /COMPARE GROUPS /STATISTICS DESCRIPTIVES /CINTERVAL 95

More information

Lampiran 1. Daftar Perusahaan. Hasil dari pemilihan sampel dengan kriteria tertentu adalah sebagai berikut:

Lampiran 1. Daftar Perusahaan. Hasil dari pemilihan sampel dengan kriteria tertentu adalah sebagai berikut: Lampiran 1. Daftar Perusahaan Hasil dari pemilihan sampel dengan kriteria tertentu adalah sebagai berikut: NO KODE NAMA PERUSAHAAN 1. AISA Tiga Pilar Sejahtera Food 2. ALMI Alumindo Light Metal Industry

More information

APPENDIX 1 DAFTAR POPULASI DAN SAMPEL TAHUN

APPENDIX 1 DAFTAR POPULASI DAN SAMPEL TAHUN APPENDIX 1 DAFTAR POPULASI DAN SAMPEL TAHUN 2011-2013 No Nama Perusahaan Kode Kriteria Kriteria Kriteri Kriteria Sampel 1 2 a 3 4 1. Agung Podomoro Land APLN 2. Alam Sutera Reality ASRI 3. Bekasi Asri

More information

Running head: DATA ANALYSIS AND INTERPRETATION 1

Running head: DATA ANALYSIS AND INTERPRETATION 1 Running head: DATA ANALYSIS AND INTERPRETATION 1 Data Analysis and Interpretation Final Project Vernon Tilly Jr. University of Central Oklahoma DATA ANALYSIS AND INTERPRETATION 2 Owners of the various

More information

Table 4.1: Descriptive Statistics for FAAM 26-Item ADL Subscale

Table 4.1: Descriptive Statistics for FAAM 26-Item ADL Subscale Table 4.1: Descriptive Statistics for FAAM 26-Item ADL Subscale Item Content Number missing Mean Median SD Skewness (Std. Error) Kurtosis (Std. Error) 1) Standing 52(5.3%) 2.74 3 1.09-0.55(.078) -0.41(.16)

More information

LAMPIRAN. Lampiran 1 Data Sampel Penelitian

LAMPIRAN. Lampiran 1 Data Sampel Penelitian LAMPIRAN Lampiran 1 Data Sampel Penelitian Variabel Audit Tenure pada Perusahaan Sampel NO KODE 2011 2012 2013 1 APLN (BING HARIANTO,SE) 1 (ALVIN ISMARTO) 1 (ALVIN ISMARTO) 2 2 ASRI (HIDAJAT RAHARDJO )1

More information

Physical Condition Contribution to The Drag Flick Performance

Physical Condition Contribution to The Drag Flick Performance Tersedia secara online http://journal.um.ac.id/index.php/jptpp/ EISSN: 2502-471X DOAJ-SHERPA/RoMEO-Google Scholar-IPI Physical Condition Contribution to The Drag Flick Performance Kartika Septianingrum

More information

Pengujian Total Fenol

Pengujian Total Fenol LAMPIRAN Lampiran 1. Dokumentasi Penelitian a. Preparasi sampel Persiapan alat dan bahan Pemotongan bahan b. Analisis proksimat Penimbangan sampel Analisis kadar lemak Analisis kadar protein Analisis abu

More information

Driv e accu racy. Green s in regul ation

Driv e accu racy. Green s in regul ation LEARNING ACTIVITIES FOR PART II COMPILED Statistical and Measurement Concepts We are providing a database from selected characteristics of golfers on the PGA Tour. Data are for 3 of the players, based

More information

One-factor ANOVA by example

One-factor ANOVA by example ANOVA One-factor ANOVA by example 2 One-factor ANOVA by visual inspection 3 4 One-factor ANOVA H 0 H 0 : µ 1 = µ 2 = µ 3 = H A : not all means are equal 5 One-factor ANOVA but why not t-tests t-tests?

More information

y ) s x x )(y i (x i r = 1 n 1 s y Statistics Lecture 7 Exploring Data , y 2 ,y n (x 1 ),,(x n ),(x 2 ,y 1 How two variables vary together

y ) s x x )(y i (x i r = 1 n 1 s y Statistics Lecture 7 Exploring Data , y 2 ,y n (x 1 ),,(x n ),(x 2 ,y 1 How two variables vary together Statistics 111 - Lecture 7 Exploring Data Numerical Summaries for Relationships between Variables Administrative Notes Homework 1 due in recitation: Friday, Feb. 5 Homework 2 now posted on course website:

More information

Stats 2002: Probabilities for Wins and Losses of Online Gambling

Stats 2002: Probabilities for Wins and Losses of Online Gambling Abstract: Jennifer Mateja Andrea Scisinger Lindsay Lacher Stats 2002: Probabilities for Wins and Losses of Online Gambling The objective of this experiment is to determine whether online gambling is a

More information

Tourism impacts from major sports events Visiting previous host destinations or future events

Tourism impacts from major sports events Visiting previous host destinations or future events Tourism impacts from major sports events Visiting previous host destinations or future events Harry Arne Solberg and Arne M. Ulvnes Trondheim Business School Do major sports events create tourism impacts?

More information

INFLUENCE OF ENVIRONMENTAL PARAMETERS ON FISHERY

INFLUENCE OF ENVIRONMENTAL PARAMETERS ON FISHERY Chapter 5 INFLUENCE OF ENVIRONMENTAL PARAMETERS ON FISHERY 5. Introduction Environmental factors contribute to the population dynamics and abundance of marine fishery. The relationships between weather,

More information

Biostatistics & SAS programming

Biostatistics & SAS programming Biostatistics & SAS programming Kevin Zhang March 6, 2017 ANOVA 1 Two groups only Independent groups T test Comparison One subject belongs to only one groups and observed only once Thus the observations

More information

The Power Contribution Of Arm Muscle Strength And Eyes-Hand Coordination To Volleyball Set Up Passing Skill

The Power Contribution Of Arm Muscle Strength And Eyes-Hand Coordination To Volleyball Set Up Passing Skill The Power Contribution Of Arm Muscle Strength And Eyes-Hand Coordination To Volleyball Set Up Passing Skill Yuni Astuti Fakultas Keguruan dan Ilmu Pendidikan, Universitas Bung Hatta, email: yuniastuti@bunghatta.ac.id

More information

Lecture 22: Multiple Regression (Ordinary Least Squares -- OLS)

Lecture 22: Multiple Regression (Ordinary Least Squares -- OLS) Statistics 22_multiple_regression.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lecture 22: Multiple Regression (Ordinary Least Squares -- OLS) Some Common Sense Assumptions for Multiple Regression

More information

Statistical Analysis of PGA Tour Skill Rankings USGA Research and Test Center June 1, 2007

Statistical Analysis of PGA Tour Skill Rankings USGA Research and Test Center June 1, 2007 Statistical Analysis of PGA Tour Skill Rankings 198-26 USGA Research and Test Center June 1, 27 1. Introduction The PGA Tour has recorded and published Tour Player performance statistics since 198. All

More information

Announcements. Lecture 19: Inference for SLR & Transformations. Online quiz 7 - commonly missed questions

Announcements. Lecture 19: Inference for SLR & Transformations. Online quiz 7 - commonly missed questions Announcements Announcements Lecture 19: Inference for SLR & Statistics 101 Mine Çetinkaya-Rundel April 3, 2012 HW 7 due Thursday. Correlation guessing game - ends on April 12 at noon. Winner will be announced

More information

Navigate to the golf data folder and make it your working directory. Load the data by typing

Navigate to the golf data folder and make it your working directory. Load the data by typing Golf Analysis 1.1 Introduction In a round, golfers have a number of choices to make. For a particular shot, is it better to use the longest club available to try to reach the green, or would it be better

More information

Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One- Way ANOVA

Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One- Way ANOVA Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One- Way ANOVA Limitations of the t-test Although the t-test is commonly used, it has limitations Can only

More information

Math SL Internal Assessment What is the relationship between free throw shooting percentage and 3 point shooting percentages?

Math SL Internal Assessment What is the relationship between free throw shooting percentage and 3 point shooting percentages? Math SL Internal Assessment What is the relationship between free throw shooting percentage and 3 point shooting percentages? fts6 Introduction : Basketball is a sport where the players have to be adept

More information

Data Set 7: Bioerosion by Parrotfish Background volume of bites The question:

Data Set 7: Bioerosion by Parrotfish Background volume of bites The question: Data Set 7: Bioerosion by Parrotfish Background Bioerosion of coral reefs results from animals taking bites out of the calcium-carbonate skeleton of the reef. Parrotfishes are major bioerosion agents,

More information

The Reliability of Intrinsic Batted Ball Statistics Appendix

The Reliability of Intrinsic Batted Ball Statistics Appendix The Reliability of ntrinsic Batted Ball Statistics Appendix Glenn Healey, EECS Department University of California, rvine, CA 92617 Given information about batted balls for a set of players, we review

More information

Regression Analysis of Travel Indicators and Public Transport Usage in Urban Areas

Regression Analysis of Travel Indicators and Public Transport Usage in Urban Areas Regression Analysis of Travel Indicators and Public Transport Usage in Urban Areas M. Moeinaddini, Z. Asadi-Shekari, M. Zaly Shah, A. Hamzah Abstract Currently, planners try to have more green travel options

More information

Midterm Exam 1, section 2. Thursday, September hour, 15 minutes

Midterm Exam 1, section 2. Thursday, September hour, 15 minutes San Francisco State University Michael Bar ECON 312 Fall 2018 Midterm Exam 1, section 2 Thursday, September 27 1 hour, 15 minutes Name: Instructions 1. This is closed book, closed notes exam. 2. You can

More information

On the association of inrun velocity and jumping width in ski. jumping

On the association of inrun velocity and jumping width in ski. jumping On the association of inrun velocity and jumping width in ski jumping Oliver Kuss Institute of Medical Epidemiology, Biostatistics, and Informatics University of Halle-Wittenberg, 06097 Halle (Saale),

More information

Announcements. % College graduate vs. % Hispanic in LA. % College educated vs. % Hispanic in LA. Problem Set 10 Due Wednesday.

Announcements. % College graduate vs. % Hispanic in LA. % College educated vs. % Hispanic in LA. Problem Set 10 Due Wednesday. Announcements Announcements UNIT 7: MULTIPLE LINEAR REGRESSION LECTURE 1: INTRODUCTION TO MLR STATISTICS 101 Problem Set 10 Due Wednesday Nicole Dalzell June 15, 2015 Statistics 101 (Nicole Dalzell) U7

More information

ASTERISK OR EXCLAMATION POINT?: Power Hitting in Major League Baseball from 1950 Through the Steroid Era. Gary Evans Stat 201B Winter, 2010

ASTERISK OR EXCLAMATION POINT?: Power Hitting in Major League Baseball from 1950 Through the Steroid Era. Gary Evans Stat 201B Winter, 2010 ASTERISK OR EXCLAMATION POINT?: Power Hitting in Major League Baseball from 1950 Through the Steroid Era by Gary Evans Stat 201B Winter, 2010 Introduction: After a playerʼs strike in 1994 which resulted

More information

CHAPTER ANALYSIS AND INTERPRETATION Average total number of collisions for a try to be scored

CHAPTER ANALYSIS AND INTERPRETATION Average total number of collisions for a try to be scored CHAPTER 8 8.1 ANALYSIS AND INTERPRETATION As mentioned in the previous chapter, four key components have been identified as indicators of the level of significance of dominant collisions when evaluating

More information

Preliminary statistical analysis of. the international eventing. results 2013

Preliminary statistical analysis of. the international eventing. results 2013 Lausanne 28/1/14 Preliminary statistical analysis of the international eventing results 2013 Overview of the talk Statistical analysis The data The statistical technique Analysis of the falls data (related

More information

save percentages? (Name) (University)

save percentages? (Name) (University) 1 IB Maths Essay: What is the correlation between the height of football players and their save percentages? (Name) (University) Table of Contents Raw Data for Analysis...3 Table 1: Raw Data...3 Rationale

More information

Keywords: multiple linear regression; pedestrian crossing delay; right-turn car flow; the number of pedestrians;

Keywords: multiple linear regression; pedestrian crossing delay; right-turn car flow; the number of pedestrians; Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Scien ce s 96 ( 2013 ) 1997 2003 13th COTA International Conference of Transportation Professionals (CICTP 2013)

More information

Guide to Computing Minitab commands used in labs (mtbcode.out)

Guide to Computing Minitab commands used in labs (mtbcode.out) Guide to Computing Minitab commands used in labs (mtbcode.out) A full listing of Minitab commands can be found by invoking the HELP command while running Minitab. A reference card, with listing of available

More information

STANDARD SCORES AND THE NORMAL DISTRIBUTION

STANDARD SCORES AND THE NORMAL DISTRIBUTION STANDARD SCORES AND THE NORMAL DISTRIBUTION REVIEW 1.MEASURES OF CENTRAL TENDENCY A.MEAN B.MEDIAN C.MODE 2.MEASURES OF DISPERSIONS OR VARIABILITY A.RANGE B.DEVIATION FROM THE MEAN C.VARIANCE D.STANDARD

More information

Rice Yield And Dangue Haemorrhagic Fever(DHF) Condition depend upon Climate Data

Rice Yield And Dangue Haemorrhagic Fever(DHF) Condition depend upon Climate Data Rice Yield And Dangue Haemorrhagic Fever(DHF) Condition depend upon Climate Data Dr Lai Lai Aung, Assistant Director( Met Service) Dr Khaing Khaing Soe Assistant Director(Public Health) Dr Thin Nwe htwe

More information

Unit 4: Inference for numerical variables Lecture 3: ANOVA

Unit 4: Inference for numerical variables Lecture 3: ANOVA Unit 4: Inference for numerical variables Lecture 3: ANOVA Statistics 101 Thomas Leininger June 10, 2013 Announcements Announcements Proposals due tomorrow. Will be returned to you by Wednesday. You MUST

More information

Statistical Analysis on Relationship between Muhammadiyah Growths in and its Heritage

Statistical Analysis on Relationship between Muhammadiyah Growths in and its Heritage International Journal of Applied Business and Information Systems ISSN: 2597-8993 Vol 1, No 2, September 2017, pp. 26-33 26 Statistical Analysis on Relationship between Growths in 1912 1964 and its Heritage

More information

Section I: Multiple Choice Select the best answer for each problem.

Section I: Multiple Choice Select the best answer for each problem. Inference for Linear Regression Review Section I: Multiple Choice Select the best answer for each problem. 1. Which of the following is NOT one of the conditions that must be satisfied in order to perform

More information

Week 7 One-way ANOVA

Week 7 One-way ANOVA Week 7 One-way ANOVA Objectives By the end of this lecture, you should be able to: Understand the shortcomings of comparing multiple means as pairs of hypotheses. Understand the steps of the ANOVA method

More information

The probability of winning a high school football game.

The probability of winning a high school football game. Columbus State University CSU epress Faculty Bibliography 2008 The probability of winning a high school football game. Jennifer Brown Follow this and additional works at: http://csuepress.columbusstate.edu/bibliography_faculty

More information

Transportation Research Forum

Transportation Research Forum Transportation Research Forum Modeling through Traffic Speed at Roundabouts along Urban and Suburban Street Arterials Author(s): Bashar H. Al-Omari, Khalid A. Ghuzlan, and Lina B. Al-Helo Source: Journal

More information

Chapter 12 Practice Test

Chapter 12 Practice Test Chapter 12 Practice Test 1. Which of the following is not one of the conditions that must be satisfied in order to perform inference about the slope of a least-squares regression line? (a) For each value

More information

EXST7015: Salaries of all American league baseball players (1994) Salaries in thousands of dollars RAW DATA LISTING

EXST7015: Salaries of all American league baseball players (1994) Salaries in thousands of dollars RAW DATA LISTING ANOVA & Design Randomized Block Design Page 1 1 **EXAMPLE 1******************************************************; 2 *** The 1994 salaries of all American league baseball players ***; 3 *** as reported

More information

Lab 11: Introduction to Linear Regression

Lab 11: Introduction to Linear Regression Lab 11: Introduction to Linear Regression Batter up The movie Moneyball focuses on the quest for the secret of success in baseball. It follows a low-budget team, the Oakland Athletics, who believed that

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics Descriptive Statistics vs Inferential Statistics Describing a sample Making inferences to a larger population Data = Information but too much information. How do we summarize data?

More information

Empirical Example II of Chapter 7

Empirical Example II of Chapter 7 Empirical Example II of Chapter 7 1. We use NBA data. The description of variables is --- --- --- storage display value variable name type format label variable label marr byte %9.2f =1 if married wage

More information

A few things to remember about ANOVA

A few things to remember about ANOVA A few things to remember about ANOVA 1) The F-test that is performed is always 1-tailed. This is because your alternative hypothesis is always that the between group variation is greater than the within

More information

Youngs Creek Hydroelectric Project

Youngs Creek Hydroelectric Project Youngs Creek Hydroelectric Project (FERC No. 10359) Resident Trout Monitoring Plan Annual Report 2014 Survey Prepared by: Everett, WA November 2014 Final This document has been prepared for the District.

More information

COMPLETING THE RESULTS OF THE 2013 BOSTON MARATHON

COMPLETING THE RESULTS OF THE 2013 BOSTON MARATHON COMPLETING THE RESULTS OF THE 2013 BOSTON MARATHON Dorit Hammerling 1, Matthew Cefalu 2, Jessi Cisewski 3, Francesca Dominici 2, Giovanni Parmigiani 2,4, Charles Paulson 5, Richard Smith 1,6 1 Statistical

More information

Distancei = BrandAi + 2 BrandBi + 3 BrandCi + i

Distancei = BrandAi + 2 BrandBi + 3 BrandCi + i . Suppose that the United States Golf Associate (USGA) wants to compare the mean distances traveled by four brands of golf balls when struck by a driver. A completely randomized design is employed with

More information

Pitching Performance and Age

Pitching Performance and Age Pitching Performance and Age Jaime Craig, Avery Heilbron, Kasey Kirschner, Luke Rector and Will Kunin Introduction April 13, 2016 Many of the oldest and most long- term players of the game are pitchers.

More information

Chapter 13. Factorial ANOVA. Patrick Mair 2015 Psych Factorial ANOVA 0 / 19

Chapter 13. Factorial ANOVA. Patrick Mair 2015 Psych Factorial ANOVA 0 / 19 Chapter 13 Factorial ANOVA Patrick Mair 2015 Psych 1950 13 Factorial ANOVA 0 / 19 Today s Menu Now we extend our one-way ANOVA approach to two (or more) factors. Factorial ANOVA: two-way ANOVA, SS decomposition,

More information

ISDS 4141 Sample Data Mining Work. Tool Used: SAS Enterprise Guide

ISDS 4141 Sample Data Mining Work. Tool Used: SAS Enterprise Guide ISDS 4141 Sample Data Mining Work Taylor C. Veillon Tool Used: SAS Enterprise Guide You may have seen the movie, Moneyball, about the Oakland A s baseball team and general manager, Billy Beane, who focused

More information

This page intentionally left blank

This page intentionally left blank PART III BASKETBALL This page intentionally left blank 28 BASKETBALL STATISTICS 101 The Four- Factor Model For each player and team NBA box scores track the following information: two- point field goals

More information

Pitching Performance and Age

Pitching Performance and Age Pitching Performance and Age By: Jaime Craig, Avery Heilbron, Kasey Kirschner, Luke Rector, Will Kunin Introduction April 13, 2016 Many of the oldest players and players with the most longevity of the

More information

Youngs Creek Hydroelectric Project (FERC No. P 10359)

Youngs Creek Hydroelectric Project (FERC No. P 10359) Youngs Creek Hydroelectric Project (FERC No. P 10359) Resident Trout Monitoring Plan Annual Report 2010 Survey and Results of Pre Project Monitoring Prepared by: September 2010 Overview The Public Utility

More information

Example 1: One Way ANOVA in MINITAB

Example 1: One Way ANOVA in MINITAB Example : One Way ANOVA in MINITAB A consumer group wants to compare a new brand of wax (Brand-X) to two leading brands (Sureglow and Microsheen) in terms of Effectiveness of wax. Following data is collected

More information

PREDICTING OUTCOMES OF NBA BASKETBALL GAMES

PREDICTING OUTCOMES OF NBA BASKETBALL GAMES PREDICTING OUTCOMES OF NBA BASKETBALL GAMES A Thesis Submitted to the Graduate Faculty of the North Dakota State University of Agriculture and Applied Science By Eric Scot Jones In Partial Fulfillment

More information

A study evaluating if targeted training for startle effect can improve pilot reactions in handling unexpected situations. DR. MICHAEL GILLEN, PH.D.

A study evaluating if targeted training for startle effect can improve pilot reactions in handling unexpected situations. DR. MICHAEL GILLEN, PH.D. A study evaluating if targeted training for startle effect can improve pilot reactions in handling unexpected situations. DR. MICHAEL GILLEN, PH.D. Disclaimer I would like to thank the FSF, United Airlines,

More information

D1.2 REPORT ON MOTORCYCLISTS IMPACTS WITH ROAD INFRASTRUCTURE BASED OF AN INDEPTH INVESTIGATION OF MOTORCYCLE ACCIDENTS

D1.2 REPORT ON MOTORCYCLISTS IMPACTS WITH ROAD INFRASTRUCTURE BASED OF AN INDEPTH INVESTIGATION OF MOTORCYCLE ACCIDENTS WP 1 D1.2 REPORT ON MOTORCYCLISTS IMPACTS WITH ROAD INFRASTRUCTURE BASED OF AN INDEPTH INVESTIGATION OF MOTORCYCLE ACCIDENTS Project Acronym: Smart RRS Project Full Title: Innovative Concepts for smart

More information

Habit Formation in Voting: Evidence from Rainy Elections Thomas Fujiwara, Kyle Meng, and Tom Vogl ONLINE APPENDIX

Habit Formation in Voting: Evidence from Rainy Elections Thomas Fujiwara, Kyle Meng, and Tom Vogl ONLINE APPENDIX Habit Formation in Voting: Evidence from Rainy Elections Thomas Fujiwara, Kyle Meng, and Tom Vogl ONLINE APPENDIX Figure A1: Share of Counties with Election-Day Rainfall by Year Share of counties with

More information

Analysis of Variance. Copyright 2014 Pearson Education, Inc.

Analysis of Variance. Copyright 2014 Pearson Education, Inc. Analysis of Variance 12-1 Learning Outcomes Outcome 1. Understand the basic logic of analysis of variance. Outcome 2. Perform a hypothesis test for a single-factor design using analysis of variance manually

More information

The Simple Linear Regression Model ECONOMETRICS (ECON 360) BEN VAN KAMMEN, PHD

The Simple Linear Regression Model ECONOMETRICS (ECON 360) BEN VAN KAMMEN, PHD The Simple Linear Regression Model ECONOMETRICS (ECON 360) BEN VAN KAMMEN, PHD Outline Definition. Deriving the Estimates. Properties of the Estimates. Units of Measurement and Functional Form. Expected

More information

Minimal influence of wind and tidal height on underwater noise in Haro Strait

Minimal influence of wind and tidal height on underwater noise in Haro Strait Minimal influence of wind and tidal height on underwater noise in Haro Strait Introduction Scott Veirs, Beam Reach Val Veirs, Colorado College December 2, 2007 Assessing the effect of wind and currents

More information

ANALYSIS OF THE DOMINATING POWER OF SERVICE RECEPTION IN VOLLEYBALL IN DIFFERENT LEVELS OF COMPETITIONS

ANALYSIS OF THE DOMINATING POWER OF SERVICE RECEPTION IN VOLLEYBALL IN DIFFERENT LEVELS OF COMPETITIONS ANALYSIS OF THE DOMINATING POWER OF SERVICE RECEPTION IN VOLLEYBALL IN DIFFERENT LEVELS OF COMPETITIONS 1 SANJIB GHOSH 2 DR. MAHESH SWETA 1 Research scholar, department of Physical Education, Visva-Bharati,

More information

Legendre et al Appendices and Supplements, p. 1

Legendre et al Appendices and Supplements, p. 1 Legendre et al. 2010 Appendices and Supplements, p. 1 Appendices and Supplement to: Legendre, P., M. De Cáceres, and D. Borcard. 2010. Community surveys through space and time: testing the space-time interaction

More information

Predicting the use of the Sacrifice Bunt in Major League Baseball. Charlie Gallagher Brian Gilbert Neelay Mehta Chao Rao

Predicting the use of the Sacrifice Bunt in Major League Baseball. Charlie Gallagher Brian Gilbert Neelay Mehta Chao Rao Predicting the use of the Sacrifice Bunt in Major League Baseball Charlie Gallagher Brian Gilbert Neelay Mehta Chao Rao Understanding the Data Data from the St. Louis Cardinals Sig Mejdal, Senior Quantitative

More information

CS 7641 A (Machine Learning) Sethuraman K, Parameswaran Raman, Vijay Ramakrishnan

CS 7641 A (Machine Learning) Sethuraman K, Parameswaran Raman, Vijay Ramakrishnan CS 7641 A (Machine Learning) Sethuraman K, Parameswaran Raman, Vijay Ramakrishnan Scenario 1: Team 1 scored 200 runs from their 50 overs, and then Team 2 reaches 146 for the loss of two wickets from their

More information

Setting up group models Part 1 NITP, 2011

Setting up group models Part 1 NITP, 2011 Setting up group models Part 1 NITP, 2011 What is coming up Crash course in setting up models 1-sample and 2-sample t-tests Paired t-tests ANOVA! Mean centering covariates Identifying rank deficient matrices

More information

STAT 155 Introductory Statistics. Lecture 2-2: Displaying Distributions with Graphs

STAT 155 Introductory Statistics. Lecture 2-2: Displaying Distributions with Graphs The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL STAT 155 Introductory Statistics Lecture 2-2: Displaying Distributions with Graphs 8/31/06 Lecture 2-2 1 Recall Data: Individuals Variables Categorical variables

More information

Development of Decision Support Tools to Assess Pedestrian and Bicycle Safety: Development of Safety Performance Function

Development of Decision Support Tools to Assess Pedestrian and Bicycle Safety: Development of Safety Performance Function Development of Decision Support Tools to Assess Pedestrian and Bicycle Safety: Development of Safety Performance Function Valerian Kwigizile, Jun Oh, Ron Van Houten, & Keneth Kwayu INTRODUCTION 2 OVERVIEW

More information

Building an NFL performance metric

Building an NFL performance metric Building an NFL performance metric Seonghyun Paik (spaik1@stanford.edu) December 16, 2016 I. Introduction In current pro sports, many statistical methods are applied to evaluate player s performance and

More information

Teachers Emphasize Science Investigation Scale, Eighth Grade

Teachers Emphasize Science Investigation Scale, Eighth Grade Teachers Emphasize Science Investigation Scale, Eighth Grade The Teachers Emphasize Science Investigation (ESI) scale was created based on teachers responses to how often they used the eight instructional

More information

An Empirical Comparison of Regression Analysis Strategies with Discrete Ordinal Variables

An Empirical Comparison of Regression Analysis Strategies with Discrete Ordinal Variables Kromrey & Rendina-Gobioff An Empirical Comparison of Regression Analysis Strategies with Discrete Ordinal Variables Jeffrey D. Kromrey Gianna Rendina-Gobioff University of South Florida The Type I error

More information

Failure Data Analysis for Aircraft Maintenance Planning

Failure Data Analysis for Aircraft Maintenance Planning Failure Data Analysis for Aircraft Maintenance Planning M. Tozan, A. Z. Al-Garni, A. M. Al-Garni, and A. Jamal Aerospace Engineering Department King Fahd University of Petroleum and Minerals Abstract This

More information

Journal of Human Sport and Exercise E-ISSN: Universidad de Alicante España

Journal of Human Sport and Exercise E-ISSN: Universidad de Alicante España Journal of Human Sport and Exercise E-ISSN: 1988-5202 jhse@ua.es Universidad de Alicante España SOÓS, ISTVÁN; FLORES MARTÍNEZ, JOSÉ CARLOS; SZABO, ATTILA Before the Rio Games: A retrospective evaluation

More information

Pairwise Comparison Models: A Two-Tiered Approach to Predicting Wins and Losses for NBA Games

Pairwise Comparison Models: A Two-Tiered Approach to Predicting Wins and Losses for NBA Games Pairwise Comparison Models: A Two-Tiered Approach to Predicting Wins and Losses for NBA Games Tony Liu Introduction The broad aim of this project is to use the Bradley Terry pairwise comparison model as

More information

Aquaculture Technology - PBBT301 UNIT I - MARINE ANIMALS IN AQUACULTURE

Aquaculture Technology - PBBT301 UNIT I - MARINE ANIMALS IN AQUACULTURE Aquaculture Technology - PBBT301 UNIT I - MARINE ANIMALS IN AQUACULTURE PART A 1. Define aquaculture. 2. Write two objectives of aquaculture? 3. List the types of aquaculture. 4. What is monoculture? 5.

More information

INFLUENCE OF DANCE ELEMENTS ON BALANCE BEAM RESULTS

INFLUENCE OF DANCE ELEMENTS ON BALANCE BEAM RESULTS INFLUENCE OF DANCE ELEMENTS ON BALANCE BEAM RESULTS Sunčica Delaš Kalinski 1, Ana Božanić 1 and Almir Atiković 2 1 Faculty of Kinesiology, University of Split, Croatia 2 Faculty of physical education and

More information

THE STATISTICAL AND GEOGRAPHICAL ANALYSIS ON THE IMPACTS OF SOCIOECONOMIC CHARACTERISTICS ON BUS-STOP DAILY BOARDING IN RICHMOND CITY

THE STATISTICAL AND GEOGRAPHICAL ANALYSIS ON THE IMPACTS OF SOCIOECONOMIC CHARACTERISTICS ON BUS-STOP DAILY BOARDING IN RICHMOND CITY Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2015 THE STATISTICAL AND GEOGRAPHICAL ANALYSIS ON THE IMPACTS OF SOCIOECONOMIC CHARACTERISTICS ON BUS-STOP

More information

Smart-Walk: An Intelligent Physiological Monitoring System for Smart Families

Smart-Walk: An Intelligent Physiological Monitoring System for Smart Families Smart-Walk: An Intelligent Physiological Monitoring System for Smart Families P. Sundaravadivel 1, S. P. Mohanty 2, E. Kougianos 3, V. P. Yanambaka 4, and M. K. Ganapathiraju 5 University of North Texas,

More information

AP Statistics Midterm Exam 2 hours

AP Statistics Midterm Exam 2 hours AP Statistics Midterm Exam 2 hours Name Directions: Work on these sheets only. Read each question carefully and answer completely but concisely (point values are from 1 to 3 points so no written answer

More information

Neighborhood Influences on Use of Urban Trails

Neighborhood Influences on Use of Urban Trails Neighborhood Influences on Use of Urban Trails Greg Lindsey, Yuling Han, Jeff Wilson Center for Urban Policy and the Environment Indiana University Purdue University Indianapolis Objectives Present new

More information

Pressured Applied by the Emergency/Israeli Bandage

Pressured Applied by the Emergency/Israeli Bandage Pressured Applied by the Emergency/Israeli Bandage By Charles S. Lessard, Ph.D. Nolan Shipman, M.D. Amanda Bickham Jasper Butler 9 December 2007 1 Introduction At the request of Performance Systems, this

More information

Name May 3, 2007 Math Probability and Statistics

Name May 3, 2007 Math Probability and Statistics Name May 3, 2007 Math 341 - Probability and Statistics Long Exam IV Instructions: Please include all relevant work to get full credit. Encircle your final answers. 1. An article in Professional Geographer

More information

Economic Value of Celebrity Endorsements:

Economic Value of Celebrity Endorsements: Economic Value of Celebrity Endorsements: Tiger Woods Impact on Sales of Nike Golf Balls September 27, 2012 On Line Appendix The Golf Equipments Golf Bags Golf bags are designed to transport the golf clubs

More information

PREDICTIVE CONTRIBUTION OF MORPHOLOGICAL CHARACTERISTICS AND MOTOR ABILITIES ON THE RESULT OF RUNNING THE 60m HURDLES IN BOYS AGED YEARS 1

PREDICTIVE CONTRIBUTION OF MORPHOLOGICAL CHARACTERISTICS AND MOTOR ABILITIES ON THE RESULT OF RUNNING THE 60m HURDLES IN BOYS AGED YEARS 1 International Journal of Science Culture and Sport June 2014; 2(2) ISSN : 2148-1148 Doi : 10.14486/IJSCS84 PREDICTIVE CONTRIBUTION OF MORPHOLOGICAL CHARACTERISTICS AND MOTOR ABILITIES ON THE RESULT OF

More information

MGB 203B Homework # LSD = 1 1

MGB 203B Homework # LSD = 1 1 MGB 0B Homework # 4.4 a α =.05: t = =.05 LSD = α /,n k t.05, 7 t α /,n k MSE + =.05 700 + = 4.8 n i n j 0 0 i =, j = 8.7 0.4 7. i =, j = 8.7.7 5.0 i =, j = 0.4.7. Conclusion: µ differs from µ and µ. b

More information

HSIS. Association of Selected Intersection Factors With Red-Light-Running Crashes. State Databases Used SUMMARY REPORT

HSIS. Association of Selected Intersection Factors With Red-Light-Running Crashes. State Databases Used SUMMARY REPORT HSIS HIGHWAY SAFETY INFORMATION SYSTEM The Highway Safety Information Systems (HSIS) is a multi-state safety data base that contains accident, roadway inventory, and traffic volume data for a select group

More information

Temporal and spatial analyses of rear-end crashes at signalized intersections

Temporal and spatial analyses of rear-end crashes at signalized intersections Accident Analysis and Prevention 38 (2006) 1137 1150 Temporal and spatial analyses of rear-end crashes at signalized intersections Xuesong Wang, Mohamed Abdel-Aty Department of Civil & Environmental Engineering,

More information

Analysis of Signalized Intersection Crashes

Analysis of Signalized Intersection Crashes Analysis of Signalized Intersection Crashes Authors: Nasima F. Bhuiyan Emelinda M. Parentela, Ph.D, P.E. Outline An overview of signalized intersections and accidents Purpose of the study Methodology Crash

More information

Statistical Measures

Statistical Measures Statistical Measures Question Paper 2 Level IGCSE Subject Maths Exam Board Edexcel Topic Handling Data Statistics Sub Topic Statistical Measures(Mean, Median, Mode) Booklet Question Paper 2 Time Allowed:

More information

Unit4: Inferencefornumericaldata 4. ANOVA. Sta Spring Duke University, Department of Statistical Science

Unit4: Inferencefornumericaldata 4. ANOVA. Sta Spring Duke University, Department of Statistical Science Unit4: Inferencefornumericaldata 4. ANOVA Sta 101 - Spring 2016 Duke University, Department of Statistical Science Dr. Çetinkaya-Rundel Slides posted at http://bit.ly/sta101_s16 Outline 1. Housekeeping

More information

Reminders. Homework scores will be up by tomorrow morning. Please me and the TAs with any grading questions by tomorrow at 5pm

Reminders. Homework scores will be up by tomorrow morning. Please  me and the TAs with any grading questions by tomorrow at 5pm Reminders Homework scores will be up by tomorrow morning Please email me and the TAs with any grading questions by tomorrow at 5pm 1 Chapter 12: Describing Distributions with Numbers Aaron Zimmerman STAT

More information

a) List and define all assumptions for multiple OLS regression. These are all listed in section 6.5

a) List and define all assumptions for multiple OLS regression. These are all listed in section 6.5 Prof. C. M. Dalton ECN 209A Spring 2015 Practice Problems (After HW1, HW2, before HW3) CORRECTED VERSION Question 1. Draw and describe a relationship with heteroskedastic errors. Support your claim with

More information