Master of Applied Statistics Applied Statistics Comprehensive Exam Computer Output January 2018
|
|
|
- Miranda Ford
- 5 years ago
- Views:
Transcription
1 Question 1a output Master of Applied Statistics Applied Statistics Comprehensive Exam Computer Output January 2018 Question 1f output Basic Statistical Measures Location Variability Mean Std Deviation Median Variance Mode. Range Interquartile Range Tests for Location: Mu0=0 Test Statistic p Value Student's t t Pr > t Sign M -1 Pr >= M Signed Rank S Pr >= S Page 1 of 9
2 Question 3 Output The SAS System Dependent Variable: WebCAPE DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total R-Square Coeff Var Root MSE WebCAPE Mean DF Type I SS Mean Square F Value Pr > F Level DF Type III SS Mean Square F Value Pr > F Level Level Least Squares Means Adjustment for Multiple Comparisons: Tukey-Kramer WebCAPE LSMEAN LSMEAN Number Elementary Intermediate Novice Least Squares Means for effect Level Pr > t for H0: LSMean(i)=LSMean(j) Dependent Variable: WebCAPE i/j Page 2 of 9
3 Question 3 Output Continued Level Least Squares Means Adjustment for Multiple Comparisons: Bonferroni WebCAPE LSMEAN LSMEAN Number Elementary Intermediate Novice Least Squares Means for effect Level Pr > t for H0: LSMean(i)=LSMean(j) Dependent Variable: WebCAPE i/j Level Least Squares Means Adjustment for Multiple Comparisons: Scheffe WebCAPE LSMEAN LSMEAN Number Elementary Intermediate Novice Least Squares Means for effect Level Pr > t for H0: LSMean(i)=LSMean(j) Dependent Variable: WebCAPE i/j Page 3 of 9
4 Question 4 Output DATA crop_yield; INPUT variety $ fertilizer $ CARDS; var_a fert_ var_a fert_ var_a fert_ var_a fert_ var_a fert_ var_a fert_ var_b fert_ var_b fert_ var_b fert_ var_b fert_ var_b fert_ var_b fert_ var_c fert_ var_c fert_ var_c fert_ var_c fert_ var_c fert_ var_c fert_ ; PROC GLM DATA=crop_yield ORDER=DATA; CLASS variety fertilizer; MODEL yield = variety fertilizer variety*fertilizer; RUN; Dependent Variable: yield DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE yield Mean DF Type I SS Mean Square F Value Pr > F variety fertilizer <.0001 variety*fertilizer DF Type III SS Mean Square F Value Pr > F variety fertilizer <.0001 variety*fertilizer Page 4 of 9
5 Question 4 Output Continued RANDOM fertilizer variety*fertilizer; Type III Expected Mean Square variety Var(Error) + 3 Var(variety*fertilizer) + Q(variety) fertilizer Var(Error) + 3 Var(variety*fertilizer) + 9 Var(fertilizer) variety*fertilizer Var(Error) + 3 Var(variety*fertilizer) RANDOM variety variety*fertilizer; Type III Expected Mean Square variety Var(Error) + 3 Var(variety*fertilizer) + 6 Var(variety) fertilizer Var(Error) + 3 Var(variety*fertilizer) + Q(fertilizer) variety*fertilizer Var(Error) + 3 Var(variety*fertilizer) RANDOM variety fertilizer variety*fertilizer; Type III Expected Mean Square variety Var(Error) + 3 Var(variety*fertilizer) + 6 Var(variety) fertilizer Var(Error) + 3 Var(variety*fertilizer) + 9 Var(fertilizer) variety*fertilizer Var(Error) + 3 Var(variety*fertilizer) Page 5 of 9
6 Question 5 Output DATA brains; INPUT logbrain logweight gest litter species $; CARDS; Deer_mouse_III Deer_mouse_IV House_mouse Deer_mouse_II Deer_mouse_I Hampster_I Elephant_shrew_I Rat_I Flying_squirrel Elephant_shrew_II Pygmy_gerbil Hampster_II Tree_shrew Hopping_mouse Gentle_lemur Tree_squirrel Rat_II Chinchilla Bush_baby Acouchis Hedgehog Guinea_pig Slow_loris Linkajou Lemur Aardvark Domestic_cat Agoutis Jack_rabbit Bat-eared_fox Quokka Ring-tail_monkey Long-nose_armadillo Gray_fox Hyrax Vervet_guenon Nutria Raccoon White_handed_gibbon Leaf_monkey Rhesus_monkey_I Red_fox Badger Porcupine_III Howler_monkey Spider_monkey_II Dog Rhesus_monkey_II Spider_monkey_I Porcupine_I Lynx Duikers Porcupine_II Barking_deer Page 6 of 9
7 Question 5 Output Continued Canadian_beaver Hamadryas_baboon Beaver Capybara Domestic_goat Western_baboon Orangutan Black_buck_antilope Chimpanzee Vicuna Leopard Domestic_sheep ; PROC GLM DATA=brains; MODEL logbrain = logweight; RUN; Dependent Variable: logbrain DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE logbrain Mean DF Type I SS Mean Square F Value Pr > F logweight <.0001 DF Type III SS Mean Square F Value Pr > F logweight <.0001 Parameter Estimate Standard Error t Value Pr > t Intercept <.0001 logweight <.0001 Page 7 of 9
8 Question 5 Output Continued PROC GLM DATA=brains; MODEL logbrain = logweight litter gest; RUN; Dependent Variable: logbrain DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total R-Square Coeff Var Root MSE logbrain Mean DF Type I SS Mean Square F Value Pr > F logweight <.0001 litter <.0001 gest DF Type III SS Mean Square F Value Pr > F logweight <.0001 litter gest Parameter Estimate Standard Error t Value Pr > t Intercept <.0001 logweight <.0001 litter gest Page 8 of 9
9 Question 6 Output Parameter DF Estimate Standard Error Analysis Of Maximum Likelihood Parameter Estimates Wald 95% Confidence Limits Wald Chi-Square Pr > ChiSq Intercept Minutes Scale Page 9 of 9
fruitfly fecundity example summary Tuesday, July 17, :13:19 PM 1
fruitfly fecundity example summary Tuesday, July 17, 2018 02:13:19 PM 1 The UNIVARIATE Procedure Variable: fecund line = NS Basic Statistical Measures Location Variability Mean 33.37200 Std Deviation 8.94201
EXST7034 Multiple Regression Geaghan Chapter 11 Bootstrapping (Toluca example) Page 1
Chapter 11 Bootstrapping (Toluca example) Page 1 Toluca Company Example (Problem from Neter, Kutner, Nachtsheim & Wasserman 1996,1.21) A particular part needed for refigeration equipment replacement parts
Análisis de la varianza (II)
01:43 Thursday, March 10, 2011 1 AGRO 6005 Análisis de la varianza (II) 1. Evaluación de supuestos. Los siguientes datos son recuentos de un áfido en trigo en 6 semanas diferentes. En cada ocasión se muestrearon
The PRINCOMP Procedure
Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, 2010 1 Food production variables The PRINCOMP Procedure Observations 16 Variables 4 Simple Statistics PRECIP ndvi aet temp Mean 260.8102476
Stat 301 Lecture 30. Model Selection. Explanatory Variables. A Good Model. Response: Highway MPG Explanatory: 13 explanatory variables
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan 1 Explanatory Variables Engine size (liters) Cylinders (number)
Stat 401 B Lecture 31
Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan 1 Explanatory Variables Engine size (liters) Cylinders (number)
Model Information Data Set. Response Variable (Events) Summe Response Variable (Trials) N Response Distribution Binomial Link Function
02:32 Donnerstag, November 03, 2016 1 Model Information Data Set WORK.EXP Response Variable (Events) Summe Response Variable (Trials) N Response Distribution inomial Link Function Logit Variance Function
HASIL OUTPUT SPSS. Reliability Scale: ALL VARIABLES
139 HASIL OUTPUT SPSS Reliability Scale: ALL VARIABLES Case Processing Summary N % 100 100.0 Cases Excluded a 0.0 Total 100 100.0 a. Listwise deletion based on all variables in the procedure. Reliability
Important Formulas. Discrete Probability Distributions. Probability and Counting Rules. The Normal Distribution. Confidence Intervals and Sample Size
blu38582_if_1-8.qxd 9/27/10 9:19 PM Page 1 Important Formulas Chapter 3 Data Description Mean for individual data: Mean for grouped data: Standard deviation for a sample: X2 s X n 1 or Standard deviation
tool<-read.csv(file="d:/chilo/regression 7/tool.csv", header=t) tool
Regression nalysis lab 7 1 Indicator variables 1.1 Import data tool
Booklet of Code and Output for STAD29/STA 1007 Final Exam
Booklet of Code and Output for STAD29/STA 1007 Final Exam List of Figures in this document by page: List of Figures 1 Raisins data.............................. 2 2 Boxplot of raisin data........................
Lampiran IV. Hasil Output SPSS Versi 16.0 untuk Analisis Deskriptif
182 Lampiran IV. Hasil Output SPSS Versi 16.0 untuk Analisis Deskriptif Frequencies Statistics Kinerja Guru Sikap Guru Thdp Kepsek Motivasi Kerja Guru Kompetensi Pedagogik Guru N Valid 64 64 64 64 Missing
Statistics for Social Research
Facoltà di Scienze della Formazione, Scienze Politiche e Sociali Statistics for Social Research Lesson 2: Descriptive Statistics Prof.ssa Monica Palma a.a. 2016-2017 DESCRIPTIVE STATISTICS How do we describe
DRIVING PERFORMANCE PROFILES OF DRIVERS WITH PARKINSON S DISEASE
14th International Conference Mobility and Transport for Elderly and Disabled Persons Lisbon, Portugal, 28-31 July 2015 DRIVING PERFORMANCE PROFILES OF DRIVERS WITH PARKINSON S DISEASE Dimosthenis Pavlou
SEM over time. Changes in Structure, changes in Means
SEM over time Changes in Structure, changes in Means Measuring at two time points Is the structure the same Do the means change (is there growth) Create the data x.model
Lecture 2. Review of Linear Regression I Statistics Statistical Methods II. Presented January 9, 2018
Review of Linear Regression I Statistics 211 - Statistical Methods II Presented January 9, 2018 Estimation of The OLS under normality the OLS Dan Gillen Department of Statistics University of California,
Basic SAS and R for HLM
Basic SAS and R for HLM Edps/Psych/Soc 589 Carolyn J. Anderson Department of Educational Psychology c Board of Trustees, University of Illinois Spring 2019 Overview The following will be demonstrated in
Appendix B STATISTICAL TABLES OVERVIEW
Appendix B STATISTICAL TABLES OVERVIEW Table B.1: Proportions of the Area Under the Normal Curve Table B.2: 1200 Two-Digit Random Numbers Table B.3: Critical Values for Student s t-test Table B.4: Power
TRINITY COLLEGE DUBLIN THE UNIVERSITY OF DUBLIN. Faculty of Engineering, Mathematics and Science. School of Computer Science and Statistics
ST7003-1 TRINITY COLLEGE DUBLIN THE UNIVERSITY OF DUBLIN Faculty of Engineering, Mathematics and Science School of Computer Science and Statistics Postgraduate Certificate in Statistics Hilary Term 2015
Guatemalan cholesterol example summary
Guatemalan cholesterol example summary Wednesday, July 11, 2018 02:04:06 PM 1 The UNIVARIATE Procedure Variable: level = rural Basic Statistical Measures Location Variability Mean 157.0204 Std Deviation
TABLE 4.1 POPULATION OF 100 VALUES 2
TABLE 4. POPULATION OF 00 VALUES WITH µ = 6. AND = 7.5 8. 6.4 0. 9.9 9.8 6.6 6. 5.7 5. 6.3 6.7 30.6.6.3 30.0 6.5 8. 5.6 0.3 35.5.9 30.7 3.. 9. 6. 6.8 5.3 4.3 4.4 9.0 5.0 9.9 5. 0.8 9.0.9 5.4 7.3 3.4 38..6
delivery<-read.csv(file="d:/chilo/regression 4/delivery.csv", header=t) delivery
Regression Analysis lab 4 1 Model Adequacy Checking 1.1 Import data delivery
Cumulative Frequency Diagrams Question Paper 1
Frequency Diagrams Question Paper 1 Level Subject Exam Board IGCSE Maths Edexcel Topic Handling Data Statistics Sub Topic Frequency Diagrams(Graphical representation of data) Booklet Question Paper 1 Time
LAMPIRAN 1. Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari Februari 2012
LAMPIRAN 1 Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari 2011 29 Februari 2012 No Tanggal Indeks Harga Saham No Tanggal Indeks Harga Saham 1 20-Jan-011 2.35 138 05-Agst-011 1.95 2
TRY OUT 25 Responden Variabel Kepuasan / x1
1 TRY OUT 25 Responden Variabel Kepuasan / x1 Case Processing Summary N % 25 100.0 Cases Excluded a 0.0 Total 25 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
UJI VALIDITAS DAN RELIABILIAS VARIABEL KOMPENSASI
1 UJI VALIDITAS DAN RELIABILIAS VARIABEL KOMPENSASI Case Processing Summary N % 20 100.0 Cases Excluded a 0.0 Total 20 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
Descriptive Statistics Practice Problems (99-04)
Descriptive Statistics Practice Problems (99-04) 1. One thousand candidates sit an examination. The distribution of marks is shown in the following grouped frequency table. Marks 1 10 11 20 21 30 31 40
TRY OUT 30 Responden Variabel Kompetensi/ x1
1 TRY OUT 30 Responden Variabel Kompetensi/ x1 Case Processing Summary N % 30 100.0 Cases Excluded a 0.0 Total 30 100.0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics
Oregon DOT Slow-Speed Weigh-in-Motion (SWIM) Project: Analysis of Initial Weight Data
Portland State University PDXScholar Center for Urban Studies Publications and Reports Center for Urban Studies 7-1997 Oregon DOT Slow-Speed Weigh-in-Motion (SWIM) Project: Analysis of Initial Weight Data
Modeling Ignition Delay in a Diesel Engine
Modeling Ignition Delay in a Diesel Engine Ivonna D. Ploma Introduction The object of this analysis is to develop a model for the ignition delay in a diesel engine as a function of four experimental variables:
Example #1: One-Way Independent Groups Design. An example based on a study by Forster, Liberman and Friedman (2004) from the
Example #1: One-Way Independent Groups Design An example based on a study by Forster, Liberman and Friedman (2004) from the Journal of Personality and Social Psychology illustrates the SAS/IML program
DEPARTMENT OF STATISTICS AND DEMOGRAPHY MAIN EXAMINATION, 2011/12 STATISTICAL INFERENCE II ST232 TWO (2) HOURS. ANSWER ANY mree QUESTIONS
I.. UNIVERSITY OF SWAZILAND Page 1 of3 DEPARTMENT OF STATISTICS AND DEMOGRAPHY, MAIN EXAMINATION, 2011/12 COURSE TITLE: STATISTICAL INFERENCE II COURSE CODE: ST232 TIME ALLOWED: TWO (2) HOURS INSTRUCTION:
. Enter. Model Summary b. Std. Error. of the. Estimate. Change. a. Predictors: (Constant), Emphaty, reliability, Assurance, responsive, Tangible
LAMPIRAN Variables Entered/Removed b Variables Model Variables Entered Removed Method 1 Emphaty, reliability, Assurance, responsive, Tangible a. Enter a. All requested variables entered. b. Dependent Variable:
Motor Trend MPG Analysis
Motor Trend MPG Analysis SJ May 15, 2016 Executive Summary For this project, we were asked to look at a data set of a collection of cars in the automobile industry. We are going to explore the relationship
R-Sq criterion Data : Surgical room data Chap 9
Chap 9 - For controlled experiments model reduction is not very important. P 347 - For exploratory observational studies, model reduction is important. Criteria for model selection p353 R-Sq criterion
DATA PENELITIAN 1. CAR CAR (%)
DATA PENELITIAN. CAR No. Tahun Nama Bank CAR (%) Arta Niaga Kencana 2,8 2 Artha Graha 0,58 3 Asiatic -9,9 4 Danpac 25,74 5 Global International 42, 6 Harmoni 7,47 7 IFI 22,62 8 Bukopin 20,37 9 International
Statistics and Quantitative Analysis U4320. Segment 8 Prof. Sharyn O Halloran
Statistics and Quantitative Analysis U4320 Segment 8 Prof. Sharyn O Halloran I. Introduction A. Overview 1. Ways to describe, summarize and display data. 2.Summary statements: Mean Standard deviation Variance
LAMPIRAN. Lampiran 1. Data deviden untuk menghitung economic performance tahun
LAMPIRAN Lampiran 1. Data deviden untuk menghitung economic performance tahun 2011-2013 No Kode 2011 2012 2013 Div Div Div 1 SMCB 23 32 48 2 UNVR 250 300 300 3 AMFG 80 80 80 4 INTP 263 293 450 5 ICBP 116
Motor Trend Yvette Winton September 1, 2016
Motor Trend Yvette Winton September 1, 2016 Executive Summary Objective In this analysis, the relationship between a set of variables and miles per gallon (MPG) (outcome) is explored from a data set of
LAMPIRAN 1. Lampiran Nama dan Kondisi Perusahaan Textile No Kode Nama Perusahaan Hasil z-score FD Non-FD
87 LAMPIRAN 1. Lampiran Nama dan Kondisi Perusahaan Textile 2010-2014 No Kode Nama Perusahaan Hasil z-score FD Non-FD 1 ADMG PT Polychem Indonesia Tbk 1,39 1 2 ARGO PT Argo Pantes Tbk 0,93 1 3 CTNX PT
Some Robust and Classical Nonparametric Procedures of Estimations in Linear Regression Model
Some Robust and Classical Nonparametric Procedures of Estimations in Linear Regression Model F.B. Adebola, Ph.D.; E.I. Olamide, M.Sc. * ; and O.O. Alabi, Ph.D. Department of Statistics, Federal University
Technical Papers supporting SAP 2009
Technical Papers supporting SAP 29 A meta-analysis of boiler test efficiencies to compare independent and manufacturers results Reference no. STP9/B5 Date last amended 25 March 29 Date originated 6 October
Drilling Example: Diagnostic Plots
Math 3080 1. Treibergs Drilling Example: Diagnostic Plots Name: Example March 1, 2014 This data is taken from Penner & Watts, Mining Information, American Statistician 1991, as quoted by Levine, Ramsey
female male help("predict") yhat age
30 40 50 60 70 female male 1.0 help("predict") 0.5 yhat 0.0 0.5 1.0 30 40 50 60 70 age 30 40 50 60 70 1.5 1.0 female male help("predict") 0.5 yhat 0.0 0.5 1.0 1.5 30 40 50 60 70 age 2 Wald Statistics Response:
Review of Upstate Load Forecast Uncertainty Model
Review of Upstate Load Forecast Uncertainty Model Arthur Maniaci Supervisor, Load Forecasting & Energy Efficiency New York Independent System Operator Load Forecasting Task Force June 17, 2011 Draft for
The Dynamics of Plug-in Electric Vehicles in the Secondary Market
The Dynamics of Plug-in Electric Vehicles in the Secondary Market Dr. Gil Tal gtal@ucdavis.edu Dr. Tom Turrentine Dr. Mike Nicholas Sponsored by the California Air Resources Board Population and Sampling
THERMOELECTRIC SAMPLE CONDITIONER SYSTEM (TESC)
THERMOELECTRIC SAMPLE CONDITIONER SYSTEM (TESC) FULLY AUTOMATED ASTM D2983 CONDITIONING AND TESTING ON THE CANNON TESC SYSTEM WHITE PAPER A critical performance parameter for transmission, gear, and hydraulic
Lampiran 1. Data Perusahaan
Lampiran. Data Perusahaan NO PERUSH MV EARN DIV CFO LB.USAHA TOT.ASS ACAP 3 9 8 5 369 9678 376 ADES 75-35 - 6 3559-5977 7358 3 AQUA 5 368 65 335 797 678 53597 BATA 88 5 9 863 958 93 5 BKSL 5.3 -. 9-9 5
Volvo City Safety loss experience a long-term update
Highway Loss Data Institute Bulletin Vol. 32, No. 1 : April 2015 Volvo City Safety loss experience a long-term update This Highway Loss Data Institute (HLDI) report updates two prior bulletins on the Volvo
Tifton, Georgia: Oat Grain Performance, Yield 1
Tifton, Georgia: Average Average Rank Wt Ht Lodg. Survival Date Horizon 201 139.9 132.2 1 155.9 30.5 54 0 100 04/11 Horizon 270 128.8 119.4 4 138.0 32.2 42 0 100 04/13 Plot Spike LA9339 124.4 115.5 9 127.3
5. CONSTRUCTION OF THE WEIGHT-FOR-LENGTH AND WEIGHT-FOR- HEIGHT STANDARDS
5. CONSTRUCTION OF THE WEIGHT-FOR-LENGTH AND WEIGHT-FOR- HEIGHT STANDARDS 5.1 Indicator-specific methodology The construction of the weight-for-length (45 to 110 cm) and weight-for-height (65 to 120 cm)
ME101, Final Examination, sample. Closed Book Examination (with the exception of one 3 x5 cards of handwritten notes, both sides, allowed)
, sample Name Closed Book Examination (with the exception of one 3 x5 cards of handwritten notes, both sides, allowed) Only calculators allowed for calculations: Hewlett Packard: The HP 33s and HP 35s
Lampiran 1. Penjualan PT Honda Mandiri Bogor
LAMPIRAN 64 Lampiran 1. Penjualan PT Honda Mandiri Bogor 29-211 PENJUALAN 29 TYPE JAN FEB MAR APR MEI JUNI JULI AGT SEP OKT NOV DES TOTA JAZZ 16 14 22 15 23 19 13 28 15 28 3 25 248 FREED 23 25 14 4 13
Analyzing Crash Risk Using Automatic Traffic Recorder Speed Data
Analyzing Crash Risk Using Automatic Traffic Recorder Speed Data Thomas B. Stout Center for Transportation Research and Education Iowa State University 2901 S. Loop Drive Ames, IA 50010 stouttom@iastate.edu
The application of the 95% Confidence interval with ISAT and IMAGE
Confidence intervals are commonly applied in many fields using statistical analyses. The most commonly seen usage of confidence intervals is within political polls. In the case of a political poll, an
DATA SAMPEL TAHUN 2006
DATA SAMPEL TAHUN 2006 No Nama Emiten CGPI Kode Saham Harga Saham EPS PER Laba Bersih 1 Bank Niaga 89.27 BNGA 920 54 17.02 647,732 2 Bank Mandiri 83.66 BMRI 2,900 118 24.65 2,422,472 3 Astra International
Author s Accepted Manuscript
Author s Accepted Manuscript Dataset on statistical analysis of Jet A-1 fuel laboratory properties for on-spec into-plane operations Aderibigbe Israel Adekitan, Tobi Shomefun, Temitope M. John, Bukola
SAN PEDRO BAY PORTS YARD TRACTOR LOAD FACTOR STUDY Addendum
SAN PEDRO BAY PORTS YARD TRACTOR LOAD FACTOR STUDY Addendum December 2008 Prepared by: Starcrest Consulting Group, LLC P.O. Box 434 Poulsbo, WA 98370 TABLE OF CONTENTS 1.0 EXECUTIVE SUMMARY...2 1.1 Background...2
In the Slow Lane: ZEV Markets in California, June 2014 to June 2017
In the Slow Lane: ZEV Markets in California, June 2014 to June 2017 STEPs Symposium Ken Kurani Plug-in Hybrid & Electric Vehicle Center Institute of Transportation Studies University of California, Davis
Inheritance of chinch bug resistance in grain pearl millet
Inheritance of chinch bug resistance in grain pearl millet Andrea Maas 1 * and Xinzhi Ni 1,2 1. Soybean Production Research, Monsanto Company, 36142 E. State Route 10, PO Box 33, Mason City, IL 62664,
International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November ISSN
International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 1970 Engineering Analysis of the Abouhenidi Gas Station in Yanbu Albahar Masters Project PREPARED BY Hamad
Regression Models Course Project, 2016
Regression Models Course Project, 2016 Venkat Batchu July 13, 2016 Executive Summary In this report, mtcars data set is explored/analyzed for relationship between outcome variable mpg (miles for gallon)
ANALYSIS OF TRAFFIC SPEEDS IN NEW YORK CITY. Austin Krauza BDA 761 Fall 2015
ANALYSIS OF TRAFFIC SPEEDS IN NEW YORK CITY Austin Krauza BDA 761 Fall 2015 Problem Statement How can Amazon Web Services be used to conduct analysis of large scale data sets? Data set contains over 80
Start Time. LOCATION: Scotts Valley Dr QC JOB #: SPECIFIC LOCATION: 0 ft from Tabor St. DIRECTION: EB/WB CITY/STATE: Scotts Valley, CA
Tube Counts Type of report: Tube Count - Volume Data LOCATION: Scotts Valley Dr QC JOB #: 245667 SPECIFIC LOCATION: ft from Tabor St DIRECTION: EB/WB CITY/STATE: Scotts Valley, CA DATE: Mar 2 24 - Mar
Verifying the accuracy of involute gear measuring machines R.C. Frazer and J. Hu Design Unit, Stephenson Building, University ofnewcastle upon Tyne,
Verifying the accuracy of involute gear measuring machines R.C. Frazer and J. Hu Design Unit, Stephenson Building, University ofnewcastle upon Tyne, Abstract This paper describes the most common methods
Investigating the Concordance Relationship Between the HSA Cut Scores and the PARCC Cut Scores Using the 2016 PARCC Test Data
Investigating the Concordance Relationship Between the HSA Cut Scores and the PARCC Cut Scores Using the 2016 PARCC Test Data A Research Report Submitted to the Maryland State Department of Education (MSDE)
ESSAYS ESSAY B ESSAY A and 2009 are given below:
ESSAYS -7- -8- Suggested time: 5 minutes Maximum score: 120 points ESSAY A Suggested time: 5 minutes Maximum score: 120 points I. Define a time series and its components. Discuss the importance and the
FINAL REPORT AP STATISTICS CLASS DIESEL TRUCK COUNT PROJECT
FINAL REPORT AP STATISTICS CLASS 2017-2018 DIESEL TRUCK COUNT PROJECT Authors: AP Statistics Class 2017-2018 Table of Contents SURVEY QUESTION...p. 2 AIR QUALITY...p. 3-4 TOTAL TRUCK COUNTS.p. 5 TRUCK
Identification of Contributing Factors for Work Zone Crashes
Identification of Contributing Factors for Work Zone Crashes Qing Wang Jian John Lu Zhenyu Wang Transportation Group Department of Civil and Environmental Engineering University of South Florida November
1 - Inventory regime changes: An application of Hamilton s state space model
GLOBAL INSIGHT S WORLD ECONOMIC OUTLOOK CONFERENCE Washington D.C., October 26 1 - Inventory regime changes: An application of Hamilton s state space model 2 Steel Demand Trends: To focus on main drivers
Passenger seat belt use in Durham Region
Facts on Passenger seat belt use in Durham Region June 2017 Highlights In 2013/2014, 85 per cent of Durham Region residents 12 and older always wore their seat belt when riding as a passenger in a car,
Descriptive Statistics
Chapter 2 Descriptive Statistics 2-1 Overview 2-2 Summarizing Data 2-3 Pictures of Data 2-4 Measures of Central Tendency 2-5 Measures of Variation 2-6 Measures of Position 2-7 Exploratory Data Analysis
9.3 Tests About a Population Mean (Day 1)
Bellwork In a recent year, 73% of first year college students responding to a national survey identified being very well off financially as an important personal goal. A state university finds that 132
Effect of Sample Size and Method of Sampling Pig Weights on the Accuracy of Estimating the Mean Weight of the Population 1
Effect of Sample Size and Method of Sampling Pig Weights on the Accuracy of Estimating the Mean Weight of the Population C. B. Paulk, G. L. Highland 2, M. D. Tokach, J. L. Nelssen, S. S. Dritz 3, R. D.
MOTORCYCLE ACCIDENT MODEL ON THE ROAD SECTION OF HIGHLANDS REGION BY USING GENELARIZED LINEAR MODEL
International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 10, October 2017, pp. 1249-1258 1248, Article ID: IJCIET_08_10_127 Available online at http://http://www.iaeme.com/ijciet/issues.asp?jtype=ijciet&vtype=8&itype=10
PREFACE. Date : Director General, Jammu & Kashmir
PREFACE National Statistical Commission constituted a committee under the Chairmanship of Prof. R. Radhakrishna on pooling of central and state samples of National Sample Surveys (NSS) to identify the
LAMPIRAN UJI VALIDITAS
LAMPIRAN UJI VALIDITAS 1 e1 e2,00 Q1,04 Q2,01 -,04 e3 Q3,00 -,21 -,09 e4 Q4,49 -,01 e5 Q5,70,50,71 e6 Q6,47,69,74 e7 Q7,54,41,08 e8 Q8,17 e9 Q9,01 e10 Q10 Kompetensi Guru Chi-Square = 78,430 Probabilitas
Road Surface characteristics and traffic accident rates on New Zealand s state highway network
Road Surface characteristics and traffic accident rates on New Zealand s state highway network Robert Davies Statistics Research Associates http://www.statsresearch.co.nz Joint work with Marian Loader,
Concrete (63) PROFICIENCY TESTING PROGRAM REPORT
www.labsmartservices.com.au Concrete - 2016 (63) PROFICIENCY TESTING PROGRAM REPORT Accredited for compliance with ISO/IEC 17043 Copyright: LabSmart Services Pty Ltd Copyright: LabSmart Services Pty Ltd
Algebra 2 Plus, Unit 10: Making Conclusions from Data Objectives: S- CP.A.1,2,3,4,5,B.6,7,8,9; S- MD.B.6,7
Algebra 2 Plus, Unit 10: Making Conclusions from Data Objectives: S- CP.A.1,2,3,4,5,B.6,7,8,9; S- MD.B.6,7 Learner Levels Level 1: I can simulate an experiment. Level 2: I can interpret two- way tables.
LAMPIRAN I Data Perusahaan Sampel kode DPS EPS Ekuitas akpi ,97 51,04 40,
LAMPIRAN I Data Perusahaan Sampel kode DPS EPS Ekuitas 2013 2014 2015 2013 2014 2015 2013 2014 2015 akpi 34 8 9 50,97 51,04 40,67 1.029.336.000.000 1.035.846.000.000 1.107.566.000.000 asii 216 216 177
Jarrell Independent School District
Jarrell Independent School District Demographic Study January 22 nd, 2014 Learn from Yesterday Understand Today Plan for Tomorrow Economic Conditions From 2000 to 2013, Texas outperformed the U.S. job
Crop Stage at Each Application A B C Crop Name: ZEAMX ZEAMX ZEAMX Height (In.): 12-15(14) 21-27(24) Stage (L): V5-6(6)8L V8 11L
Date Planted: 5/4/07 Row Spacing: 30 IN Variety: DKC 52-59 No. of Reps: 4 Population: 30,000 S/A % OM: 2.0 Soil Type: Sandy Loam ph: 7.3 Plot Size: 10 X 35 FT Design: RANDOMIZED COMPLETE BLOCK Tillage:
Lecture 3: Measure of Central Tendency
Lecture 3: Measure of Central Tendency Donglei Du (ddu@unb.edu) Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton E3B 9Y2 Donglei Du (UNB) ADM 2623: Business Statistics
DEFECT DISTRIBUTION IN WELDS OF INCOLOY 908
PSFC/RR-10-8 DEFECT DISTRIBUTION IN WELDS OF INCOLOY 908 Jun Feng August 10, 2010 Plasma Science and Fusion Center Massachusetts Institute of Technology Cambridge, MA 02139, USA This work was supported
FRICTION POTENTIAL AND SAFETY : PREDICTION OF
BRITE EURAM PROJECT BRPR CT97 0461 2 ND INTERNATIONAL COLLOQUIUM ON VEHICLE TYRE ROAD INTERACTION FRICTION POTENTIAL AND SAFETY : PREDICTION OF HANDLING BEHAVIOR FLORENCE, FEBRUARY 23 rd 2001 Title : Correlations
Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR
Tutorial 1 Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR Dataset for running Correlated Component Regression This tutorial 1 is based on data provided by Michel Tenenhaus and
An Introduction to Partial Least Squares Regression
An Introduction to Partial Least Squares Regression Randall D. Tobias, SAS Institute Inc., Cary, NC Abstract Partial least squares is a popular method for soft modelling in industrial applications. This
Oat. Tifton, Georgia: Oat Grain Performance, Data 2-Year Average 3 Rank Yield 1 Wt Ht Lodg.
Oat Brand-Variety Tifton, Georgia: 2 Test 3 Rank Horizon 201 104.5 100.5 16 71.0 27.2 46 14 03/31 SS 76-50 100.4 96.6 9 79.9 28.0 37 3 04/05 Gerard 224 96.9 97.8 8 81.1 29.8 42 1 04/04 Horizon 306 94.5
IVEware Analysis Example Replication C10
IVEware Analysis Example Replication C10 * IVEware Analysis Examples Replication for ASDA 2nd Edition * Berglund April 2017 * Chapter 10 ; libname ncsr "P:\ASDA 2\Data sets\ncsr\" ; *set options and location
Lampiran 1. Tabel Sampel Penelitian
Lampiran 1 Tabel Sampel Penelitian No Kode Emiten Nama Perusahaan Tanggal IPO 1 APLN Agung Podomoro Land Tbk 11 Nov 2010 2 ASRI Alam Sutera Reality Tbk 18 Dec 2007 3 BAPA Bekasi Asri Pemula Tbk 14 Jan
Trial report. Variety testing of. Poa pratensis, Festuca rubra, Lolium perenne, Dactylis glomerata and Festuca arundinacea. Second year harvest
Trial report Variety testing of Poa pratensis, Festuca rubra, Lolium perenne, Dactylis glomerata and Festuca arundinacea Second year harvest AGRONOVA LC Field Trials 2007 Agronova Møllevej 15-17 4140 Borup
Oat. Tifton, Georgia: Oat Grain Performance,
Oat Tifton, Georgia: An oat variety grain trial was planted at this location on September 23, 2015. However, crown rust disease and lodging during the growing season resulted in some very low grain yields
MONTHLY NEW RESIDENTIAL SALES, APRIL 2017
FOR RELEASE AT 10:00 AM EDT, TUESDAY, MAY 23, MONTHLY NEW RESIDENTIAL SALES, APRIL Release Number: CB17-80 May 23, - The U.S. Census Bureau and the U.S. Department of Housing and Urban Development jointly
EFFICACY/CROP TOLERANCE PERFORMANCE FORM Page 1 of 69 Tolerance Study AAFC15-068T-200 Indaziflam / Lowbush Blueberry
EFFICACY/CROP TOLERANCE PERFORMANCE FORM Page 1 of 69 Project title: Crop Tolerance of Lowbush Blueberry to Alion Herbicide (indaziflam) Over Two Years. Sprouting and Harvest Years. Final Report Project
QUALITY ASSURANCE & LAB ACCREDITATION
TOP 9 QUALITY ASSURANCE & LAB ACCREDITATION Control Charts Accreditation of laboratories 6./7.Dec 2011 Workshop ANKARA TOP 9 Quality 1 TOP 9 QUALITY ASSURANCE & LAB ACCREDITATION Control Charts Accreditation
Performance of the Mean- and Variance-Adjusted ML χ 2 Test Statistic with and without Satterthwaite df Correction
FORDHAM UNIVERSITY THE JESUIT UNIVERSITY OF NEW YORK Performance of the Mean- and Variance-Adjusted ML χ 2 Test Statistic with and without Satterthwaite df Correction Jonathan M. Lehrfeld Heining Cham
namibia UniVERSITY OF SCIEnCE AnD TECHnOLOGY FACULTY OF HEALTH AND APPLIED SCIENCES DEPARTMENT OF MATHEMATICS AND STATISTICS MARKS: 100
namibia UniVERSITY OF SCIEnCE AnD TECHnOLOGY FACULTY OF HEALTH AND APPLIED SCIENCES DEPARTMENT OF MATHEMATICS AND STATISTICS QUALIFICATION: BACHELOR OF ECONOMICS -., QUALIFICATION CODE: 7BAMS LEVEL: 7
Fall Hint: criterion? d) Based measure of spread? Solution. Page 1
Question #1 (12 Marks) The following are the golf scores of 12 members of a women s golf team in tournament play: 89 90 87 95 86 81 102 105 83 88 91 79 Hint: n 1 x 2 i x 67 74.3979 a) Present the distribution
MONTHLY NEW RESIDENTIAL SALES, SEPTEMBER 2018
FOR RELEASE AT 10:00 AM EDT, WEDNESDAY, OCTOBER 24, MONTHLY NEW RESIDENTIAL SALES, SEPTEMBER Release Number: CB18 160 October 24, The U.S. Census Bureau and the U.S. Department of Housing and Urban Development