LAMPIRAN. Tests of Normality. Kolmogorov-Smirnov a. Berat_Limfa KB KP P

Size: px
Start display at page:

Download "LAMPIRAN. Tests of Normality. Kolmogorov-Smirnov a. Berat_Limfa KB KP P"

Transcription

1 LAMPIRAN 1. Data Analisis Statistik 1.1 Berat Limpa U1 U2 U3 U4 U5 U6 Rata- SD Rata KB KP P P P Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistik df Sig. Statistik df Sig. KB KP P P P * a. Lilliefors Significce Correction *. This is a lower bound of the true significce. Test of Homogeneity of Varice Levene Statistik df1 df2 Sig. Based on Me Based on Medi Based on Medi d with adjusted df Based on trimmed me

2 Trsform Pertama (natural log) Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistik df Sig. Statistik df Sig. KB KP P P P * a. Lilliefors Significce Correction *. This is a lower bound of the true significce. Test of Homogeneity of Varice Levene Statistik df1 df2 Sig. Based on Me Based on Medi Based on Medi d with adjusted df Based on trimmed me Trsform Kedua (reciprocal) Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistik df Sig. Statistik df Sig. KB KP P P P * a. Lilliefors Significce Correction *. This is a lower bound of the true significce.

3 Test of Homogeneity of Varice Levene Statistik df1 df2 Sig. Based on Me Based on Medi Based on Medi d with adjusted df Based on trimmed me Trsform Ketiga (square root) Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistik df Sig. Statistik df Sig. KB KP P P P * a. Lilliefors Significce Correction *. This is a lower bound of the true significce. Test of Homogeneity of Varice Levene Statistik df1 df2 Sig. Based on Me Based on Medi Based on Medi d with adjusted df Based on trimmed me

4 Kruskal-Wallis Test Rks N Me Rk KB KP P P P Total 30 Test Statistiks a,b Chi-Square df 4 Asymp. Sig..296 a. Kruskal Wallis Test b. Grouping Variable: Mn-Whitney Test Rks N Me Rk Sum of Rks KB KP

5 Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable: Mn-Whitney Test Rks N Me Rk Sum of Rks KB P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable:

6 Mn-Whitney Test Rks N Me Rk Sum of Rks KB P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable: Mn-Whitney Test Rks N Me Rk Sum of Rks KB P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a

7 Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable: Mn-Whitney Test Rks N Me Rk Sum of Rks KP P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable:

8 Mn-Whitney Test Rks N Me Rk Sum of Rks KP P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable: Mn-Whitney Test Rks N Me Rk Sum of Rks KP P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a

9 Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable: Mn-Whitney Test Rks N Me Rk Sum of Rks P P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable:

10 Mn-Whitney Test Rks N Me Rk Sum of Rks P P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable: Mn-Whitney Test Rks N Me Rk Sum of Rks P P Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a

11 Mn-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed) a b. Grouping Variable: 1.2 Data Jumlah Sel Raksasa U1 U2 U3 U4 U5 U6 Ratarata SD KB KP P P P Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistik df Sig. Statistik df Sig. Jumlah_Sel_Raksasa KB KP * P * P P * a. Lilliefors Significce Correction *. This is a lower bound of the true significce.

12 Test of Homogeneity of Varice Levene Statistik df1 df2 Sig. Jumlah_Sel_Raksasa Based on Me Based on Medi Based on Medi d with adjusted df Based on trimmed me Oneway ANOVA Jumlah_Sel_Raksasa Sum of Squares df Me Square F Sig. Between Groups Within Groups Total Post Hoc Tests Jumlah_Sel_Raksasa Bonferroni Multiple Comparisons (I) (J) 95% Confidence Interval Me Difference (I-J) Std. Error Sig. Lower Bound Upper Bound KB KP P P P * KP KB P P P *

13 P1 KB KP P P P2 KB KP P P P3 KB * KP * P P *. The me difference is significt at the 0.05 level. 1.3 Diameter Sel Raksasa U1 U2 U3 U4 U5 U6 Rata- Rata KB KP P P P SD Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistik df Sig. Statistik df Sig. Diameter_Sel_Raksasa KB * KP * P P * P *

14 Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistik df Sig. Statistik df Sig. Diameter_Sel_Raksasa KB * KP * P P * P * a. Lilliefors Significce Correction *. This is a lower bound of the true significce. Test of Homogeneity of Varice Levene Statistik df1 df2 Sig. Diameter_Sel_Raksasa Based on Me Based on Medi Based on Medi d with adjusted df Based on trimmed me Oneway ANOVA Diameter_Sel_Raksasa Sum of Squares df Me Square F Sig. Between Groups Within Groups Total

15 Post Hoc Tests Multiple Comparisons Diameter_Sel_Raksasa Bonferroni (I) (J) 95% Confidence Interval Me Difference (I-J) Std. Error Sig. Lower Bound Upper Bound KB KP P P P KP KB P P P P1 KB KP P P P2 KB KP P P P3 KB KP P P

Eksamen ERN4110, 6/ VEDLEGG SPSS utskrifter til oppgavene (Av plasshensyn kan utskriftene være noe redigert)

Eksamen ERN4110, 6/ VEDLEGG SPSS utskrifter til oppgavene (Av plasshensyn kan utskriftene være noe redigert) Eksamen ERN4110, 6/9-2018 VEDLEGG SPSS utskrifter til oppgavene (Av plasshensyn kan utskriftene være noe redigert) 1 Oppgave 1 Datafila I SPSS: Variabelnavn Beskrivelse Kjønn Kjønn (1=Kvinne, 2=Mann) Studieinteresse

More information

APPENDIX. Appendix 2. HE Staining Examination Result: Distribution of of BALB/c

APPENDIX. Appendix 2. HE Staining Examination Result: Distribution of of BALB/c APPENDIX Appendix 2. HE Staining Examination Result: Distribution of of BALB/c mice nucleus liver cells changes in percents between control group and intervention groups. Descriptives Groups Statistic

More information

Perpustakaan Unika LAMPIRAN

Perpustakaan Unika LAMPIRAN LAMPIRAN Lampiran 1. Hasil Penelitian Pendahuluan Tabel Hasil Pengukuran Absorbansi Ekstrak Monascus purpureus Hari ke- Media Air Tajin MEB 5 0.4792 0.2744 6 0.6469 0.3695 7 0.6974 0.4817 8 0.6534 0.4661

More information

E-Campus Inferential Statistics - Part 2

E-Campus Inferential Statistics - Part 2 E-Campus Inferential Statistics - Part 2 Group Members: James Jones Question 4-Isthere a significant difference in the mean prices of the stores? New Textbook Prices New Price Descriptives 95% Confidence

More information

TABEL DISTRIBUSI DAN HUBUNGAN LENGKUNG RAHANG DAN INDEKS FASIAL N MIN MAX MEAN SD

TABEL DISTRIBUSI DAN HUBUNGAN LENGKUNG RAHANG DAN INDEKS FASIAL N MIN MAX MEAN SD TABEL DISTRIBUSI DAN HUBUNGAN LENGKUNG RAHANG DAN INDEKS FASIAL Lengkung Indeks fasial rahang Euryprosopic mesoprosopic leptoprosopic Total Sig. n % n % n % n % 0,000 Narrow 0 0 0 0 15 32,6 15 32,6 Normal

More information

Lampiran 6 HASIL STATISTIK

Lampiran 6 HASIL STATISTIK Lampiran 6 HASIL STATISTIK Usia 11.37 of.450 Median 12.00 Mode 12 Std. Deviation 3.488 Minimum 2 Maximum 16 usia Frequency Valid Valid 2 2 3.3 3.3 3.3 4 2 3.3 3.3 6.7 6 2 3.3 3.3 10.0 7 4 6.7 6.7 16.7

More information

Set up of the data is similar to the Randomized Block Design situation. A. Chang 1. 1) Setting up the data sheet

Set up of the data is similar to the Randomized Block Design situation. A. Chang 1. 1) Setting up the data sheet Repeated Measure Analysis (Univariate Mixed Effect Model Approach) (Treatment as the Fixed Effect and the Subject as the Random Effect) (This univariate approach can be used for randomized block design

More information

LAMPIRAN 1 : DATA HASIL PENELITIAN

LAMPIRAN 1 : DATA HASIL PENELITIAN LAMPIRAN 1 : DATA HASIL PENELITIAN SKPD SDM KOMUNIKASI SARANA KOMITMEN MOTIVASI RATA 43 15 74 42 64 78 52,6666667 47 14 66 40 50 80 49,5 55 15 61 40 56 87 52,3333333 49 12 50 41 58 87 49,5 44 12 49 30

More information

LAMPIRAN B ANALISIS DATA

LAMPIRAN B ANALISIS DATA 100 116 LAMPIRAN B ANALISIS DATA 101 117 Kemandirian Belajar NPAR TESTS /K-S(NORMAL)= /MISSING ANALYSIS. NPar Tests[DataSet0] One-Sample Kolmogorov-Smirnov Test N 91 Normal Parameters a Mean 111.0769 Std.

More information

Hypermarket Retail Analysis Customer Buying Behavior. Reachout Analytics Client Sample Report

Hypermarket Retail Analysis Customer Buying Behavior. Reachout Analytics Client Sample Report Hypermarket Retail Analysis Customer Buying Behavior Report Tools Used: R Python WEKA Techniques Applied: Comparesion Tests Association Tests Requirement 1: All the Store Brand significance to Gender Towards

More information

for statistical analyses

for statistical analyses Using for statistical analyses Robert Bauer Warnemünde, 05/16/2012 Day 6 - Agenda: non-parametric alternatives to t-test and ANOVA (incl. post hoc tests) Wilcoxon Rank Sum/Mann-Whitney U-Test Kruskal-Wallis

More information

LAMPIRAN Hubungan Job..., Dian Tri Utami, F.PSI UI, 2008

LAMPIRAN Hubungan Job..., Dian Tri Utami, F.PSI UI, 2008 LAMPIRA Case Processing Summary a. Listwise deleti based all variables in the procedure. % Crbach's Alpha Items a of Items,812 815 4 Case Processing Summary % Crbach's Alpha Items of Items,671,654 4 Case

More information

FREQUENCIES VARIABLES=CAT_MSDS /STATISTICS=STDDEV MINIMUM MAXIMUM MEAN MEDIAN MODE /ORDER=ANALYSIS.

FREQUENCIES VARIABLES=CAT_MSDS /STATISTICS=STDDEV MINIMUM MAXIMUM MEAN MEDIAN MODE /ORDER=ANALYSIS. 1. Uji Univariat FREQUENCIES VARIABLES=CAT_MSDS /STATISTICS=STDDEV MINIMUM MAXIMUM MEAN MEDIAN MODE /ORDER=ANALYSIS. Frequencies Notes Output Created 31-MAY-2017 20:53:29 Comments Input Data Active Dataset

More information

Enter your UID and password. Make sure you have popups allowed for this site.

Enter your UID and password. Make sure you have popups allowed for this site. Log onto: https://apps.csbs.utah.edu/ Enter your UID and password. Make sure you have popups allowed for this site. You may need to go to preferences (right most tab) and change your client to Java. I

More information

Product Catalog. AcaStat. Software

Product Catalog. AcaStat. Software Product Catalog AcaStat Software AcaStat AcaStat is an inexpensive and easy-to-use data analysis tool. Easily create data files or import data from spreadsheets or delimited text files. Run crosstabulations,

More information

FreeJSTAT for Windows. Manual

FreeJSTAT for Windows. Manual FreeJSTAT for Windows Manual (c) Copyright Masato Sato, 1998-2018 1 Table of Contents 1. Introduction 3 2. Functions List 6 3. Data Input / Output 7 4. Summary Statistics 8 5. t-test 9 6. ANOVA 10 7. Contingency

More information

SPSS. (Statistical Packages for the Social Sciences)

SPSS. (Statistical Packages for the Social Sciences) Inger Persson SPSS (Statistical Packages for the Social Sciences) SHORT INSTRUCTIONS This presentation contains only relatively short instructions on how to perform basic statistical calculations in SPSS.

More information

5:2 LAB RESULTS - FOLLOW-UP ANALYSES FOR FACTORIAL

5:2 LAB RESULTS - FOLLOW-UP ANALYSES FOR FACTORIAL 5:2 LAB RESULTS - FOLLOW-UP ANALYSES FOR FACTORIAL T1. n F and n C for main effects = 2 + 2 + 2 = 6 (i.e., 2 observations in each of 3 cells for other factor) Den t = SQRT[3.333x(1/6+1/6)] = 1.054 Den

More information

LAMPIRAN. Sampel Penelitian

LAMPIRAN. Sampel Penelitian LAMPIRAN Lampiran 1 Daftar Perusahaan Sampel Penelitian No. Kode Kriteria Perusahaan 1 2 3 4 Sampel 1 ADES 1 2 AISA 2 3 ALTO 4 CEKA 5 DAVO 6 DLTA 3 7 ICBP 4 8 INDF 5 9 MLBI 6 10 MYOR 11 PSDN 7 12 ROTI

More information

CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening

CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening CDAA No. 4 - Part Two - Multiple Regression - Initial Data Screening Variables Entered/Removed b Variables Entered GPA in other high school, test, Math test, GPA, High school math GPA a Variables Removed

More information

Why is Statistics important in Bioinformatics?

Why is Statistics important in Bioinformatics? Why is Statistics important in Bioinformatics? Random processes are inherent in evolution and in sampling (data collection). Errors are often unavoidable in the data collection process. Statistics helps

More information

Interval Estimation. The data set belongs to the MASS package, which has to be pre-loaded into the R workspace prior to use.

Interval Estimation. The data set belongs to the MASS package, which has to be pre-loaded into the R workspace prior to use. Interval Estimation It is a common requirement to efficiently estimate population parameters based on simple random sample data. In the R tutorials of this section, we demonstrate how to compute the estimates.

More information

Index. Bar charts, 106 bartlett.test function, 159 Bottles dataset, 69 Box plots, 113

Index. Bar charts, 106 bartlett.test function, 159 Bottles dataset, 69 Box plots, 113 Index A Add-on packages information page, 186 187 Linux users, 191 Mac users, 189 mirror sites, 185 Windows users, 187 aggregate function, 62 Analysis of variance (ANOVA), 152 anova function, 152 as.data.frame

More information

Correlations. Butir 1 Pearson Correlation ** Sig. (2-tailed) N

Correlations. Butir 1 Pearson Correlation ** Sig. (2-tailed) N 109 Lampiran olahan Data. 1. Nilai Pelanggan (X1) s Butir 1 Butir 2 Butir 3 Butir Total Butir 1 1.049 -.157.441 ** Sig. (2-tailed).626.120.000 N 100 100 100 100 Butir 2.049 1 -.003.735 ** Sig. (2-tailed).626.978.000

More information

The ctest Package. January 3, 2000

The ctest Package. January 3, 2000 R objects documented: The ctest Package January 3, 2000 bartlett.test....................................... 1 binom.test........................................ 2 cor.test.........................................

More information

Cluster Randomization Create Cluster Means Dataset

Cluster Randomization Create Cluster Means Dataset Chapter 270 Cluster Randomization Create Cluster Means Dataset Introduction A cluster randomization trial occurs when whole groups or clusters of individuals are treated together. Examples of such clusters

More information

Bluman & Mayer, Elementary Statistics, A Step by Step Approach, Canadian Edition

Bluman & Mayer, Elementary Statistics, A Step by Step Approach, Canadian Edition Bluman & Mayer, Elementary Statistics, A Step by Step Approach, Canadian Edition Online Learning Centre Technology Step-by-Step - Minitab Minitab is a statistical software application originally created

More information

Regression. Notes. Page 1 25-JAN :21:57. Output Created Comments

Regression. Notes. Page 1 25-JAN :21:57. Output Created Comments /STATISTICS COEFF OUTS CI(95) R ANOVA /CRITERIA=PIN(.05) POUT(.10) /DEPENDENT Favorability /METHOD=ENTER zcontemp ZAnxious6 zallcontact. Regression Notes Output Created Comments Input Missing Value Handling

More information

ANSWERS -- Prep for Psyc350 Laboratory Final Statistics Part Prep a

ANSWERS -- Prep for Psyc350 Laboratory Final Statistics Part Prep a ANSWERS -- Prep for Psyc350 Laboratory Final Statistics Part Prep a Put the following data into an spss data set: Be sure to include variable and value labels and missing value specifications for all variables

More information

GraphPad Prism Features

GraphPad Prism Features GraphPad Prism Features GraphPad Prism 4 is available for both Windows and Macintosh. The two versions are very similar. You can open files created on one platform on the other platform with no special

More information

1. Crosstabs a. Usia*jenis kelamin

1. Crosstabs a. Usia*jenis kelamin 1. Crosstabs a. Usia*jenis kelamin usia * jenis_kelamin Valid Missing Percent Percent Percent 27 100.0% 0.0% 27 100.0% usia * jenis_kelamin Crosstabulation usia >=30 tahun 31-40 tahun 41-50 tahun

More information

Statistical Research Consultants Bangladesh (SRCBD) Testing for Normality using SPSS

Statistical Research Consultants Bangladesh (SRCBD)   Testing for Normality using SPSS Testing for Normality using SPSS An assessment of the normality of data is a prerequisite for many statistical tests because normal data is an underlying assumption in parametric testing. There are two

More information

Correlations. Correlations

Correlations. Correlations LAMPIRAN 3 UJI VALIDITAS ketergantun ketergantun ketergantun ketergantun ketergantun gan1 gan2 gan3 gan4 gan5 total1 ketergantungan1 Pearson 1.470(**).179.223.299.614(**) Sig. (2-tailed).009.343.236.109.000

More information

CELL PHONE USAGE WHILE DRIVING INFLUENCE ON DRIVER'S REACTION TIME

CELL PHONE USAGE WHILE DRIVING INFLUENCE ON DRIVER'S REACTION TIME XII International Symposium "ROAD ACCIDENTS PREVENTION 2014" Hotel Jezero, Borsko Jezero, 09 th and 10 th October 2014. UDK: CELL PHONE USAGE WHILE DRIVING INFLUENCE ON DRIVER'S REACTION TIME Igor Milanović

More information

THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533. Time: 50 minutes 40 Marks FRST Marks FRST 533 (extra questions)

THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533. Time: 50 minutes 40 Marks FRST Marks FRST 533 (extra questions) THE UNIVERSITY OF BRITISH COLUMBIA FORESTRY 430 and 533 MIDTERM EXAMINATION: October 14, 2005 Instructor: Val LeMay Time: 50 minutes 40 Marks FRST 430 50 Marks FRST 533 (extra questions) This examination

More information

Crosstabs Notes Output Created 17-Mai :40:54 Comments Input

Crosstabs Notes Output Created 17-Mai :40:54 Comments Input Crosstabs Notes Output Created 17-Mai-2011 01:40:54 Comments Input Data /Users/corinnahornei/Desktop/spss table.sav Active Dataset DatenSet3 Filter Weight Split File N of Rows in Working 189 Data File

More information

Table Of Contents. Table Of Contents

Table Of Contents. Table Of Contents Statistics Table Of Contents Table Of Contents Basic Statistics... 7 Basic Statistics Overview... 7 Descriptive Statistics Available for Display or Storage... 8 Display Descriptive Statistics... 9 Store

More information

Data Management - Summary statistics - Graphics Choose a dataset to work on, maybe use it already

Data Management - Summary statistics - Graphics Choose a dataset to work on, maybe use it already Exercises Day 1 Data Management - Summary statistics - Graphics Choose a dataset to work on, maybe use it already Day 2 Inference introduction R Data Management - Summary statistics - Graphics Days 3 and

More information

Constructing Statistical Tolerance Limits for Non-Normal Data. Presented by Dr. Neil W. Polhemus

Constructing Statistical Tolerance Limits for Non-Normal Data. Presented by Dr. Neil W. Polhemus Constructing Statistical Tolerance Limits for Non-Normal Data Presented by Dr. Neil W. Polhemus Statistical Tolerance Limits Consider a sample of n observations taken from a continuous population. {X 1,

More information

On the Analysis of Experimental Results in Evolutionary Computation

On the Analysis of Experimental Results in Evolutionary Computation On the Analysis of Experimental Results in Evolutionary Computation Stjepan Picek Faculty of Electrical Engineering and Computing Unska 3, Zagreb, Croatia Email:stjepan@computer.org Marin Golub Faculty

More information

Land Cover Stratified Accuracy Assessment For Digital Elevation Model derived from Airborne LIDAR Dade County, Florida

Land Cover Stratified Accuracy Assessment For Digital Elevation Model derived from Airborne LIDAR Dade County, Florida Land Cover Stratified Accuracy Assessment For Digital Elevation Model derived from Airborne LIDAR Dade County, Florida FINAL REPORT Submitted October 2004 Prepared by: Daniel Gann Geographic Information

More information

One way ANOVA when the data are not normally distributed (The Kruskal-Wallis test).

One way ANOVA when the data are not normally distributed (The Kruskal-Wallis test). One way ANOVA when the data are not normally distributed (The Kruskal-Wallis test). Suppose you have a one way design, and want to do an ANOVA, but discover that your data are seriously not normal? Just

More information

Forfattere Intro to SPSS 19.0 Description

Forfattere Intro to SPSS 19.0 Description Forfattere Nicholas Fritsche Rasmus Porsgaard Casper Voigt Rasmussen Martin Klint Hansen Morten Christoffersen Ulrick Tøttrup Niels Yding Sørensen Morten Mondrup Andreassen Jesper Pedersen Intro to SPSS

More information

Statgraphics Centurion Version 17 Enhancements

Statgraphics Centurion Version 17 Enhancements Statgraphics Centurion Version 17 Enhancements Version 17 of Statgraphics Centurion contains many significant enhancements to the program. These enhancements include: 1. 32 new statistical procedures.

More information

SPSS INSTRUCTION CHAPTER 9

SPSS INSTRUCTION CHAPTER 9 SPSS INSTRUCTION CHAPTER 9 Chapter 9 does no more than introduce the repeated-measures ANOVA, the MANOVA, and the ANCOVA, and discriminant analysis. But, you can likely envision how complicated it can

More information

6:1 LAB RESULTS -WITHIN-S ANOVA

6:1 LAB RESULTS -WITHIN-S ANOVA 6:1 LAB RESULTS -WITHIN-S ANOVA T1/T2/T3/T4. SStotal =(1-12) 2 + + (18-12) 2 = 306.00 = SSpill + SSsubj + SSpxs df = 9-1 = 8 P1 P2 P3 Ms Ms-Mg 1 8 15 8.0-4.0 SSsubj= 3x(-4 2 + ) 4 17 15 12.0 0 = 96.0 13

More information

SPSS Modules Features

SPSS Modules Features SPSS Modules Features Core System Functionality (included in every license) Data access and management Data Prep features: Define Variable properties tool; copy data properties tool, Visual Bander, Identify

More information

STAT:5201 Applied Statistic II

STAT:5201 Applied Statistic II STAT:5201 Applied Statistic II Two-Factor Experiment (one fixed blocking factor, one fixed factor of interest) Randomized complete block design (RCBD) Primary Factor: Day length (short or long) Blocking

More information

Organizing Your Data. Jenny Holcombe, PhD UT College of Medicine Nuts & Bolts Conference August 16, 3013

Organizing Your Data. Jenny Holcombe, PhD UT College of Medicine Nuts & Bolts Conference August 16, 3013 Organizing Your Data Jenny Holcombe, PhD UT College of Medicine Nuts & Bolts Conference August 16, 3013 Learning Objectives Identify Different Types of Variables Appropriately Naming Variables Constructing

More information

Assumption 1: Groups of data represent random samples from their respective populations.

Assumption 1: Groups of data represent random samples from their respective populations. Tutorial 6: Comparing Two Groups Assumptions The following methods for comparing two groups are based on several assumptions. The type of test you use will vary based on whether these assumptions are met

More information

Sta$s$cs & Experimental Design with R. Barbara Kitchenham Keele University

Sta$s$cs & Experimental Design with R. Barbara Kitchenham Keele University Sta$s$cs & Experimental Design with R Barbara Kitchenham Keele University 1 Comparing two or more groups Part 5 2 Aim To cover standard approaches for independent and dependent groups For two groups Student

More information

Minitab Study Card J ENNIFER L EWIS P RIESTLEY, PH.D.

Minitab Study Card J ENNIFER L EWIS P RIESTLEY, PH.D. Minitab Study Card J ENNIFER L EWIS P RIESTLEY, PH.D. Introduction to Minitab The interface for Minitab is very user-friendly, with a spreadsheet orientation. When you first launch Minitab, you will see

More information

STATS PAD USER MANUAL

STATS PAD USER MANUAL STATS PAD USER MANUAL For Version 2.0 Manual Version 2.0 1 Table of Contents Basic Navigation! 3 Settings! 7 Entering Data! 7 Sharing Data! 8 Managing Files! 10 Running Tests! 11 Interpreting Output! 11

More information

Joe Swintek Badger Technical Services. June 6, 2018

Joe Swintek Badger Technical Services. June 6, 2018 StatCharrms: An R Package for Statistical Analysis of Chemistry, Histopathology, and Reproduction Endpoints Including Repeated Measures and Multi Generation Studies Joe Swintek Badger Technical Services

More information

A Methodology for Analyzing the Performance of Genetic Based Machine Learning by Means of Non-Parametric Tests

A Methodology for Analyzing the Performance of Genetic Based Machine Learning by Means of Non-Parametric Tests A Methodology for Analyzing the Performance of Genetic Based Machine Learning by Means of Non-Parametric Tests Salvador García salvagl@decsai.ugr.es Department of Computer Science and Artificial Intelligence,

More information

Contrasts and Multiple Comparisons

Contrasts and Multiple Comparisons Contrasts and Multiple Comparisons /* onewaymath.sas */ title2 'Oneway with contrasts and multiple comparisons (Exclude Other/DK)'; %include 'readmath.sas'; if ethnic ne 6; /* Otherwise, throw the case

More information

Within-Cases: Multivariate approach part one

Within-Cases: Multivariate approach part one Within-Cases: Multivariate approach part one /* sleep2.sas */ options linesize=79 noovp formdlim=' '; title "Student's Sleep data: Matched t-tests with proc reg"; data bedtime; infile 'studentsleep.data'

More information

Conducting a Path Analysis With SPSS/AMOS

Conducting a Path Analysis With SPSS/AMOS Conducting a Path Analysis With SPSS/AMOS Download the PATH-INGRAM.sav data file from my SPSS data page and then bring it into SPSS. The data are those from the research that led to this publication: Ingram,

More information

DataSet2. <none> <none> <none>

DataSet2. <none> <none> <none> GGraph Notes Output Created 09-Dec-0 07:50:6 Comments Input Active Dataset Filter Weight Split File DataSet Syntax Resources N of Rows in Working Data File Processor Time Elapsed Time 77 GGRAPH /GRAPHDATASET

More information

Laboratory Topics 1 & 2

Laboratory Topics 1 & 2 PLS205 Lab 1 January 12, 2012 Laboratory Topics 1 & 2 Welcome, introduction, logistics, and organizational matters Introduction to SAS Writing and running programs; saving results; checking for errors

More information

R commander an Introduction

R commander an Introduction R commander an Introduction Natasha A. Karp nk3@sanger.ac.uk May 2010 Preface This material is intended as an introductory guide to data analysis with R commander. It was produced as part of an applied

More information

Lampiran 1 Data Sampel Penelitian. Kode Nama Sektor/Sub Sektor

Lampiran 1 Data Sampel Penelitian. Kode Nama Sektor/Sub Sektor Lampiran Data Sampel Penelitian Kode Nama Sektor/Sub Sektor AALI Astra Agro Lestari Tbk Pertanian/Perkebunan ADRO Adaro Energy Tbk Pertambangan/Batubara ASII Astra International Tbk Aneka Industri/Otomotif

More information

The Kenton Study. (Applied Linear Statistical Models, 5th ed., pp , Kutner et al., 2005) Page 1 of 5

The Kenton Study. (Applied Linear Statistical Models, 5th ed., pp , Kutner et al., 2005) Page 1 of 5 The Kenton Study The Kenton Food Company wished to test four different package designs for a new breakfast cereal. Twenty stores, with approximately equal sales volumes, were selected as the experimental

More information

R commander an Introduction

R commander an Introduction R commander an Introduction Natasha A. Karp nk3@sanger.ac.uk May 2010 Preface This material is intended as an introductory guide to data analysis with R commander. It was produced as part of an applied

More information

An Example of Using inter5.exe to Obtain the Graph of an Interaction

An Example of Using inter5.exe to Obtain the Graph of an Interaction An Example of Using inter5.exe to Obtain the Graph of an Interaction This example covers the general use of inter5.exe to produce data from values inserted into a regression equation which can then be

More information

Regression Lab 1. The data set cholesterol.txt available on your thumb drive contains the following variables:

Regression Lab 1. The data set cholesterol.txt available on your thumb drive contains the following variables: Regression Lab The data set cholesterol.txt available on your thumb drive contains the following variables: Field Descriptions ID: Subject ID sex: Sex: 0 = male, = female age: Age in years chol: Serum

More information

Want to Do a Better Job? - Select Appropriate Statistical Analysis in Healthcare Research

Want to Do a Better Job? - Select Appropriate Statistical Analysis in Healthcare Research Want to Do a Better Job? - Select Appropriate Statistical Analysis in Healthcare Research Liping Huang, Center for Home Care Policy and Research, Visiting Nurse Service of New York, NY, NY ABSTRACT The

More information

Bootstrapped and Means Trimmed One Way ANOVA and Multiple Comparisons in R

Bootstrapped and Means Trimmed One Way ANOVA and Multiple Comparisons in R Bootstrapped and Means Trimmed One Way ANOVA and Multiple Comparisons in R Another way to do a bootstrapped one-way ANOVA is to use Rand Wilcox s R libraries. Wilcox (2012) states that for one-way ANOVAs,

More information

PLS205 Lab 1 January 9, Laboratory Topics 1 & 2

PLS205 Lab 1 January 9, Laboratory Topics 1 & 2 PLS205 Lab 1 January 9, 2014 Laboratory Topics 1 & 2 Welcome, introduction, logistics, and organizational matters Introduction to SAS Writing and running programs saving results checking for errors Different

More information

Statistical Pattern Recognition

Statistical Pattern Recognition Statistical Pattern Recognition Features and Feature Selection Hamid R. Rabiee Jafar Muhammadi Spring 2012 http://ce.sharif.edu/courses/90-91/2/ce725-1/ Agenda Features and Patterns The Curse of Size and

More information

Laboratory for Two-Way ANOVA: Interactions

Laboratory for Two-Way ANOVA: Interactions Laboratory for Two-Way ANOVA: Interactions For the last lab, we focused on the basics of the Two-Way ANOVA. That is, you learned how to compute a Brown-Forsythe analysis for a Two-Way ANOVA, as well as

More information

Lab #9: ANOVA and TUKEY tests

Lab #9: ANOVA and TUKEY tests Lab #9: ANOVA and TUKEY tests Objectives: 1. Column manipulation in SAS 2. Analysis of variance 3. Tukey test 4. Least Significant Difference test 5. Analysis of variance with PROC GLM 6. Levene test for

More information

Statistics Lab #7 ANOVA Part 2 & ANCOVA

Statistics Lab #7 ANOVA Part 2 & ANCOVA Statistics Lab #7 ANOVA Part 2 & ANCOVA PSYCH 710 7 Initialize R Initialize R by entering the following commands at the prompt. You must type the commands exactly as shown. options(contrasts=c("contr.sum","contr.poly")

More information

Unit 1: The One-Factor ANOVA as a Generalization of the Two-Sample t Test

Unit 1: The One-Factor ANOVA as a Generalization of the Two-Sample t Test Minitab Notes for STAT 6305: Analysis of Variance Models Department of Statistics and Biostatistics CSU East Bay Unit 1: The One-Factor ANOVA as a Generalization of the Two-Sample t Test 1.1. Data and

More information

Lampiran 2 MASTER TABEL

Lampiran 2 MASTER TABEL 64 Lampiran 2 MASTER TABEL No. No. Responden Umur Pendidikan Pekerjaan Paritas Kanker 1 427363 35 S1 PNS 4 Tidak 2 504024 36 SMA IRT 4 Tidak 3 500316 35 SMA IRT 5 Tidak 4 504014 35 SMA PNS 1 Tidak 5 447158

More information

The following procedures and commands, are covered in this part: Command Purpose Page

The following procedures and commands, are covered in this part: Command Purpose Page Some Procedures in SPSS Part (2) This handout describes some further procedures in SPSS, following on from Part (1). Because some of the procedures covered are complex, with many sub-commands, the descriptions

More information

Lampiran 1. Peta Jalur Penelitian di Stasiun Penelitian Hutan Batang Toru Blok Barat Tapanuli Utara

Lampiran 1. Peta Jalur Penelitian di Stasiun Penelitian Hutan Batang Toru Blok Barat Tapanuli Utara 56 Lampiran 1. Peta Jalur Penelitian di Stasiun Penelitian Hutan Batang Toru Blok Barat Tapanuli Utara 57 Lampiran 2. Tabulasi Data No Individu Jam Kegiatan Item Jenis Tinggi Kanopi (meter) 1 Beta 7.44

More information

Descriptive Statistics, Standard Deviation and Standard Error

Descriptive Statistics, Standard Deviation and Standard Error AP Biology Calculations: Descriptive Statistics, Standard Deviation and Standard Error SBI4UP The Scientific Method & Experimental Design Scientific method is used to explore observations and answer questions.

More information

Conditional and Unconditional Regression with No Measurement Error

Conditional and Unconditional Regression with No Measurement Error Conditional and with No Measurement Error /* reg2ways.sas */ %include 'readsenic.sas'; title2 ''; proc reg; title3 'Conditional Regression'; model infrisk = stay census; proc calis cov; /* Analyze the

More information

Minitab 18 Feature List

Minitab 18 Feature List Minitab 18 Feature List * New or Improved Assistant Measurement systems analysis * Capability analysis Graphical analysis Hypothesis tests Regression DOE Control charts * Graphics Scatterplots, matrix

More information

Brief Guide on Using SPSS 10.0

Brief Guide on Using SPSS 10.0 Brief Guide on Using SPSS 10.0 (Use student data, 22 cases, studentp.dat in Dr. Chang s Data Directory Page) (Page address: http://www.cis.ysu.edu/~chang/stat/) I. Processing File and Data To open a new

More information

SPSS: AN OVERVIEW. V.K. Bhatia Indian Agricultural Statistics Research Institute, New Delhi

SPSS: AN OVERVIEW. V.K. Bhatia Indian Agricultural Statistics Research Institute, New Delhi SPSS: AN OVERVIEW V.K. Bhatia Indian Agricultural Statistics Research Institute, New Delhi-110012 The abbreviation SPSS stands for Statistical Package for the Social Sciences and is a comprehensive system

More information

The Solution to the Factorial Analysis of Variance

The Solution to the Factorial Analysis of Variance The Solution to the Factorial Analysis of Variance As shown in the Excel file, Howell -2, the ANOVA analysis (in the ToolPac) yielded the following table: Anova: Two-Factor With Replication SUMMARYCounting

More information

MINITAB Release Comparison Chart Release 14, Release 13, and Student Versions

MINITAB Release Comparison Chart Release 14, Release 13, and Student Versions Technical Support Free technical support Worksheet Size All registered users, including students Registered instructors Number of worksheets Limited only by system resources 5 5 Number of cells per worksheet

More information

Nonparametric Testing

Nonparametric Testing Nonparametric Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com

More information

Explorations on Web Usability

Explorations on Web Usability American Journal of Applied Sciences 6 (3): 424-429, 2009 ISSN 1546-9239 2009 Science Publications Explorations on Web Usability K.K Teoh, T.S. Ong, P.W. Lim, Rachel P.Y. Liong and C.Y. Yap Faculty of

More information

Statistical Pattern Recognition

Statistical Pattern Recognition Statistical Pattern Recognition Features and Feature Selection Hamid R. Rabiee Jafar Muhammadi Spring 2013 http://ce.sharif.edu/courses/91-92/2/ce725-1/ Agenda Features and Patterns The Curse of Size and

More information

Francisco Herrera.

Francisco Herrera. Data Mining i and Soft Computing Francisco Herrera Research Group on Soft Computing and Information Intelligent Systems (SCI 2 S) Dept. of Computer Science and A.I. University of Granada, Spain Email:

More information

Package ScottKnottESD

Package ScottKnottESD Type Package Package ScottKnottESD May 8, 2018 Title The Scott-Knott Effect Size Difference (ESD) Test Version 2.0.3 Date 2018-05-08 Author Chakkrit Tantithamthavorn Maintainer Chakkrit Tantithamthavorn

More information

Nonparametric Methods

Nonparametric Methods 1 Excel Manual Nonparametric Methods Chapter 15 In this chapter, Excel is used more as an organizer; the actual statistics will performed by hand. Excel is used to do the simple task of ranking data, determining

More information

SAS Example A10. Output Delivery System (ODS) Sample Data Set sales.txt. Examples of currently available ODS destinations: Mervyn Marasinghe

SAS Example A10. Output Delivery System (ODS) Sample Data Set sales.txt. Examples of currently available ODS destinations: Mervyn Marasinghe SAS Example A10 data sales infile U:\Documents\...\sales.txt input Region : $8. State $2. +1 Month monyy5. Headcnt Revenue Expenses format Month monyy5. Revenue dollar12.2 proc sort by Region State Month

More information

EXPLORATORY STUDIES ON NETWORK OPERATION OF FUZZY SIGNAL CONTROLLERS

EXPLORATORY STUDIES ON NETWORK OPERATION OF FUZZY SIGNAL CONTROLLERS EXPLORATORY STUDIES ON NETWORK OPERATION OF FUZZY SIGNAL CONTROLLERS ABSTRACT Michelle Andrade Federal University of Goiás Maria Alice Prudêncio Jacques University of Brasília Marcelo Ladeira University

More information

Hierarchical Loglinear Analysis. Design 1. Stairs, Escalators, and Obesity

Hierarchical Loglinear Analysis. Design 1. Stairs, Escalators, and Obesity Stairs, Escalators, and Obesity 1 HILOGLINEAR weight(1 3) direct(1 2) device(1 2) /CRITERIA ITERATION(20) DELTA(0) /PRINT=ASSOCIATION ESTIM /DESIGN. Note I have deleted some tables, some rows, some columns,

More information

Statistical Pattern Recognition

Statistical Pattern Recognition Statistical Pattern Recognition Features and Feature Selection Hamid R. Rabiee Jafar Muhammadi Spring 2014 http://ce.sharif.edu/courses/92-93/2/ce725-2/ Agenda Features and Patterns The Curse of Size and

More information

TI-83 Users Guide. to accompany. Statistics: Unlocking the Power of Data by Lock, Lock, Lock, Lock, and Lock

TI-83 Users Guide. to accompany. Statistics: Unlocking the Power of Data by Lock, Lock, Lock, Lock, and Lock TI-83 Users Guide to accompany by Lock, Lock, Lock, Lock, and Lock TI-83 Users Guide- 1 Getting Started Entering Data Use the STAT menu, then select EDIT and hit Enter. Enter data for a single variable

More information

LAST UPDATED: October 16, 2012 DISTRIBUTIONS PSYC 3031 INTERMEDIATE STATISTICS LABORATORY. J. Elder

LAST UPDATED: October 16, 2012 DISTRIBUTIONS PSYC 3031 INTERMEDIATE STATISTICS LABORATORY. J. Elder LAST UPDATED: October 16, 2012 DISTRIBUTIONS Acknowledgements 2 Some of these slides have been sourced or modified from slides created by A. Field for Discovering Statistics using R. LAST UPDATED: October

More information

arxiv: v1 [stat.co] 17 Jan 2015

arxiv: v1 [stat.co] 17 Jan 2015 JavaNPST: Nonparametric Statistical Tests in Java Joaquín Derrac 1, Salvador García 2, and Francisco Herrera 2 arxiv:1501.04222v1 [stat.co] 17 Jan 2015 1 Affectv: Affectv Limited, 33-34 Alfred Place, London,

More information

for Windows User guide Exeter Software Statistical software for biologists Version 3.3

for Windows User guide Exeter Software Statistical software for biologists Version 3.3 for Windows Statistical software for biologists Version 3.3 User guide F. James Rohlf Dennis E. Slice Department of Ecology and Evolution State University of New York Stony Brook, NY 11794 Exeter Software

More information

How to find a minimum spanning tree

How to find a minimum spanning tree Print How to find a minimum spanning tree Definitions Kruskal s algorithm Spanning tree example Definitions Trees A tree is a connected graph without any cycles. It can also be defined as a connected graph

More information

Info 2950, Lecture 16

Info 2950, Lecture 16 Info 2950, Lecture 16 28 Mar 2017 Prob Set 5: due Fri night 31 Mar Breadth first search (BFS) and Depth First Search (DFS) Must have an ordering on the vertices of the graph. In most examples here, the

More information