Perpustakaan Unika LAMPIRAN
|
|
- Bridget Caldwell
- 6 years ago
- Views:
Transcription
1 LAMPIRAN
2 Lampiran 1. Hasil Penelitian Pendahuluan Tabel Hasil Pengukuran Absorbansi Ekstrak Monascus purpureus Hari ke- Media Air Tajin MEB
3 Lampiran 2. Hasil Uji Anova Untuk Absorbansi Uji Normalitas Absrbnsi Tests of Normality Kolmogorov-Smirnov a *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Uji Homogenitas Shapiro-Wilk Statistic df Sig. Statistic df Sig * * * * * * * Test of Homogeneity of Variance Absrbnsi Based on Mean Based on Median Based on Median and with adjusted df Based on trimmed mean Levene Statistic df1 df2 Sig
4 Uji Post Hoc Duncan a Sig. Absrbnsi N Subset for alpha = Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = Deskriptiv Descriptives Absrbnsi Total 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum
5 Lampiran 3. Hasil Uji Anova Untuk ph Awal Uji Normalitas ph_awal Tests of Normality Kolmogorov-Smirnov a Statistic df Sig. Statistic df Sig. *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Uji Homogenitas Shapiro-Wilk Test of Homogeneity of Variance Based on Mean Based on Median Based on Median and with adjusted df Based on trimmed mean Levene Statistic df1 df2 Sig Uji Post Hoc Duncan a Sig. ph_awal N Subset for alpha = Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size =
6 Deskriptif Descriptives ph_awal Total 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower BoundUpper Bound Minimum Maximum
7 Lampiran 3. Hasil Uji Anova Untuk ph Akhir Uji Normalitas ph Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig. Statistic df Sig * *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Uji Homogenitas * * * * * * * * Test of Homogeneity of Variance ph Based on Mean Based on Median Based on Median and with adjusted df Based on trimmed mean Levene Statistic df1 df2 Sig Deskriptif Descriptives ph Total 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum
8 Uji Post Hoc Duncan a Sig. ph N Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = Subset for alpha =
9 Lampiran 5. Hasil Uji Anova Untuk Diameter Zona Jernih Uji Normalitas Zn_jrnih staphylo- staphylo- staphylo- staphylo- staphylo- satphylo- satphylo- staphylo- staphylo- salmonella- salmonella- salmonella- salmonella- salmonella- salmonella- salmonella- *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Tests of Normality b,c Kolmogorov-Smirnov a Shapiro-Wilk Statistic df Sig. Statistic df Sig * * * * * * * * * b. Zn_jrnih is constant when = salmonella-. It has been omitted. c. Zn_jrnih is constant when = salmonella-. It has been omitted. Uji Homogenitas Zn_jrnih Based on Mean Based on Median Based on Median and with adjusted df Based on trimmed mean Test of Homogeneity of Variance a,b Levene Statistic df1 df2 Sig a. Zn_jrnih is constant when = salmonella-. It has been omitted. b. Zn_jrnih is constant when = salmonella-. It has been omitted.
10 Deskriptif Descriptives Zn_jrnih staphylo- staphylo- staphylo- staphylo- staphylo- satphylo- satphylo- staphylo- staphylo- salmonella- salmonella- salmonella- salmonella- salmonella- salmonella- salmonella- salmonella- salmonella- Total 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum
11 Uji Post Hoc Duncan a salmonella- salmonella- salmonella- salmonella- salmonella- staphylo- salmonella- staphylo- staphylo- satphylo- staphylo- staphylo- satphylo- staphylo- staphylo- salmonella- salmonella- salmonella- Sig. Zn_jrnih N Means for groups in homogeneous subsets are displayed. a. Uses Harmonic Mean Sample Size = Subset for alpha =
12 Lampiran 6. Hasil Uji Korelasi Staphylococcus aureus Correlations Zn_jrnih absorbansi ph Zn_jrnih Pearson Correlation 1.804(**) -.747(**) Sig. (2-tailed) N absorbansi Pearson Correlation.804(**) (**) Sig. (2-tailed) N ph Pearson Correlation -.747(**) -.833(**) 1 Sig. (2-tailed) N ** Correlation is significant at the 0.01 level (2-tailed). Salmonella typhi Correlations Zn_jrnih absorbansi ph Zn_jrnih Pearson Correlation 1.970(**) -.829(**) Sig. (2-tailed) N absorbansi Pearson Correlation.970(**) (**) Sig. (2-tailed) N ph Pearson Correlation -.829(**) -.833(**) 1 Sig. (2-tailed) N ** Correlation is significant at the 0.01 level (2-tailed).
LAMPIRAN. Tests of Normality. Kolmogorov-Smirnov a. Berat_Limfa KB KP P
LAMPIRAN 1. Data Analisis Statistik 1.1 Berat Limpa U1 U2 U3 U4 U5 U6 Rata- SD Rata KB 0.53 0.17 0.18 0.2 0.18 0.13 0.23 0.15 KP 0.31 0.27 0.27 0.27 0.11 0.23 0.24 0.07 P1 0.23 0.21 0.12 0.2 0.24 0.23
More informationLAMPIRAN 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 informationE-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 informationTABEL 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 informationEksamen 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 informationAPPENDIX. 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 informationLAMPIRAN 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 informationCorrelations. 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 informationLampiran 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 informationFREQUENCIES 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 informationCDAA 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 informationHypermarket 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 informationLAMPIRAN. 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 informationEnter 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 informationSet 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 information1. 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 informationLAMPIRAN 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 informationLampiran 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 informationCopyright 2015 by Sean Connolly
1 Copyright 2015 by Sean Connolly All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other
More informationCluster 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 information5:2 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 informationSPSS. (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 informationStatistical 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 informationTHE 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 informationCorrelations. 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 informationExcel 2010 with XLSTAT
Excel 2010 with XLSTAT J E N N I F E R LE W I S PR I E S T L E Y, PH.D. Introduction to Excel 2010 with XLSTAT The layout for Excel 2010 is slightly different from the layout for Excel 2007. However, with
More informationMinitab 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 information050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 55555555555555555555555555555555555555555555555555 NYYNNYNNNYNYYYYYNNYNNNNNYNYYYYYNYNNNNYNNYNNNYNNNNN 01 CAEADDBEDEDBABBBBCBDDDBAAAECEEDCDCDBACCACEECACCCEA
More informationfor 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 informationProduct 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 informationJoe 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 informationTable 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 informationInterval 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 information6:1 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 informationSubset Selection in Multiple Regression
Chapter 307 Subset Selection in Multiple Regression Introduction Multiple regression analysis is documented in Chapter 305 Multiple Regression, so that information will not be repeated here. Refer to that
More informationLaboratory 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 informationCELL 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 information050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 55555555555555555555555555555555555555555555555555 YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY 01 CAEADDBEDEDBABBBBCBDDDBAAAECEEDCDCDBACCACEECACCCEA
More informationForfattere 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 informationConfidence Intervals: Estimators
Confidence Intervals: Estimators Point Estimate: a specific value at estimates a parameter e.g., best estimator of e population mean ( ) is a sample mean problem is at ere is no way to determine how close
More informationEcon 3790: Business and Economics Statistics. Instructor: Yogesh Uppal
Econ 3790: Business and Economics Statistics Instructor: Yogesh Uppal Email: yuppal@ysu.edu Chapter 8: Interval Estimation Population Mean: Known Population Mean: Unknown Margin of Error and the Interval
More informationIntermediate SAS: Statistics
Intermediate SAS: Statistics OIT TSS 293-4444 oithelp@mail.wvu.edu oit.wvu.edu/training/classmat/sas/ Table of Contents Procedures... 2 Two-sample t-test:... 2 Paired differences t-test:... 2 Chi Square
More informationUsing PC SAS/ASSIST* for Statistical Analyses
Using PC SAS/ASSIST* for Statistical Analyses Margaret A. Nemeth, Monsanto Company lptroductjon SAS/ASSIST, a user friendly, menu driven applications system, is available on several platforms. This paper
More informationBivariate (Simple) Regression Analysis
Revised July 2018 Bivariate (Simple) Regression Analysis This set of notes shows how to use Stata to estimate a simple (two-variable) regression equation. It assumes that you have set Stata up on your
More informationPLS205 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 informationInstructions for Using ABCalc James Alan Fox Northeastern University Updated: August 2009
Instructions for Using ABCalc James Alan Fox Northeastern University Updated: August 2009 Thank you for using ABCalc, a statistical calculator to accompany several introductory statistics texts published
More informationSTAT: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 informationLaboratory 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 informationStatgraphics 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 informationMultiple Regression White paper
+44 (0) 333 666 7366 Multiple Regression White paper A tool to determine the impact in analysing the effectiveness of advertising spend. Multiple Regression In order to establish if the advertising mechanisms
More informationANSWERS -- 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 informationLab #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 informationOn 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 informationSAS 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 informationRobust Linear Regression (Passing- Bablok Median-Slope)
Chapter 314 Robust Linear Regression (Passing- Bablok Median-Slope) Introduction This procedure performs robust linear regression estimation using the Passing-Bablok (1988) median-slope algorithm. Their
More informationMinitab 17 commands Prepared by Jeffrey S. Simonoff
Minitab 17 commands Prepared by Jeffrey S. Simonoff Data entry and manipulation To enter data by hand, click on the Worksheet window, and enter the values in as you would in any spreadsheet. To then save
More informationSTATISTICS FOR PSYCHOLOGISTS
STATISTICS FOR PSYCHOLOGISTS SECTION: JAMOVI CHAPTER: USING THE SOFTWARE Section Abstract: This section provides step-by-step instructions on how to obtain basic statistical output using JAMOVI, both visually
More informationThe ctest Package. January 3, 2000
R objects documented: The ctest Package January 3, 2000 bartlett.test....................................... 1 binom.test........................................ 2 cor.test.........................................
More informationRegression. 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 informationEXST3201 Mousefeed01 Page 1
EXST3201 Mousefeed01 Page 1 3 /* 4 Examine differences among the following 6 treatments 5 N/N85 fed normally before weaning and 85 kcal/wk after 6 N/R40 fed normally before weaning and 40 kcal/wk after
More informationTI-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 informationBootstrapped 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 informationLAMPIRAN 1 PENGARUH KETERSEDIAAN KOLEKSI PERPUSTAKAAN TERHADAP MINAT BACA SISWA SMP NEGERI 30 MEDAN
LAMPIRAN 1 ANGKET PENELITIAN PENGARUH KETERSEDIAAN KOLEKSI PERPUSTAKAAN TERHADAP MINAT BACA SISWA SMP NEGERI 30 MEDAN Saya mengharapkan kesediaan Saudara untuk mengisi angket dalam rangka penelitian tetang
More informationIndex. 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 informationSPSS 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 informationThe ANOVA Procedure (Chapter)
SAS/STAT 9.3 User s Guide The ANOVA Procedure (Chapter) SAS Documentation This document is an individual chapter from SAS/STAT 9.3 User s Guide. The correct bibliographic citation for the complete manual
More informationData Analysis and Hypothesis Testing Using the Python ecosystem
ARISTOTLE UNIVERSITY OF THESSALONIKI Data Analysis and Hypothesis Testing Using the Python ecosystem t-test & ANOVAs Stavros Demetriadis Assc. Prof., School of Informatics, Aristotle University of Thessaloniki
More informationI. MODEL. Q3i: Check my . Q29s: I like to see films and TV programs from other countries. Q28e: I like to watch TV shows on a laptop/tablet/phone
1 Multiple Regression-FORCED-ENTRY HIERARCHICAL MODEL DORIS ACHEME COM 631/731, Spring 2017 Data: Film & TV Usage 2015 I. MODEL IV Block 1: Demographics Sex (female dummy):q30 Age: Q31 Income: Q34 Block
More information9.1 Random coefficients models Constructed data Consumer preference mapping of carrots... 10
St@tmaster 02429/MIXED LINEAR MODELS PREPARED BY THE STATISTICS GROUPS AT IMM, DTU AND KU-LIFE Module 9: R 9.1 Random coefficients models...................... 1 9.1.1 Constructed data........................
More informationStatistics 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 informationThe 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 informationPsychology 282 Lecture #21 Outline Categorical IVs in MLR: Effects Coding and Contrast Coding
Psychology 282 Lecture #21 Outline Categorical IVs in MLR: Effects Coding and Contrast Coding In the previous lecture we learned how to incorporate a categorical research factor into a MLR model by using
More informationRegression. Page 1. Notes. Output Created Comments Data. 26-Mar :31:18. Input. C:\Documents and Settings\BuroK\Desktop\Data Sets\Prestige.
GET FILE='C:\Documents and Settings\BuroK\Desktop\DataSets\Prestige.sav'. GET FILE='E:\MacEwan\Teaching\Stat252\Data\SPSS_data\MENTALID.sav'. DATASET ACTIVATE DataSet1. DATASET CLOSE DataSet2. GET FILE='E:\MacEwan\Teaching\Stat252\Data\SPSS_data\survey_part.sav'.
More informationData Analysis and Solver Plugins for KSpread USER S MANUAL. Tomasz Maliszewski
Data Analysis and Solver Plugins for KSpread USER S MANUAL Tomasz Maliszewski tmaliszewski@wp.pl Table of Content CHAPTER 1: INTRODUCTION... 3 1.1. ABOUT DATA ANALYSIS PLUGIN... 3 1.3. ABOUT SOLVER PLUGIN...
More informationWhy 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 informationExample Using Missing Data 1
Ronald H. Heck and Lynn N. Tabata 1 Example Using Missing Data 1 Creating the Missing Data Variable (Miss) Here is a data set (achieve subset MANOVAmiss.sav) with the actual missing data on the outcomes.
More informationIn this computer exercise we will work with the analysis of variance in R. We ll take a look at the following topics:
UPPSALA UNIVERSITY Department of Mathematics Måns Thulin, thulin@math.uu.se Analysis of regression and variance Fall 2011 COMPUTER EXERCISE 2: One-way ANOVA In this computer exercise we will work with
More informationDesign of Experiments
Seite 1 von 1 Design of Experiments Module Overview In this module, you learn how to create design matrices, screen factors, and perform regression analysis and Monte Carlo simulation using Mathcad. Objectives
More informationPackage 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 informationLong Term Analysis for the BAM device Donata Bonino and Daniele Gardiol INAF Osservatorio Astronomico di Torino
Long Term Analysis for the BAM device Donata Bonino and Daniele Gardiol INAF Osservatorio Astronomico di Torino 1 Overview What is BAM Analysis in the time domain Analysis in the frequency domain Example
More informationBland-Altman Plot and Analysis
Chapter 04 Bland-Altman Plot and Analysis Introduction The Bland-Altman (mean-difference or limits of agreement) plot and analysis is used to compare two measurements of the same variable. That is, it
More informationDataSet2. <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 informationConducting 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 informationQuantitative - One Population
Quantitative - One Population The Quantitative One Population VISA procedures allow the user to perform descriptive and inferential procedures for problems involving one population with quantitative (interval)
More informationDescriptives. Graph. [DataSet1] C:\Documents and Settings\BuroK\Desktop\Prestige.sav
GET FILE='C:\Documents and Settings\BuroK\Desktop\Prestige.sav'. DESCRIPTIVES VARIABLES=prestige education income women /STATISTICS=MEAN STDDEV MIN MAX. Descriptives Input Missing Value Handling Resources
More informationThe 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 informationOrganizing 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 informationAn 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 informationZ-TEST / Z-STATISTIC: used to test hypotheses about. µ when the population standard deviation is unknown
Z-TEST / Z-STATISTIC: used to test hypotheses about µ when the population standard deviation is known and population distribution is normal or sample size is large T-TEST / T-STATISTIC: used to test hypotheses
More information* Sample SAS program * Data set is from Dean and Voss (1999) Design and Analysis of * Experiments. Problem 3, page 129.
SAS Most popular Statistical software worldwide. SAS claims that its products are used at over 40,000 sites, including at 90% of the Fortune 500. This will not be all SAS as they make other products, such
More informationJournal on Banking Financial Services & Insurance Research Vol. 6, Issue 12, December 2016, Impact Factor: ISSN: ( )
A Comparative Study of Mobile Banking Transactions of Select Public and Private Sector Banks in India Dr. Veena K.P. Associate Professor, Dept. of Master of Business Administration (MBA), Visvesvaraya
More informationAnalyzing traffic source impact on returning visitors ratio in information provider website
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Analyzing traffic source impact on returning visitors ratio in information provider website To cite this article: A Prasetio et
More informationAnalysis of variance - ANOVA
Analysis of variance - ANOVA Based on a book by Julian J. Faraway University of Iceland (UI) Estimation 1 / 50 Anova In ANOVAs all predictors are categorical/qualitative. The original thinking was to try
More informationStatistical Tests for Variable Discrimination
Statistical Tests for Variable Discrimination University of Trento - FBK 26 February, 2015 (UNITN-FBK) Statistical Tests for Variable Discrimination 26 February, 2015 1 / 31 General statistics Descriptional:
More informationConstructing 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 informationRobust Methods of Performing One Way RM ANOVA in R
Robust Methods of Performing One Way RM ANOVA in R Wilcox s WRS package (Wilcox & Schönbrodt, 2014) provides several methods of testing one-way repeated measures data. The rmanova() command can trim means
More informationStat 5303 (Oehlert): Unreplicated 2-Series Factorials 1
Stat 5303 (Oehlert): Unreplicated 2-Series Factorials 1 Cmd> a
More informationIQC monitoring in laboratory networks
IQC for Networked Analysers Background and instructions for use IQC monitoring in laboratory networks Modern Laboratories continue to produce large quantities of internal quality control data (IQC) despite
More informationSTATS 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 informationMultivariate Capability Analysis
Multivariate Capability Analysis Summary... 1 Data Input... 3 Analysis Summary... 4 Capability Plot... 5 Capability Indices... 6 Capability Ellipse... 7 Correlation Matrix... 8 Tests for Normality... 8
More information