Eksamen ERN4110, 6/ VEDLEGG SPSS utskrifter til oppgavene (Av plasshensyn kan utskriftene være noe redigert)
|
|
|
- Melvin Randall
- 5 years ago
- Views:
Transcription
1 Eksamen ERN4110, 6/ VEDLEGG SPSS utskrifter til oppgavene (Av plasshensyn kan utskriftene være noe redigert) 1
2 Oppgave 1 Datafila I SPSS: Variabelnavn Beskrivelse Kjønn Kjønn (1=Kvinne, 2=Mann) Studieinteresse Hvor tidlig fattet du interesse for studiet? (1=Fra jeg var barn, 2=I ungdomstida, 3=Siste år) Data Weight Cases Weight cases by: Antall Analyze - Descriptive Statistics - Crosstabs Row(s): Kjønn Column(s): Studieinteresse Statistics: Chi-square Cells: Expected Row Crosstabs Kjønn * Studieinteresse Crosstabulation Studieinteresse Fra jeg var barn I ungdomstida Siste år Kjønn Kvinne Count Total Expected Count 79,5 398,3 862,2 1340,0 % within Kjønn 6,9% 28,7% 64,3% 100,0% Mann Count Expected Count 24,5 122,7 265,8 413,0 % within Kjønn 2,7% 32,9% 64,4% 100,0% Total Count Expected Count 104,0 521,0 1128,0 1753,0 % within Kjønn 5,9% 29,7% 64,3% 100,0% 2
3 Chi-Square Tests Value df Asymptotic Significance (2- sided) Pearson Chi-Square 11,606 a 2,003 Likelihood Ratio 13,387 2,001 Linear-by-Linear Association 1,655 1,198 N of Valid Cases 1753 a. 0 cells (0,0%) have expected count less than 5. The minimum expected count is 24,50. 3
4 Datafila i SPSS: Oppgave 2 Variabelnavn skole score Beskrivelse Skole (1=Skole1, 2=Skole2, 3=Skole3) IQ score Analyze Descriptive Statistics Explore Dependent List: score Factor List: skole Plots: Histogram Normality plots with tests 4
5 Explore Case Processing Summary Cases Valid Missing Total skole N Percent N Percent N Percent score 1, ,0% 0 0,0% ,0% 2, ,0% 0 0,0% ,0% 3, ,0% 0 0,0% ,0% Descriptives skole Statistic Std. Error score 1,00 Mean 99,2000 3, % Confidence Interval for Mean Lower Bound 91,6610 Upper Bound 106,7390 5% Trimmed Mean 99,1111 Median 99,5000 Variance 111,067 Std. Deviation 10,53882 Minimum 85,00 Maximum 115,00 Range 30,00 Interquartile Range 21,00 Skewness,141,687 Kurtosis -1,386 1,334 2,00 Mean 112,6000 4, % Confidence Interval for Mean Lower Bound 102,3427 Upper Bound 122,8573 5% Trimmed Mean 112,3889 Median 112,5000 Variance 205,600 Std. Deviation 14,33876 Minimum 94,00 Maximum 135,00 Range 41,00 Interquartile Range 29,00 Skewness,020,687 Kurtosis -1,328 1,334 3,00 Mean 93,3000 4, % Confidence Interval for Mean Lower Bound 84,2385 Upper Bound 102,3615 5% Trimmed Mean 93,2778 Median 94,5000 Variance 160,456 Std. Deviation 12,66711 Minimum 74,00 Maximum 113,00 Range 39,00 Interquartile Range 18,50 5
6 Skewness -,100,687 Kurtosis -,670 1,334 Tests of Normality Kolmogorov-Smirnov a Shapiro-Wilk skole Statistic df Sig. Statistic df Sig. score 1,00,147 10,200 *,943 10,585 2,00,177 10,200 *,925 10,397 3,00,114 10,200 *,967 10,864 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Histograms 6
7 7
8 Normal Q-Q Plots 8
9 Detrended Normal Q-Q Plots 9
10 10
11 Analyze Nonparametric Tests Legacy Dialogs K Independent Samples Test variable List: score Grouping Variable: skole (1 3) Kruskal-Wallis H Exact tests: Exact NPar Tests Kruskal-Wallis Test Ranks skole N Mean Rank score 1, ,20 2, ,45 3, ,85 Total 30 Test Statistics a,b score Kruskal-Wallis H 7,596 df 2 Asymp. Sig.,022 a. Kruskal Wallis Test b. Grouping Variable: skole Analyze Nonparametric Tests Legacy Dialogs 2 Independent Samples Test variable List: score Grouping Variable: skole (1 2) Mann Whitney U Exact tests: Exact 11
12 NPar Tests Mann-Whitney Test Ranks skole N Mean Rank Sum of Ranks score 1, ,85 78,50 2, ,15 131,50 Total 20 Test Statistics a score Mann-Whitney U 23,500 Wilcoxon W 78,500 Z -2,006 Asymp. Sig. (2-tailed),045 Exact Sig. [2*(1-tailed Sig.)],043 b Exact Sig. (2-tailed),045 Exact Sig. (1-tailed),023 Point Probability,002 a. Grouping Variable: skole b. Not corrected for ties. Analyze Nonparametric Tests Legacy Dialogs 2 Independent Samples Test variable List: score Grouping Variable: skole (1 3) Mann Whitney U Exact tests: Exact NPar Tests Mann-Whitney Test Ranks skole N Mean Rank Sum of Ranks score 1, ,85 118,50 3, ,15 91,50 Total 20 Test Statistics a score Mann-Whitney U 36,500 Wilcoxon W 91,500 Z -1,023 Asymp. Sig. (2-tailed),306 Exact Sig. [2*(1-tailed Sig.)],315 b Exact Sig. (2-tailed),323 Exact Sig. (1-tailed),161 Point Probability,009 a. Grouping Variable: skole b. Not corrected for ties. 12
13 Analyze Nonparametric Tests Legacy Dialogs 2 Independent Samples Test variable List: score Grouping Variable: skole (2 3) Mann Whitney U Exact tests: Exact NPar Tests Mann-Whitney Test Ranks skole N Mean Rank Sum of Ranks score 2, ,80 138,00 3, ,20 72,00 Total 20 Test Statistics a score Mann-Whitney U 17,000 Wilcoxon W 72,000 Z -2,500 Asymp. Sig. (2-tailed),012 Exact Sig. [2*(1-tailed Sig.)],011 b Exact Sig. (2-tailed),011 Exact Sig. (1-tailed),005 Point Probability,001 a. Grouping Variable: skole b. Not corrected for ties. Analyze Compare Means One-Way ANOVA Dependent List: score Factor: skole Options: Descriptive Homogeneity of variance Oneway Descriptives score 95% Confidence Interval for Std. Mean N Mean Deviation Std. Error Lower Bound Upper Bound Minimum Maximum 1, , , , , , ,00 115,00 2, , , , , , ,00 135,00 3, , , , , , ,00 113,00 Total , , , , , ,00 135,00 Test of Homogeneity of Variances Levene Statistic df1 df2 Sig. score Based on Mean, ,588 Based on Median, ,592 Based on Median and with, ,834,592 adjusted df Based on trimmed mean, ,588 13
14 score ANOVA Sum of Squares df Mean Square F Sig. Between Groups 1956, ,100 6,150,006 Within Groups 4294, ,041 Total 6250, Analyze Compare Means One-Way ANOVA Dependent List: score Factor: skole Posthoc: Bonferroni Oneway ANOVA score Sum of Squares df Mean Square F Sig. Between Groups 1956, ,100 6,150,006 Within Groups 4294, ,041 Total 6250, Post Hoc Tests Multiple Comparisons Dependent Variable: score Bonferroni Mean Difference (I- 95% Confidence Interval (I) skole (J) skole J) Std. Error Sig. Lower Bound Upper Bound 1,00 2,00-13, ,63987,075-27,7955,9955 3,00 5, ,63987,914-8, ,2955 2,00 1,00 13, ,63987,075 -, ,7955 3,00 19,30000 * 5,63987,006 4, ,6955 3,00 1,00-5, ,63987,914-20,2955 8,4955 2,00-19,30000 * 5,63987,006-33,6955-4,9045 *. The mean difference is significant at the 0.05 level. 14
15 Oppgave 3 Ingen utskrift. Oppgave 4 Ingen utskrift 15
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
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
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.
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
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
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,
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
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
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
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.
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
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
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
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
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
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
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
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
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
Nuts and Bolts Research Methods Symposium
Organizing Your Data Jenny Holcombe, PhD UT College of Medicine Nuts & Bolts Conference August 16, 3013 Topics to Discuss: Types of Variables Constructing a Variable Code Book Developing Excel Spreadsheets
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
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
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
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
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
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
11. Chi Square. Calculate Chi Square for contingency tables. A Chi Square is used to analyze categorical data. It compares observed
11. Chi Square Objectives Calculate goodness of fit Chi Square Calculate Chi Square for contingency tables Calculate effect size Save data entry time by weighting cases A Chi Square is used to analyze
The ctest Package. January 3, 2000
R objects documented: The ctest Package January 3, 2000 bartlett.test....................................... 1 binom.test........................................ 2 cor.test.........................................
Minitab 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
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
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.
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
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
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
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
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
Copyright 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
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
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
1. Basic Steps for Data Analysis Data Editor. 2.4.To create a new SPSS file
1 SPSS Guide 2009 Content 1. Basic Steps for Data Analysis. 3 2. Data Editor. 2.4.To create a new SPSS file 3 4 3. Data Analysis/ Frequencies. 5 4. Recoding the variable into classes.. 5 5. Data Analysis/
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,
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
An introduction to SPSS
An introduction to SPSS To open the SPSS software using U of Iowa Virtual Desktop... Go to https://virtualdesktop.uiowa.edu and choose SPSS 24. Contents NOTE: Save data files in a drive that is accessible
Instructions 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
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
STAT 503 Fall Introduction to SAS
Getting Started Introduction to SAS 1) Download all of the files, sas programs (.sas) and data files (.dat) into one of your directories. I would suggest using your H: drive if you are using a computer
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
Chapter 5: The beast of bias
Chapter 5: The beast of bias Self-test answers SELF-TEST Compute the mean and sum of squared error for the new data set. First we need to compute the mean: + 3 + + 3 + 2 5 9 5 3. Then the sum of squared
SPSS: AN OVERVIEW. SEEMA JAGGI Indian Agricultural Statistics Research Institute Library Avenue, New Delhi
: AN OVERVIEW SEEMA JAGGI Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-110 012 seema@iasri.res.in 1. Introduction The abbreviation SPSS stands for Statistical Package for
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
Introduction to SPSS A Tutorial for SPSS v20
Student Learning Services (Tā te Ākonga) Introduction to SPSS A Tutorial for SPSS v20 As with any software package, there are multiple ways to perform any task. In order to avoid confusion for the beginning
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
- 1 - Fig. A5.1 Missing value analysis dialog box
WEB APPENDIX Sarstedt, M. & Mooi, E. (2019). A concise guide to market research. The process, data, and methods using SPSS (3 rd ed.). Heidelberg: Springer. Missing Value Analysis and Multiple Imputation
SUPPLEMENT USING NCSS 2004/2007 IN BUSI 444
SUPPLEMENT USING NCSS 2004/2007 IN BUSI 444 TABLE OF CONTENTS Introduction... 1 LESSON 1... 2 Page 1.12: Scatterplots... 2 Page 1.15: Fit line for a second degree polynomial... 2 Page 1.17: Setting a Filter...
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 55555555555555555555555555555555555555555555555555 YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY 01 CAEADDBEDEDBABBBBCBDDDBAAAECEEDCDCDBACCACEECACCCEA
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
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
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
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
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.
STATA 13 INTRODUCTION
STATA 13 INTRODUCTION Catherine McGowan & Elaine Williamson LONDON SCHOOL OF HYGIENE & TROPICAL MEDICINE DECEMBER 2013 0 CONTENTS INTRODUCTION... 1 Versions of STATA... 1 OPENING STATA... 1 THE STATA
4. Descriptive Statistics: Measures of Variability and Central Tendency
4. Descriptive Statistics: Measures of Variability and Central Tendency Objectives Calculate descriptive for continuous and categorical data Edit output tables Although measures of central tendency and
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
AcaStat User Manual. Version 10 for Mac and Windows. Copyright 2018, AcaStat Software. All rights Reserved.
AcaStat User Manual Version 10 for Mac and Windows Copyright 2018, AcaStat Software. All rights Reserved. http://www.acastat.com Table of Contents NEW IN VERSION 10... 6 INTRODUCTION... 7 GETTING HELP...
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
Quantitative - 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)
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
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
Excel 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
MHPE 494: Data Analysis. Welcome! The Analytic Process
MHPE 494: Data Analysis Alan Schwartz, PhD Department of Medical Education Memoona Hasnain,, MD, PhD, MHPE Department of Family Medicine College of Medicine University of Illinois at Chicago Welcome! Your
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
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 55555555555555555555555555555555555555555555555555 NYYNNYNNNYNYYYYYNNYNNNNNYNYYYYYNYNNNNYNNYNNNYNNNNN 01 CAEADDBEDEDBABBBBCBDDDBAAAECEEDCDCDBACCACEECACCCEA
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
CREATING THE DISTRIBUTION ANALYSIS
Chapter 12 Examining Distributions Chapter Table of Contents CREATING THE DISTRIBUTION ANALYSIS...176 BoxPlot...178 Histogram...180 Moments and Quantiles Tables...... 183 ADDING DENSITY ESTIMATES...184
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
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
WolStat: A new statistics package for the behavioral and social sciences
Behavior Research Methods 2008, 40 (I), 94-101 doi: 10.3758/13RM40.1.94 WolStat: A new statistics package for the behavioral and social sciences ALLEN H. WOLACH Illinois Institute of Technology, Chicago,
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
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
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
Research Methods for Business and Management. Session 8a- Analyzing Quantitative Data- using SPSS 16 Andre Samuel
Research Methods for Business and Management Session 8a- Analyzing Quantitative Data- using SPSS 16 Andre Samuel A Simple Example- Gym Purpose of Questionnaire- to determine the participants involvement
Chapter 1. Looking at Data-Distribution
Chapter 1. Looking at Data-Distribution Statistics is the scientific discipline that provides methods to draw right conclusions: 1)Collecting the data 2)Describing the data 3)Drawing the conclusions Raw
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
One Factor Experiments
One Factor Experiments 20-1 Overview Computation of Effects Estimating Experimental Errors Allocation of Variation ANOVA Table and F-Test Visual Diagnostic Tests Confidence Intervals For Effects Unequal
EXST3201 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
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
Subset 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
For Additional Information...
For Additional Information... The materials in this handbook were developed by Master Black Belts at General Electric Medical Systems to assist Black Belts and Green Belts in completing Minitab Analyses.
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
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
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
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
15 Wyner Statistics Fall 2013
15 Wyner Statistics Fall 2013 CHAPTER THREE: CENTRAL TENDENCY AND VARIATION Summary, Terms, and Objectives The two most important aspects of a numerical data set are its central tendencies and its variation.
The Power and Sample Size Application
Chapter 72 The Power and Sample Size Application Contents Overview: PSS Application.................................. 6148 SAS Power and Sample Size............................... 6148 Getting Started:
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
Data 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...
WINKS SDA 7. Version 7
WINKS SDA 7 Version 7 (For BASIC and PROFESSIONAL Editions of WINKS SDA) PowerPoint Slides for this Guide are svailable at the website Click Instructors. www.texasoft.com TexaSoft, 2015 Do these tutorials
Learn What s New. Statistical Software
Statistical Software Learn What s New Upgrade now to access new and improved statistical features and other enhancements that make it even easier to analyze your data. The Assistant Data Customization
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
Applied Regression Modeling: A Business Approach
i Applied Regression Modeling: A Business Approach Computer software help: SPSS SPSS (originally Statistical Package for the Social Sciences ) is a commercial statistical software package with an easy-to-use