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

Size: px
Start display at page:

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

Transcription

1 LAMPIRA

2 Case Processing Summary a. Listwise deleti based all variables in the procedure. % Crbach's Alpha Items a of Items, Case Processing Summary % Crbach's Alpha Items of Items,671,654 4

3 Case Processing Summary % Crbach's a Alpha Items of Items.702,705 4 Case Processing Summary % Crbach's Alpha Items of Items,832,789 4

4 Case Processing Summary % Crbach's Alpha Items of Items,789,809 4 Case Processing Summary % Crbach's Alpha Items of Items,859,835 4

5 Case Processing Summary % Crbach's Alpha Items of Items,751,763 4 Case Processing Summary l % Crbach's a Alpha Items of Items.781,786 4

6 Case Processing Summary % Crbach's Alpha Items of Items,812,824 4

7 tot_kepuasan *. Correlati is significant at the 0.01 level (2-tailed). total_kepuasan *. Correlati is significant at the 0.05 level (2-tailed). total_kepuasan Sig. (1-tailed) Sig. (1-tailed) tot_kepuasan 1 -,356 * **. Correlati is significant at the 0.05 level (1-tailed)..,000 -,356 * 1,000. total_ 1 -,167 *.,029 -,167 * 1, total_ kepuasan 1 -,174 **.,023 -,174 ** 1,023.

8 tot_kepuasan *. Correlati is significant at the 0.05 level (2-tailed). total_kepuasan *. Correlati is significant at the 0.05 level (2-tailed). total_kepuasan Sig. (1-tailed) Sig. (1-tailed) tot_kepuasan 1 -,173 * **. Correlati is significant at the 0.01 level (1-tailed)..,024 -,173 * 1,024. total_ kepuasan 1 -,308 *.,000 -,308 * 1,000. total_ kepuasan 1 -,336 **.,007 -,336 ** 1,000.

9 tot_kepuasan *. Correlati is significant at the 0.01 level (2-tailed). total_kepuasan *. Correlati is significant at the 0.05 level (2-tailed). total_kepuasan Sig. (1-tailed) Sig. (1-tailed) tot_kepuasan 1 -,204 * **. Correlati is significant at the 0.01 level (1-tailed)..,007 -,204 * 1,007. total_ kepuasan 1 -,268 *.,000 -,268 * 1,000. total_ kepuasan 1 -,007 **.,924 -,007 ** 1,924.

10 tot_kepuasan *. Correlati is significant at the 0.05 level (2-tailed). tot_kepuasan 1 -,273 *.,000 -,273 * 1,000.

11 AOVA tot_dm6 Between Groups Within Groups Oneway tot_dm7 Between Groups Within Groups Oneway tot_8 Between Groups Within Groups Oneway Sum of Squares df Mean Square F Sig. 104, ,457 3,839, , , , AOVA Sum of Squares df Mean Square F Sig. 136, ,407 3,760, , , , AOVA Sum of Squares df Mean Square F Sig. 49, ,988 2,618, , , ,

12 AOVA tot_dm9 Between Groups Within Groups T-Test Sum of Squares df Mean Square F Sig. 11, ,642 1,762, , , , Group Statistics mean_ji jk_ji laki-laki perempuan Std. Error Mean Std. Deviati Mean 117 4,5090,21764, ,5254,19386,02638 Independent Samples Test mean_ji Equal variances assumed Equal variances not assumed Levene's Test for Equality of Variances F Sig. t df t-test for Equality of Means Mean Difference 95% Cfidence Interval of the Std. Error Difference Difference Lower Upper,196,659 -, ,637 -,01637, ,08472, , ,839,623 -,01637, ,08209,04936 Oneway

13 AOVA mean_ji Between Groups Within Groups Sum of Squares df Mean Square F Sig.,010 1,010,223,637 7, ,044 7,

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

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

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

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

050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA

050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 55555555555555555555555555555555555555555555555555 NYYNNYNNNYNYYYYYNNYNNNNNYNYYYYYNYNNNNYNNYNNNYNNNNN 01 CAEADDBEDEDBABBBBCBDDDBAAAECEEDCDCDBACCACEECACCCEA

More information

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

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 information

050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA

050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 55555555555555555555555555555555555555555555555555 YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY 01 CAEADDBEDEDBABBBBCBDDDBAAAECEEDCDCDBACCACEECACCCEA

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

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

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

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

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

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

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

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

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

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

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

I. 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

I. 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 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

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

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

Confidence Intervals: Estimators

Confidence 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 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

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

Stat 401 B Lecture 26

Stat 401 B Lecture 26 Stat B Lecture 6 Forward Selection The Forward selection rocedure looks to add variables to the model. Once added, those variables stay in the model even if they become insignificant at a later ste. Backward

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

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

Regression. Page 1. Notes. Output Created Comments Data. 26-Mar :31:18. Input. C:\Documents and Settings\BuroK\Desktop\Data Sets\Prestige.

Regression. 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 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

Rubberball: Survey analysis and recommendations

Rubberball: Survey analysis and recommendations Brigham Young University BYU ScholarsArchive All Student Publications 2008-04-07 Rubberball: Survey analysis and recommendations Jared Bell jaredbbell@gmail.com Nate Shields Chris Clegg Garrett Beeston

More information

Multiple Regression White paper

Multiple 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 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

11-2 Probability Distributions

11-2 Probability Distributions Classify each random variable X as discrete or continuous. Explain your reasoning. 1. X represents the number of text messages sent by a randomly chosen student during a given day. Discrete; the number

More information

Descriptives. Graph. [DataSet1] C:\Documents and Settings\BuroK\Desktop\Prestige.sav

Descriptives. 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 information

Quantitative - One Population

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)

More information

DATA DEFINITION PHASE

DATA DEFINITION PHASE Twoway Analysis of Variance Unlike previous problems in the manual, the present problem involves two independent variables (gender of juror and type of crime committed by defendant). There are two levels

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

Independent Variables

Independent Variables 1 Stepwise Multiple Regression Olivia Cohen Com 631, Spring 2017 Data: Film & TV Usage 2015 I. MODEL Independent Variables Demographics Item: Age Item: Income Dummied Item: Gender (Female) Digital Media

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

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

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

A. Incorrect! This would be the negative of the range. B. Correct! The range is the maximum data value minus the minimum data value.

A. Incorrect! This would be the negative of the range. B. Correct! The range is the maximum data value minus the minimum data value. AP Statistics - Problem Drill 05: Measures of Variation No. 1 of 10 1. The range is calculated as. (A) The minimum data value minus the maximum data value. (B) The maximum data value minus the minimum

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

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

Bivariate (Simple) Regression Analysis

Bivariate (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 information

[DataSet1] C:\Documents and Settings\myersb\Desktop\fall2010 spss exams and practice\outp ut practice for spss2\spss_practice2_data.sav. Std.

[DataSet1] C:\Documents and Settings\myersb\Desktop\fall2010 spss exams and practice\outp ut practice for spss2\spss_practice2_data.sav. Std. T-TEST GROUPS=gender(1 2) /MISSIG=AALYSIS /VARIABLES=age /CRITERIA=CI(.9500). [DaaSe1] C:\Documens and Seings\myersb\Deskop\fall2010 spss exams and pracice\oup Group Saisics age gender male female 23 22

More information

ST512. Fall Quarter, Exam 1. Directions: Answer questions as directed. Please show work. For true/false questions, circle either true or false.

ST512. Fall Quarter, Exam 1. Directions: Answer questions as directed. Please show work. For true/false questions, circle either true or false. ST512 Fall Quarter, 2005 Exam 1 Name: Directions: Answer questions as directed. Please show work. For true/false questions, circle either true or false. 1. (42 points) A random sample of n = 30 NBA basketball

More information

NCSS Statistical Software. Design Generator

NCSS Statistical Software. Design Generator Chapter 268 Introduction This program generates factorial, repeated measures, and split-plots designs with up to ten factors. The design is placed in the current database. Crossed Factors Two factors are

More information

Exploring Campus Netflow for Managing Network

Exploring Campus Netflow for Managing Network Exploring Campus Netflow for Managing Network Hung-Jen Yang, Miao-Kuei Ho, Lung-Hsing Kuo, Hsieh-Hua Yang, Hsueh-Chih Lin Abstract The purpose of this study was to monitoring and modeling netflows on a

More information

T-test og variansanalyse i SAS. T-test og variansanalyse i SAS p.1/18

T-test og variansanalyse i SAS. T-test og variansanalyse i SAS p.1/18 T-test og variansanalyse i SAS T-test og variansanalyse i SAS p.1/18 T-test og variansanalyse i SAS T-test (Etstik, tostik, parrede observationer) Variansanalyse SAS-procedurer: PROC TTEST PROC GLM T-test

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

- 1 - Fig. A5.1 Missing value analysis dialog box

- 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

More information

Software Size Estimation II

Software Size Estimation II Copyright 1994 Carnegie Mellon University Disciplined Software Engineering - Lecture 4 1 Software Size Estimation II Material adapted from: Disciplined Software Engineering Software Engineering Institute

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

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

Journal on Banking Financial Services & Insurance Research Vol. 6, Issue 12, December 2016, Impact Factor: ISSN: ( )

Journal 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 information

One Factor Experiments

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

More information

An introduction to SPSS

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

More information

Exploring association among quality indicators of the In-service Education Information Service

Exploring association among quality indicators of the In-service Education Information Service Exploring association among quality indicators of the In-service Education Information Service LUNG-HSING KUO, HUNG-JEN YANG, TSUNG-JUNG TSAI, FONG-CHING SU National Kaohsiung Normal University No.116,

More information

Have you experienced any adverse health effects from the sun? Yes 1 No 2 Unsure 3

Have you experienced any adverse health effects from the sun? Yes 1 No 2 Unsure 3 This session takes you through some more of the statistical tests available in SPSS, as well as the use of syntax. It uses the Health 2011 data file. The questionnaire from which the data file was created

More information

Stat 5303 (Oehlert): Response Surfaces 1

Stat 5303 (Oehlert): Response Surfaces 1 Stat 5303 (Oehlert): Response Surfaces 1 > data

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

Instruction on JMP IN of Chapter 19

Instruction on JMP IN of Chapter 19 Instruction on JMP IN of Chapter 19 Example 19.2 (1). Download the dataset xm19-02.jmp from the website for this course and open it. (2). Go to the Analyze menu and select Fit Model. Click on "REVENUE"

More information

Z-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 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

Instructions for Using ABCalc James Alan Fox Northeastern University Updated: August 2009

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

More information

Multidimensional Scaling Presentation. Spring Rob Goodman Paul Palisin

Multidimensional Scaling Presentation. Spring Rob Goodman Paul Palisin 1 Multidimensional Scaling Presentation Spring 2009 Rob Goodman Paul Palisin Social Networking Facebook MySpace Instant Messaging Email Youtube Text Messaging Twitter 2 Create a survey for your MDS Enter

More information

Beta-Regression with SPSS Michael Smithson School of Psychology, The Australian National University

Beta-Regression with SPSS Michael Smithson School of Psychology, The Australian National University 9/1/2005 Beta-Regression with SPSS 1 Beta-Regression with SPSS Michael Smithson School of Psychology, The Australian National University (email: Michael.Smithson@anu.edu.au) SPSS Nonlinear Regression syntax

More information

Soci Statistics for Sociologists

Soci Statistics for Sociologists University of North Carolina Chapel Hill Soci708-001 Statistics for Sociologists Fall 2009 Professor François Nielsen Stata Commands for Module 7 Inference for Distributions For further information on

More information

Compare Linear Regression Lines for the HP-41C

Compare Linear Regression Lines for the HP-41C Compare Linear Regression Lines for the HP-41C by Namir Shammas This article presents an HP-41C program that calculates the linear regression statistics for two data sets and then compares their slopes

More information

**************************************************************************************************************** * Single Wave Analyses.

**************************************************************************************************************** * Single Wave Analyses. ASDA2 ANALYSIS EXAMPLE REPLICATION SPSS C11 * Syntax for Analysis Example Replication C11 * Use data sets previously prepared in SAS, refer to SAS Analysis Example Replication for C11 for details. ****************************************************************************************************************

More information

Multiple Linear Regression: Global tests and Multiple Testing

Multiple Linear Regression: Global tests and Multiple Testing Multiple Linear Regression: Global tests and Multiple Testing Author: Nicholas G Reich, Jeff Goldsmith This material is part of the statsteachr project Made available under the Creative Commons Attribution-ShareAlike

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

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

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

Econ 3790: Business and Economics Statistics. Instructor: Yogesh Uppal

Econ 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 information

ST706: Spring One-Way Random effects example.

ST706: Spring One-Way Random effects example. ST706: Spring 2013. One-Way Random effects example. DATA IS FROM CONTROL ARM OF SKIN CANCER STUDY AT DARTMOUTH. DC0 TO DC5 ARE MEASURES OF DIETARY INTAKE OVER 6 YEARS. For illustration here we treat the

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

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

2.830J / 6.780J / ESD.63J Control of Manufacturing Processes (SMA 6303) Spring 2008

2.830J / 6.780J / ESD.63J Control of Manufacturing Processes (SMA 6303) Spring 2008 MIT OpenCourseWare http://ocw.mit.edu.83j / 6.78J / ESD.63J Control of Manufacturing Processes (SMA 633) Spring 8 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

Unit 8 SUPPLEMENT Normal, T, Chi Square, F, and Sums of Normals

Unit 8 SUPPLEMENT Normal, T, Chi Square, F, and Sums of Normals BIOSTATS 540 Fall 017 8. SUPPLEMENT Normal, T, Chi Square, F and Sums of Normals Page 1 of Unit 8 SUPPLEMENT Normal, T, Chi Square, F, and Sums of Normals Topic 1. Normal Distribution.. a. Definition..

More information

Stat 5100 Handout #12.f SAS: Time Series Case Study (Unit 7)

Stat 5100 Handout #12.f SAS: Time Series Case Study (Unit 7) Stat 5100 Handout #12.f SAS: Time Series Case Study (Unit 7) Data: Weekly sales (in thousands of units) of Super Tech Videocassette Tapes over 161 weeks [see Bowerman & O Connell Forecasting and Time Series:

More information

Chapter 1. Looking at Data-Distribution

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

More information

Using Computator page F or t or means or r -> d. Effect Sizes (ES) & Meta-Analyses. Using Computator page F or t or means or r -> d

Using Computator page F or t or means or r -> d. Effect Sizes (ES) & Meta-Analyses. Using Computator page F or t or means or r -> d Using Computator page F or t or means or r -> d Effect Sizes (ES) & Meta-Analyses ES using Computator ES transformations Meta Analysis Top is the definitional formula using means, std & n for each group

More information

Package visualize. April 28, 2017

Package visualize. April 28, 2017 Type Package Package visualize April 28, 2017 Title Graph Probability Distributions with User Supplied Parameters and Statistics Version 4.3.0 Date 2017-04-27 Depends R (>= 3.0.0) Graphs the pdf or pmf

More information

A simple method to do circular convolution

A simple method to do circular convolution A simple method to do circular convolution Nasser M. Abbasi November 2, 2018 Compiled on November 2, 2018 at 11:5am This describes a simple method I found to do circular convolution, which I think is simpler

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

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

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

Factorial ANOVA with SAS

Factorial ANOVA with SAS Factorial ANOVA with SAS /* potato305.sas */ options linesize=79 noovp formdlim='_' ; title 'Rotten potatoes'; title2 ''; proc format; value tfmt 1 = 'Cool' 2 = 'Warm'; data spud; infile 'potato2.data'

More information

Copyright 2015 by Sean Connolly

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

More information

Compare Linear Regression Lines for the HP-67

Compare Linear Regression Lines for the HP-67 Compare Linear Regression Lines for the HP-67 by Namir Shammas This article presents an HP-67 program that calculates the linear regression statistics for two data sets and then compares their slopes and

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

Model Selection and Inference

Model Selection and Inference Model Selection and Inference Merlise Clyde January 29, 2017 Last Class Model for brain weight as a function of body weight In the model with both response and predictor log transformed, are dinosaurs

More information

Exercise 2.23 Villanova MAT 8406 September 7, 2015

Exercise 2.23 Villanova MAT 8406 September 7, 2015 Exercise 2.23 Villanova MAT 8406 September 7, 2015 Step 1: Understand the Question Consider the simple linear regression model y = 50 + 10x + ε where ε is NID(0, 16). Suppose that n = 20 pairs of observations

More information

AMELIA II: A Program for Missing Data

AMELIA II: A Program for Missing Data AMELIA II: A Program for Missing Data Amelia II is an R package that performs multiple imputation to deal with missing data, instead of other methods, such as pairwise and listwise deletion. In multiple

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

Week 4: Simple Linear Regression III

Week 4: Simple Linear Regression III Week 4: Simple Linear Regression III Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Goodness of

More information

ZunZun.com. User-Selectable Polynomial. Sat Jan 14 09:49: local server time

ZunZun.com. User-Selectable Polynomial. Sat Jan 14 09:49: local server time ZunZun.com User-Selectable Polynomial y = a + bx 1 + cx 2 + dx 3 + fx 4 + gx 5 Sat Jan 14 09:49:08 2012 local server time Coefficients y = a + bx 1 + cx 2 + dx 3 + fx 4 + gx 5 Fitting target of sum of

More information

Subset Selection in Multiple Regression

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

More information