Correlations. Butir 1 Pearson Correlation ** Sig. (2-tailed) N
|
|
|
- Laurence Stevenson
- 6 years ago
- Views:
Transcription
1 109 Lampiran olahan Data. 1. Nilai Pelanggan (X1) s Butir 1 Butir 2 Butir 3 Butir Total Butir ** Sig. (2-tailed) N Butir ** Sig. (2-tailed) N Butir ** Sig. (2-tailed) N Butir Total.441 **.735 **.472 ** 1 Sig. (2-tailed) N **. is significant at the 0.01 level (2-tailed). 2. Daya Tarik Iklan (X2) s Butir1 Butir2 Butir3 Butir4 Butir5 ButirTotal Butir **.469 **.375 **.501 **.819 ** Butir2.527 ** **.244 *.398 **.721 ** Sig. (2-tailed) Butir3.469 **.446 ** *.399 **.715 ** Sig. (2-tailed) Butir4.375 **.244 *.254 * **.623 ** Sig. (2-tailed)
2 110 Butir5.501 **.398 **.399 **.377 ** ** ButirTotal.819 **.721 **.715 **.623 **.722 ** 1 **. is significant at the 0.01 level (2-tailed). *. is significant at the 0.05 level (2-tailed). 3. Kompetensi Tenaga Penjual (X3) s Butir1 Butir2 Butir3 Butir4 Butir5 Butir6 Butir1 Butir2 Butir3 Butir4 Butir5 Butir6 ButirTotal **.630 **.576 **.624 **.497 **.818 ** ** **.589 **.586 **.462 **.802 ** **.637 ** **.667 **.663 **.861 ** **.589 **.747 ** **.761 **.868 ** **.586 **.667 **.675 ** **.853 ** **.462 **.663 **.761 **.741 ** **.000 ButirTotal.818 **.802 **.861 **.868 **.853 **.821 ** **. is significant at the 0.01 level (2-tailed).
3 Motivasi (X4) Butir1 s Butir1 Butir2 Butir3 Butir4 Butir5 Butir6 ButirTotal *.423 **.243 *.219 *.392 **.667 ** Sig. (2-tailed) Butir2.243 * ** Sig. (2-tailed) Butir3.423 ** *.332 **.420 **.738 ** Sig. (2-tailed) Butir4.243 * * **.227 *.504 ** Sig. (2-tailed) Butir5.219 * **.309 ** **.600 ** Sig. (2-tailed) Butir6.392 ** **.227 *.313 ** ** Sig. (2-tailed) ButirTotal.667 **.461 **.738 **.504 **.600 **.711 ** *. is significant at the 0.05 level (2-tailed). **. is significant at the 0.01 level (2-tailed).
4 Kepuasan Pelanggan (X5) s Butir1 Butir2 Butir3 Butir4 Butir5 ButirTotal Butir **.575 **.597 **.554 **.852 ** Butir2.719 ** **.542 **.516 **.837 ** Butir3.575 **.669 ** **.673 **.843 ** Butir4.597 **.542 **.628 ** **.811 ** Butir5.554 **.516 **.673 **.642 ** ** ButirTotal.852 **.837 **.843 **.811 **.804 ** 1 **. is significant at the 0.01 level (2-tailed). 6. Loyalitas Pelanggan (Y) s Butir1 Butir2 Butir3 Butir4 ButirTotal Butir **.469 **.345 **.738 ** Sig. (2-tailed) N Butir2.589 ** **.539 **.841 ** Sig. (2-tailed) N Butir3.469 **.490 ** **.785 ** Sig. (2-tailed) N Butir4.345 **.539 **.360 ** ** Sig. (2-tailed) N ButirTotal.738 **.841 **.785 **.724 ** 1 Sig. (2-tailed) N **. is significant at the 0.01 level (2-tailed).
5 113 Realibilitas Reliability Statistics Cronbach's Alpha N of Items Item-Total Statistics Scale Mean if Item Scale Variance if Corrected Item-Total Cronbach's Alpha if Deleted Butir Butir Butir Reliability Statistics Cronbach's Alpha N of Items Item-Total Statistics Scale Mean if Item Scale Variance if Corrected Item-Total Cronbach's Alpha if Deleted Butir Butir Butir Butir Butir Reliability Statistics Cronbach's Alpha N of Items.914 6
6 114 Item-Total Statistics Scale Mean if Item Scale Variance if Corrected Item-Total Cronbach's Alpha if Deleted Butir Butir Butir Butir Butir Butir Reliability Statistics Cronbach's Alpha N of Items Item-Total Statistics Scale Mean if Item Scale Variance if Corrected Item-Total Cronbach's Alpha if Deleted Butir Butir Butir Butir Butir Butir Reliability Statistics Cronbach's Alpha N of Items.883 5
7 115 Item-Total Statistics Scale Mean if Item Scale Variance if Corrected Item-Total Cronbach's Alpha if Deleted Butir Butir Butir Butir Butir Reliability Statistics Cronbach's Alpha N of Items Item-Total Statistics Scale Mean if Item Scale Variance if Corrected Item-Total Cronbach's Alpha if Deleted Butir Butir Butir Butir
8 116 One-Sample Kolmogorov-Smirnov Test Unstandardized Residual N 100 Normal Parameters a Mean Std. Deviation Most Extreme Differences Absolute.067 Positive.050 Negative Kolmogorov-Smirnov Z.666 Asymp. Sig. (2-tailed).766 a. Test distribution is Normal.
9 117 Regression Descriptive Statistics Mean Std. Deviation N Loyalitas Pelanggan Nilai Pelanggan Daya Tarik Iklan Kompetensi Tenaga Penjual Motivasi Kepuasan Pelanggan
10 118 Variables Entered/Removed b Model Variables Entered Variables Removed Method 1 Kepuasan Pelanggan, Nilai Pelanggan, Motivasi, Daya Tarik Iklan, Kompetensi Tenaga Penjual a. Enter a. All requested variables entered. b. Dependent Variable: Loyalitas Pelanggan Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate a a. Predictors: (Constant), Kepuasan Pelanggan, Motivasi, Daya Tarik Iklan, Nilai Pelanggan, Kompetensi Tenaga Penjual b. Dependent Variable: Loyalitas Pelanggan ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression a Residual Total a. Predictors: (Constant), Kepuasan Pelanggan, Nilai Pelanggan, Motivasi, Daya Tarik Iklan, Kompetensi Tenaga Penjual b. Dependent Variable: Loyalitas Pelanggan
11 119 Coefficients a Unstandardized Coefficients Standardized Coefficients Collinearity Statistics Model B Std. Error Beta t Sig. Tolerance VIF 1 (Constant) Nilai Pelanggan Daya Tarik Iklan Kompetensi Penjual Tenaga Motivasi Kepuasan Pelanggan a. Dependent Variable: Loyalitas Pelanggan Collinearity Diagnostics a Variance Proportions Daya Kompetensi Condition Nilai Tarik Tenaga Kepuasan Model Dimension Eigenvalue Index (Constant) Pelanggan Iklan Penjual Motivasi Pelanggan a. Dependent Variable: Loyalitas Pelanggan
12 120 Regression Coefficients a Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) Nilai Pelanggan a. Dependent Variable: Loyalitas Pelanggan Coefficients a Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) Daya Tarik Iklan a. Dependent Variable: Loyalitas Pelanggan Coefficients a Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) Kompetensi Penjual Tenaga a. Dependent Variable: Loyalitas Pelanggan Coefficients a Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) Motivasi a. Dependent Variable: Loyalitas Pelanggan
13 121 Coefficients a Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) Kepuasan Pelanggan a. Dependent Variable: Loyalitas Pelanggan Frequency Table Jenis Kelamin Frequency Percent Valid Percent Cumulative Percent Valid Laki-laki Perempuan Total Umur Frequency Percent Valid Percent Cumulative Percent Valid
14 Total Pendidikan Frequency Percent Valid Percent Cumulative Percent Valid D III/Sarjana muda SLTA/Sederajat Strata Strata 2 (S2) Total Pekerjaan Frequency Percent Valid Percent Cumulative Percent Valid BUMN Ibu rumah tangga Lainnya Mahasiswa Pegawai Negeri Pegawai Swasta Pengusaha Profesional TNI / Polisi Total
15 123 Lama Menggunakan Kartu Frequency Percent Valid Percent Cumulative Percent Valid < 6 bulan > 5 tahun tahun tahun tahun bulan Total Jenis Kartu Frequency Percent Valid Percent Cumulative Percent Valid Corporate Everyday Gold & Silver Card Gold Card Golf Card Hypermart Silver Lainnya Platinum Card Platinum Card, Skyz Platinum Card,Skyz C Skyz Card Skyz Card, Corporate Total
16 124 Penghasilan Frequency Percent Valid Percent Cumulative Percent Valid < 5 juta > 20 juta juta juta juta Total
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
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
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
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
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
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
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.
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
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
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
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
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
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'.
Descriptives. Graph. [DataSet1] C:\Documents and Settings\BuroK\Desktop\Prestige.sav
![Descriptives. Graph. [DataSet1] C:\Documents and Settings\BuroK\Desktop\Prestige.sav](/thumbs/82/84761217.jpg "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
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
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
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
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
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
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
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 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
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
- 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
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
Lampiran 2 MASTER TABEL
64 Lampiran 2 MASTER TABEL No. No. Responden Umur Pendidikan Pekerjaan Paritas Kanker 1 427363 35 S1 PNS 4 Tidak 2 504024 36 SMA IRT 4 Tidak 3 500316 35 SMA IRT 5 Tidak 4 504014 35 SMA PNS 1 Tidak 5 447158
Everything taken from (Hair, Hult et al. 2017) but some formulas taken elswere or created by Erik Mønness.
/Users/astacbf/Desktop/Assessing smartpls (engelsk).docx 1/8 Assessing smartpls Everything taken from (Hair, Hult et al. 017) but some formulas taken elswere or created by Erik Mønness. Run PLS algorithm,
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
Lecture 7: Linear Regression (continued)
Lecture 7: Linear Regression (continued) Reading: Chapter 3 STATS 2: Data mining and analysis Jonathan Taylor, 10/8 Slide credits: Sergio Bacallado 1 / 14 Potential issues in linear regression 1. Interactions
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
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
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
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
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
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,
Introduction to Statistical Analyses in SAS
Introduction to Statistical Analyses in SAS Programming Workshop Presented by the Applied Statistics Lab Sarah Janse April 5, 2017 1 Introduction Today we will go over some basic statistical analyses in
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
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
The problem we have now is called variable selection or perhaps model selection. There are several objectives.
STAT-UB.0103 NOTES for Wednesday 01.APR.04 One of the clues on the library data comes through the VIF values. These VIFs tell you to what extent a predictor is linearly dependent on other predictors. We
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
Stat 5100 Handout #14.a SAS: Logistic Regression
Stat 5100 Handout #14.a SAS: Logistic Regression Example: (Text Table 14.3) Individuals were randomly sampled within two sectors of a city, and checked for presence of disease (here, spread by mosquitoes).
Fathom Dynamic Data TM Version 2 Specifications
Data Sources Fathom Dynamic Data TM Version 2 Specifications Use data from one of the many sample documents that come with Fathom. Enter your own data by typing into a case table. Paste data from other
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
Psychology 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
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
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
Study Guide. Module 1. Key Terms
Study Guide Module 1 Key Terms general linear model dummy variable multiple regression model ANOVA model ANCOVA model confounding variable squared multiple correlation adjusted squared multiple correlation
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...
Genotype x Environmental Analysis with R for Windows
Genotype x Environmental Analysis with R for Windows Biometrics and Statistics Unit Angela Pacheco CIMMYT,Int. 23-24 Junio 2015 About GEI In agricultural experimentation, a large number of genotypes are
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
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
Solution to Bonus Questions
Solution to Bonus Questions Q2: (a) The histogram of 1000 sample means and sample variances are plotted below. Both histogram are symmetrically centered around the true lambda value 20. But the sample
Measures of Dispersion
Measures of Dispersion 6-3 I Will... Find measures of dispersion of sets of data. Find standard deviation and analyze normal distribution. Day 1: Dispersion Vocabulary Measures of Variation (Dispersion
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.
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.
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
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
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
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.
Reference
Leaning diary: research methodology 30.11.2017 Name: Juriaan Zandvliet Student number: 291380 (1) a short description of each topic of the course, (2) desciption of possible examples or exercises done
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
Package robustreg. R topics documented: April 27, Version Date Title Robust Regression Functions
Version 0.1-10 Date 2017-04-25 Title Robust Regression Functions Package robustreg April 27, 2017 Author Ian M. Johnson Maintainer Ian M. Johnson Depends
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
Correctly Compute Complex Samples Statistics
SPSS Complex Samples 15.0 Specifications Correctly Compute Complex Samples Statistics When you conduct sample surveys, use a statistics package dedicated to producing correct estimates for complex sample
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
StatCalc User Manual. Version 9 for Mac and Windows. Copyright 2018, AcaStat Software. All rights Reserved.
StatCalc User Manual Version 9 for Mac and Windows Copyright 2018, AcaStat Software. All rights Reserved. http://www.acastat.com Table of Contents Introduction... 4 Getting Help... 4 Uninstalling StatCalc...
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
Regression on SAT Scores of 374 High Schools and K-means on Clustering Schools
Regression on SAT Scores of 374 High Schools and K-means on Clustering Schools Abstract In this project, we study 374 public high schools in New York City. The project seeks to use regression techniques
Orange Juice data. Emanuele Taufer. 4/12/2018 Orange Juice data (1)
Orange Juice data Emanuele Taufer file:///c:/users/emanuele.taufer/google%20drive/2%20corsi/5%20qmma%20-%20mim/0%20labs/l10-oj-data.html#(1) 1/31 Orange Juice Data The data contain weekly sales of refrigerated
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
RSM Split-Plot Designs & Diagnostics Solve Real-World Problems
RSM Split-Plot Designs & Diagnostics Solve Real-World Problems Shari Kraber Pat Whitcomb Martin Bezener Stat-Ease, Inc. Stat-Ease, Inc. Stat-Ease, Inc. 221 E. Hennepin Ave. 221 E. Hennepin Ave. 221 E.
Stat 5100 Handout #19 SAS: Influential Observations and Outliers
Stat 5100 Handout #19 SAS: Influential Observations and Outliers Example: Data collected on 50 countries relevant to a cross-sectional study of a lifecycle savings hypothesis, which states that the response
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
Section 2.2: Covariance, Correlation, and Least Squares
Section 2.2: Covariance, Correlation, and Least Squares Jared S. Murray The University of Texas at Austin McCombs School of Business Suggested reading: OpenIntro Statistics, Chapter 7.1, 7.2 1 A Deeper
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
Evaluating the Numerical Accuracy of Analyse-it for Microsoft Excel
Evaluating the Numerical Accuracy of Analyse-it for Microsoft Excel This document describes the performance of Analyse-it for Microsoft Excel version 4.00 against the NIST StRD. Analyse-it for Microsoft
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
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
Lab 07: Multiple Linear Regression: Variable Selection
Lab 07: Multiple Linear Regression: Variable Selection OBJECTIVES 1.Use PROC REG to fit multiple regression models. 2.Learn how to find the best reduced model. 3.Variable diagnostics and influential statistics
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
Chapter 8: Regression. Self-test answers
Chapter 8: Regression Self-test answers SELF-TEST Residuals are used to compute which of the three sums of squares? The residuals are used to calculate the residual sum of squares (SSR). This value is
Student Version 8 AVERILL M. LAW & ASSOCIATES
ExpertFit Student Version 8 AVERILL M. LAW & ASSOCIATES 4729 East Sunrise Drive, # 462 Tucson, AZ 85718 Phone: 520-795-6265 E-mail: averill@simulation.ws Website: www.averill-law.com 1. Introduction ExpertFit
Correctly Compute Complex Samples Statistics
PASW Complex Samples 17.0 Specifications Correctly Compute Complex Samples Statistics When you conduct sample surveys, use a statistics package dedicated to producing correct estimates for complex sample
Further Maths Notes. Common Mistakes. Read the bold words in the exam! Always check data entry. Write equations in terms of variables
Further Maths Notes Common Mistakes Read the bold words in the exam! Always check data entry Remember to interpret data with the multipliers specified (e.g. in thousands) Write equations in terms of variables
Accelerated Life Testing Module Accelerated Life Testing - Overview
Accelerated Life Testing Module Accelerated Life Testing - Overview The Accelerated Life Testing (ALT) module of AWB provides the functionality to analyze accelerated failure data and predict reliability
610 R12 Prof Colleen F. Moore Analysis of variance for Unbalanced Between Groups designs in R For Psychology 610 University of Wisconsin--Madison
610 R12 Prof Colleen F. Moore Analysis of variance for Unbalanced Between Groups designs in R For Psychology 610 University of Wisconsin--Madison R is very touchy about unbalanced designs, partly because
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
LISA: Explore JMP Capabilities in Design of Experiments. Liaosa Xu June 21, 2012
LISA: Explore JMP Capabilities in Design of Experiments Liaosa Xu June 21, 2012 Course Outline Why We Need Custom Design The General Approach JMP Examples Potential Collinearity Issues Prior Design Evaluations
CHAPTER 7 ASDA ANALYSIS EXAMPLES REPLICATION-SPSS/PASW V18 COMPLEX SAMPLES
CHAPTER 7 ASDA ANALYSIS EXAMPLES REPLICATION-SPSS/PASW V18 COMPLEX SAMPLES GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures
STA 570 Spring Lecture 5 Tuesday, Feb 1
STA 570 Spring 2011 Lecture 5 Tuesday, Feb 1 Descriptive Statistics Summarizing Univariate Data o Standard Deviation, Empirical Rule, IQR o Boxplots Summarizing Bivariate Data o Contingency Tables o Row
( ) = Y ˆ. Calibration Definition A model is calibrated if its predictions are right on average: ave(response Predicted value) = Predicted value.
Calibration OVERVIEW... 2 INTRODUCTION... 2 CALIBRATION... 3 ANOTHER REASON FOR CALIBRATION... 4 CHECKING THE CALIBRATION OF A REGRESSION... 5 CALIBRATION IN SIMPLE REGRESSION (DISPLAY.JMP)... 5 TESTING
Example 1 of panel data : Data for 6 airlines (groups) over 15 years (time periods) Example 1
Panel data set Consists of n entities or subjects (e.g., firms and states), each of which includes T observations measured at 1 through t time period. total number of observations : nt Panel data have
Regression on the trees data with R
> trees Girth Height Volume 1 8.3 70 10.3 2 8.6 65 10.3 3 8.8 63 10.2 4 10.5 72 16.4 5 10.7 81 18.8 6 10.8 83 19.7 7 11.0 66 15.6 8 11.0 75 18.2 9 11.1 80 22.6 10 11.2 75 19.9 11 11.3 79 24.2 12 11.4 76
Using the DATAMINE Program
6 Using the DATAMINE Program 304 Using the DATAMINE Program This chapter serves as a user s manual for the DATAMINE program, which demonstrates the algorithms presented in this book. Each menu selection
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
Generalized Procrustes Analysis Example with Annotation
Generalized Procrustes Analysis Example with Annotation James W. Grice, Ph.D. Oklahoma State University th February 4, 2007 Generalized Procrustes Analysis (GPA) is particularly useful for analyzing repertory
Spatial Patterns Point Pattern Analysis Geographic Patterns in Areal Data
Spatial Patterns We will examine methods that are used to analyze patterns in two sorts of spatial data: Point Pattern Analysis - These methods concern themselves with the location information associated
Resources for statistical assistance. Quantitative covariates and regression analysis. Methods for predicting continuous outcomes.
Resources for statistical assistance Quantitative covariates and regression analysis Carolyn Taylor Applied Statistics and Data Science Group (ASDa) Department of Statistics, UBC January 24, 2017 Department
SLStats.notebook. January 12, Statistics:
Statistics: 1 2 3 Ways to display data: 4 generic arithmetic mean sample 14A: Opener, #3,4 (Vocabulary, histograms, frequency tables, stem and leaf) 14B.1: #3,5,8,9,11,12,14,15,16 (Mean, median, mode,
Using HLM for Presenting Meta Analysis Results. R, C, Gardner Department of Psychology
Data_Analysis.calm: dacmeta Using HLM for Presenting Meta Analysis Results R, C, Gardner Department of Psychology The primary purpose of meta analysis is to summarize the effect size results from a number