Statistika Penelitian Bisnis & Manajemen

Total Page:16

File Type:pdf, Size:1020Kb

Statistika Penelitian Bisnis & Manajemen Statistika Penelitian Bisnis & Manajemen Statistik Parametrik, Non-Parametrik, Regresi Linier, Analisis Jalur dan SEM Oleh : Prof. Dr. H. Siswoyo Haryono, MM, MPd. Penyusun : Dwi Puryanto, SE, MM ISBN : Cetakan pertama: 2020 Penerbit LP3M UMY Universitas Muhammadiyah Yogyakarta Hak Cipta dilindungi Undang-undang. Dilarang mengutip atau memperbanyak sebagian atau seluruh buku dalam bentuk apapun, tanpa ijin tertulis dari penerbit. Statistika Penelitian Manajemen i Prakata Pertama-tama penulis panjatkan puji dan syukur kehadirat Tuhan YME, Allah SWT, bahwa pada akhirnya buku ini dapat diterbitkan. Tujuan diterbitkannya buku ini adalah untuk membantu para mahasiswa dalam mempelajari Statistika (Ilmu Statistik) khususnya dalam aplikasi penelitian bidang manajemen. Mengingat statistik pada saat ini telah banyak memanfaatkan program-program computer, diantaranya SPSS, dan SEM maka buku ini diberi judul Statistika Penelitian Bisnis dan Manajemen (Statistik Parametrik, Non-Parametrik, Regresi Linier, Analisis Jalur dan SEM). Sasaran pembaca buku ini adalah mahasiswa Program Sarjana Strata Satu (S-1) Magister (S-2) dan Doktor (S-3) yang sedang menyiapkan diri menulis Skripsi, Tesis atau Disertasi. Sasaran lain adalah para manajer atau praktisi bisnis yang berminat memperdalam ilmu Penelitian di bidang Bisnis dan Manajemen. Pada kesempatan ini penulis menyampaikan ucapan terimakasih dan penghargaan yang tulus kepada semua pihak yang telah membantu tersusunnya buku ini. Penulis menyadari bahwa masih terdapat kekurangsempurnaan dalam penulisan buku ini. Oleh karena itu segala bentuk kritik dan saran yang membangun akan diterima dengan senang hati demi penyempurnaan buku ini di masa mendatang. Yogyakarta, 2020 Penulis, Prof. Dr. H. Siswoyo Haryono, MM, M.Pd. ii Statistika Penelitian Manajemen DAFTAR ISI Prakata................................................................................................................... Daftar Isi ...................................................................................................................... Daftar Tabel................................................................................................................ Daftar Gambar.......................................................................................................... BAB I PENDAHULUAN............................................................................. 1 A. Sejarah dan Perkembangan Statistik............................................. 1 B. Statistik Modern.............................................................................. 3 C. Statistik dan Ilmu Pengetahuan.................................................... 3 D. Statistik dan Komputer.................................................................... 6 BAB II STATISTIKA DAN PENELITIAN................................................. 9 A. Pengertian Penelitian.................................................................... 9 B. Proses Penelitian......................................................................... 9 C. Peranan Statistik dalam Penelitian................................................ 10 D. Pembagian Statistik dan Rancangan Penelitian............................. 11 BAB III DATA PENELITIAN........................................................................ 12 A. Sumber Data............................................................................... 12 B. Jenis-jenis Data.......................................................................... 12 C. Variabel Penelitian..................................................................... 15 D. Populasi, Sampel dan Metode Sampling...................................... 15 BAB IV INSTRUMEN PENELITIAN.......................................................... 37 A. Pengembangan Instrumen Penelitian........................................... 37 B. Uji Validitas dan Reliabilitas........................................................ 37 1. Uji Korelasi Pearson dan Alpha Cronbach............................ 38 2. Uji Corrected Item-Total Correlation.................................... 42 BAB V PROBABILITAS DAN DISTRIBUSI NORMAL………………... 44 A. Kejadian ………………………………………………………… 44 B. Peluang …………………………………………………………. 44 C. Distribusi Peluang ……………………………………………… 45 D. Distribusi Binomial ……………………………………………. 48 E. Distribusi Poisson dan Distribusi Normal ………………………. 50 BAB VI PENGUJIAN HIPOTESIS ………………………………………… 55 A. Pengertian Hipotesis ……………………………………………. 55 B. Prosedur Pengujian Hipotesis ………………………………….. 56 C. Langkah-langkah Pengujian Hipotesis …………………………. 58 D. Kesalahan Tipe I dan II ………………………………………… 61 E. Two Tailed and One Tailed Hypothesis Testing ……………….. 63 F. Pengujian dengan Probabilitas (P-Value) ………………………. 69 Statistika Penelitian Manajemen iii BAB VII STATISTIK DESKRIPTIF.............................................................. 72 A. Pengertian Statistik Deskriptif..................................................... 72 B. Pengaturan Data........................................................................... 73 C. Distrubusi Frekwensi.................................................................. 74 D. Nilai Tengah (Central Tendency)................................................. 75 E. Pengukuran Dispersi/Variasi....................................................... 81 F. Pengukuran Bentuk Sebaran Data............................................... 84 G. Statistik Deskriptif dan SPSS...................................................... 89 H. Deskriptif Data dengan Grafik..................................................... 92 BAB VIII STATISTIK INFERENSIAL.......................................................... 94 A. Pengertian Statistik Inferensial...................................................... 94 B. Jenis-jenis Sumber Data............................................................... 95 BAB IX UJI PERSYARATAN ANALISIS REGRESI................................. 97 A. Uji Normalitas Sebaran Data Manual.......................................... 97 B. Uji Normalitas Data dengan SPSS................................................ 104 C. Uji Homogenitas Sumber Data................................................... 106 D. Uji Linieritas Distribusi Data...................................................... 109 BAB X ANALISIS REGRESI DAN KORELASI SEDERHANA............... 114 A. Sejarah Analisis Regresi.............................................................. 114 B. Analisis Univariate, Bivariate, dan Multivariate.......................... 117 C. Estimasi dengan Metode Garis Regresi........................................ 118 D. Metode Kuadrat Terkecil Biasa atau OLS (Ordinary Least Square method).................................................. 120 E. Korelasi dan Koefisien Korelasi................................................... 125 BAB XI ANALISIS REGRESI, KORELASI DAN MULTIVARIATE.............. 132 A. Analisis Regresi dan Korelasi Ganda........................................... 132 B. Model Umum Regresi Ganda....................................................... 133 C. Analisis Ganda Manual................................................................ 136 D. Model Umum Analisis Multivariate............................................. 148 E. Uji Multikolinearitas..................................................................... 149 F. Uji Autokorelasi.......................................................................... 152 G. Uji Heterokedastisitas................................................................... 154 BAB XII STATISTIK NON-PARAMETRIK................................................. 160 A. Statistik Non Parametrik Dalam Penelitian................................... 160 B. Panduan Menggunakan Statistik Non Parametrik......................... 160 C. Keunggulan dan Kelemahan Statistik Non Parametrik................. 163 D. Contoh Analisis Statistik Non Parametrik dengan SPSS.............. 164 Daftar Pustaka................................................................................................................... 199 DAFTAR TABEL iv Statistika Penelitian Manajemen Tabel 1.1. Nama Aplikasi Statistik………………………………………………………………….. 6 Tabel 3.1. Lambang Parameter dan Statistik……………………………………………………… 21 Tabel 4.1. Interprestasi Nilai Alpha Terhadap Reliabilitas……………………………………… 38 Tabel 6.1. Kesalahan Hipotesis………………………………………………………………………….. 62 Tabel 6.2. Pedoman Probabilitas Distribusi dalam Pengujian Hipotesis……………………. 63 Tabel 6.3. Formulasi Statistik Pengujian Hipotesis………………………………………………… 64 Tabel 6.4. Nilai Alpha dan Z Istimewa…………………………………………………………………… 65 Tabel 7.1. Data Mentah Penjualan Mobil……………………………………………………………… 73 Tabel 7.2. Tabel Bantu Statistik Deskriptif……………………………………………………………. 75 Tabel 7.3. Jumlah Order Penyemprotan Anti Rayap…………………………………………….. 78 Tabel 7.4. Distribusi Pendapatan menurut Distribusi Normal………………………………. 84 Tabel 7.5. Data Sales…………………………………………………………………………………………… 89 Tabel 9.1. Luas Wilayah Kurva Normal Baku………………………………………………………… 101 Tabel 9.2. Tabel Bantu Uji Normalitas X2-test dengan Standar Kurnal Normal …………. 102 Tabel 9.3. Biaya Promosi, Training dan Jumlah Penjualan……………………………………….. 110 Tabel 10.1. Hubungan yang Mungkin Terjadi dalam Regresi…………………………………. 115 Tabel 10.2. Nilai Penyimpangan Absolut dari Dua Grafik…………………………………….. 122 Tabel 10.3. Jumlah Nilai Absolut Error dari Dua Grafik……………………………………….. 123 Tabel 10.4. Hasil Least Square dari Estimator……………………………………………………….. 123 Tabel 10.5. Menghitung Nilai Pembilang…………………………………………………………….. 125 Tabel 10.6. Ilustrasi Korelasi
Recommended publications
  • Binomials, Contingency Tables, Chi-Square Tests
    Binomials, contingency tables, chi-square tests Joe Felsenstein Department of Genome Sciences and Department of Biology Binomials, contingency tables, chi-square tests – p.1/16 Confidence limits on a proportion To work out a confidence interval on binomial proportions, use binom.test. If we want to see whether 0.20 is too high a probability of Heads when we observe 5 Heads out of 50 tosses, we use binom.test(5, 50, 0.2) which gives probability of 5 or fewer Heads as 0.09667, so a test does not exclude 0.20 as the Heads probability. > binom.test(5,50,0.2) Exact binomial test data: 5 and 50 number of successes = 5, number of trials = 50, p-value = 0.07883 alternative hypothesis: true probability of success is not equal to 0.2 95 percent confidence interval: 0.03327509 0.21813537 sample estimates: probability of success 0.1 Binomials, contingency tables, chi-square tests – p.2/16 Confidence intervals and tails of binomials Confidence limits for p if there are 5 Heads out of 50 tosses p = 0.0332750 is 0.025 p = 0.21813537 is 0.025 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Binomials, contingency tables, chi-square tests – p.3/16 Testing equality of binomial proportions How do we test whether two coins have the same Heads probability? This is hard, but there is a good approximation, the chi-square test. You set up a 2 × 2 table of numbers of outcomes: Heads Tails Coin #1 15 25 Coin #2 9 31 In fact the chi-square test can test bigger tables: R rows by C columns.
    [Show full text]
  • University of Pardubice
    UNIVERSITY OF PARDUBICE FACULTY OF ECONOMICS AND ADMINISTRATION Institute of Economic Sciences EVALUATION OF THE ROLE OF CRUCIAL IMPACTS ON NETWORKS FOR TECHNOLOGICAL INNOVATION Ing. Anderson, Henry Junior SUPERVISOR: Assoc. Prof. Ing. Jan Stejskal, Ph.D. Dissertation thesis 2020 TABLE OF CONTENTS INTRODUCTION ..................................................................................................................... 1 1. CONCEPTUAL FRAMEWORK ................................................................................... 3 1.1. Innovation Systems: Technological Innovation Systems............................................ 3 1.1.1. Functional Processes of Technological Innovation System................................. 4 1.1.2. Emergence and Renewed Direction of Technological Innovation System ......... 7 1.2. Contemporary Indicators of Modern Technological Innovation................................. 9 1.2.1. Research Systems................................................................................................. 9 1.2.2. Financial Cradles ............................................................................................... 10 1.2.3. Human Capital ................................................................................................... 12 1.2.4. Cooperation ........................................................................................................ 13 1.3. Regional Attractiveness for Innovation and Foreign Direct Investments ................. 14 1.4. Innovation Indicators’ Contribution
    [Show full text]
  • Binomial Test Models and Item Difficulty
    Binomial Test Models and Item Difficulty Wim J. van der Linden Twente University of Technology In choosing a binomial test model, it is impor- have characteristic functions of the Guttman type. tant to know exactly what conditions are imposed In contrast, the stochastic conception allows non- on item difficulty. In this paper these conditions Guttman items but requires that all characteristic are examined for both a deterministic and a sto- functions must intersect at the same point, which chastic conception of item responses. It appears implies equal classically defined difficulty. The that they are more restrictive than is generally beta-binomial model assumes identical char- understood and differ for both conceptions. When acteristic functions for both conceptions, and this the binomial model is applied to a fixed examinee, also implies equal difficulty. Finally, the compound the deterministic conception imposes no conditions binomial model entails no restrictions on item diffi- on item difficulty but requires instead that all items culty. In educational and psychological testing, binomial models are a class of models increasingly being applied. For example, in the area of criterion-referenced measurement or mastery testing, where tests are usually conceptualized as samples of items randomly drawn from a large pool or do- main, binomial models are frequently used for estimating examinees’ mastery of a domain and for de- termining sample size. Despite the agreement among several writers on the usefulness of binomial models, opinions seem to differ on the restrictions on item difficulties implied by the models. Mill- man (1973, 1974) noted that in applying the binomial models, items may be relatively heterogeneous in difficulty.
    [Show full text]
  • Bitest — Binomial Probability Test
    Title stata.com bitest — Binomial probability test Description Quick start Menu Syntax Option Remarks and examples Stored results Methods and formulas Reference Also see Description bitest performs exact hypothesis tests for binomial random variables. The null hypothesis is that the probability of a success on a trial is #p. The total number of trials is the number of nonmissing values of varname (in bitest) or #N (in bitesti). The number of observed successes is the number of 1s in varname (in bitest) or #succ (in bitesti). varname must contain only 0s, 1s, and missing. bitesti is the immediate form of bitest; see [U] 19 Immediate commands for a general introduction to immediate commands. Quick start Exact test for probability of success (a = 1) is 0.4 bitest a = .4 With additional exact probabilities bitest a = .4, detail Exact test that the probability of success is 0.46, given 22 successes in 74 trials bitesti 74 22 .46 Menu bitest Statistics > Summaries, tables, and tests > Classical tests of hypotheses > Binomial probability test bitesti Statistics > Summaries, tables, and tests > Classical tests of hypotheses > Binomial probability test calculator 1 2 bitest — Binomial probability test Syntax Binomial probability test bitest varname== #p if in weight , detail Immediate form of binomial probability test bitesti #N #succ #p , detail by is allowed with bitest; see [D] by. fweights are allowed with bitest; see [U] 11.1.6 weight. Option Advanced £ £detail shows the probability of the observed number of successes, kobs; the probability of the number of successes on the opposite tail of the distribution that is used to compute the two-sided p-value, kopp; and the probability of the point next to kopp.
    [Show full text]
  • Organizational Capabilities for Family Firm Sustainability: the Role of Knowledge Accumulation and Family Essence
    sustainability Article Organizational Capabilities for Family Firm Sustainability: The Role of Knowledge Accumulation and Family Essence Ismael Barros-Contreras 1, Jesús Manuel Palma-Ruiz 2,* and Angel Torres-Toukoumidis 3 1 Instituto de Gestión e Industria, Universidad Austral de Chile, Valdivia 5480000, Chile; [email protected] 2 Facultad de Contaduría y Administración, Universidad Autónoma de Chihuahua, Chihuahua 31000, Mexico 3 Social Science Knowledge and Human Behavior, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador; [email protected] * Correspondence: [email protected] Abstract: While prior studies recognize the importance of organizational capabilities for family firm sustainability, current research has still failed to empirically identify the role of different types of knowledge accumulation with regard to these organizational capabilities. Based on the dynamic capabilities theory, the main goal of this paper is to address this research gap and to explore the rela- tionships between both internal and external knowledge accumulation, and ordinary organizational capabilities. This research also contributes to analyzing the complex effect of the family firm essence, influenced by both family involvement and generational involvement levels, as an antecedent of internal and external knowledge accumulation. Our analysis of 102 non-listed Spanish family firms shows that the family firm essence, which is influenced by the family involvement, strengthens only the internal knowledge accumulation but not the external one. Furthermore, our study also reveals Citation: Barros-Contreras, I.; that both internal and knowledge accumulation are positively related to ordinary capabilities. Palma-Ruiz, J.M.; Torres-Toukoumidis, A. Keywords: dynamic capabilities; internal knowledge accumulation; external knowledge accumu- Organizational Capabilities for lation; family essence; family firms; ordinary capabilities; structural equation modeling; partial Family Firm Sustainability: The Role least squares of Knowledge Accumulation and Family Essence.
    [Show full text]
  • Estimating Complex Production Functions: the Importance of Starting Values
    Version: January 26 Estimating complex production functions: The importance of starting values Mark Neal Presented at the 51st Australian Agricultural and Resource Economics Society Conference, Queenstown, New Zealand, 14-16 February, 2007. Postdoctoral Research Fellow Risk and Sustainable Management Group University of Queensland Room 519, School of Economics, Colin Clark Building (39) University of Queensland, St Lucia, QLD, 4072 Email: [email protected] Ph: +61 (0) 7 3365 6601 Fax: +61 (0) 7 3365 7299 http://www.uq.edu.au/rsmg Acknowledgements Chris O’Donnell helpfully provided access to Gauss code that he had written for estimation of latent class models so it could be translated into Shazam. Leighton Brough (UQ), Ariel Liebman (UQ) and Tom Pechey (UMelb) helped by organising a workshop with Nimrod/enFuzion at UQ in June 2006. enFuzion (via Rok Sosic) provided a limited license for use in the project. Leighton Brough, tools coordinator for the ARC Centre for Complex Systems (ACCS), assisted greatly in setting up an enFuzion grid and collating the large volume of results. Son Nghiem provided assistance in collating and analysing data as well as in creating some of the figures. Page 1 of 30 Version: January 26 ABSTRACT Production functions that take into account uncertainty can be empirically estimated by taking a state contingent view of the world. Where there is no a priori information to allocate data amongst a small number of states, the estimation may be carried out with finite mixtures model. The complexity of the estimation almost guarantees a large number of local maxima for the likelihood function.
    [Show full text]
  • Apple / Shazam Merger Procedure Regulation (Ec)
    EUROPEAN COMMISSION DG Competition CASE M.8788 – APPLE / SHAZAM (Only the English text is authentic) MERGER PROCEDURE REGULATION (EC) 139/2004 Article 8(1) Regulation (EC) 139/2004 Date: 06/09/2018 This text is made available for information purposes only. A summary of this decision is published in all EU languages in the Official Journal of the European Union. Parts of this text have been edited to ensure that confidential information is not disclosed; those parts are enclosed in square brackets. EUROPEAN COMMISSION Brussels, 6.9.2018 C(2018) 5748 final COMMISSION DECISION of 6.9.2018 declaring a concentration to be compatible with the internal market and the EEA Agreement (Case M.8788 – Apple/Shazam) (Only the English version is authentic) TABLE OF CONTENTS 1. Introduction .................................................................................................................. 6 2. The Parties and the Transaction ................................................................................... 6 3. Jurisdiction of the Commission .................................................................................... 7 4. The procedure ............................................................................................................... 8 5. The investigation .......................................................................................................... 8 6. Overview of the digital music industry ........................................................................ 9 6.1. The digital music distribution value
    [Show full text]
  • Statistics and GIS Assistance Help with Statistics
    Statistics and GIS assistance An arrangement for help and advice with regard to statistics and GIS is now in operation, principally for Master’s students. How do you seek advice? 1. The users, i.e. students at INA, make direct contact with the person whom they think can help and arrange a time for consultation. Remember to be well prepared! 2. Doctoral students and postdocs register the time used in Agresso (if you have questions about this contact Gunnar Jensen). Help with statistics Research scientist Even Bergseng Discipline: Forest economy, forest policies, forest models Statistical expertise: Regression analysis, models with random and fixed effects, controlled/truncated data, some time series modelling, parametric and non-parametric effectiveness analyses Software: Stata, Excel Postdoc. Ole Martin Bollandsås Discipline: Forest production, forest inventory Statistics expertise: Regression analysis, sampling Software: SAS, R Associate Professor Sjur Baardsen Discipline: Econometric analysis of markets in the forest sector Statistical expertise: General, although somewhat “rusty”, expertise in many econometric topics (all-rounder) Software: Shazam, Frontier Associate Professor Terje Gobakken Discipline: GIS og long-term predictions Statistical expertise: Regression analysis, ANOVA and PLS regression Software: SAS, R Ph.D. Student Espen Halvorsen Discipline: Forest economy, forest management planning Statistical expertise: OLS, GLS, hypothesis testing, autocorrelation, ANOVA, categorical data, GLM, ANOVA Software: (partly) Shazam, Minitab og JMP Ph.D. Student Jan Vidar Haukeland Discipline: Nature based tourism Statistical expertise: Regression and factor analysis Software: SPSS Associate Professor Olav Høibø Discipline: Wood technology Statistical expertise: Planning of experiments, regression analysis (linear and non-linear), ANOVA, random and non-random effects, categorical data, multivariate analysis Software: R, JMP, Unscrambler, some SAS Ph.D.
    [Show full text]
  • Bab 1 Pendahuluan
    BAB 1 PENDAHULUAN Bab ini akan membahas pengertian dasar statistik dengan sub-sub pokok bahasan sebagai berikut : Sub Bab Pokok Bahasan A. Sejarah dan Perkembangan Statistik B. Tokoh-tokoh Kontributor Statistika C. Definisi dan Konsep Statistik Modern D. Kegunaan Statistik E. Pembagian Statistik F. Statistik dan Komputer G. Soal Latihan A. Sejarah dan Perkembangan Statistik Penggunaan istilah statistika berakar dari istilah-istilah dalam bahasa latin modern statisticum collegium (“dewan negara”) dan bahasa Italia statista (“negarawan” atau “politikus”). Istilah statistik pertama kali digunakan oleh Gottfried Achenwall (1719-1772), seorang guru besar dari Universitas Marlborough dan Gottingen. Gottfried Achenwall (1749) menggunakan Statistik dalam bahasa Jerman untuk pertama kalinya sebagai nama bagi kegiatan analisis data kenegaraan, dengan mengartikannya sebagai “ilmu tentang negara/state”. Pada awal abad ke- 19 telah terjadi pergeseran arti menjadi “ilmu mengenai pengumpulan dan klasifikasi data”. Sir John Sinclair memperkenalkan nama dan pengertian statistics ini ke dalam bahasa Inggris. E.A.W. Zimmerman mengenalkan kata statistics ke negeri Inggris. Kata statistics dipopulerkan di Inggris oleh Sir John Sinclair dalam karyanya: Statistical Account of Scotland 1791-1799. Namun demikian, jauh sebelum abad XVIII masyarakat telah mencatat dan menggunakan data untuk keperluan mereka. Pada awalnya statistika hanya mengurus data yang dipakai lembaga- lembaga administratif dan pemerintahan. Pengumpulan data terus berlanjut, khususnya melalui sensus yang dilakukan secara teratur untuk memberi informasi kependudukan yang selalu berubah. Dalam bidang pemerintahan, statistik telah digunakan seiring dengan perjalanan sejarah sejak jaman dahulu. Kitab perjanjian lama (old testament) mencatat adanya kegiatan sensus penduduk. Pemerintah kuno Babilonia, Mesir, dan Roma mengumpulkan data lengkap tentang penduduk dan kekayaan alam yang dimilikinya.
    [Show full text]
  • Construction of a Neuro-Immune-Cognitive Pathway-Phenotype Underpinning the Phenome Of
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 October 2019 doi:10.20944/preprints201910.0239.v1 Construction of a neuro-immune-cognitive pathway-phenotype underpinning the phenome of deficit schizophrenia. Hussein Kadhem Al-Hakeima, Abbas F. Almullab, Arafat Hussein Al-Dujailic, Michael Maes d,e,f. a Department of Chemistry, College of Science, University of Kufa, Iraq. E-mail: [email protected]. b Medical Laboratory Technology Department, College of Medical Technology, The Islamic University, Najaf, Iraq. E-mail: [email protected]. c Senior Clinical Psychiatrist, Faculty of Medicine, Kufa University. E-mail: [email protected]. d* Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; e Department of Psychiatry, Medical University of Plovdiv, Plovdiv, Bulgaria; f IMPACT Strategic Research Centre, Deakin University, PO Box 281, Geelong, VIC, 3220, Australia. Corresponding author Prof. Dr. Michael Maes, M.D., Ph.D. Department of Psychiatry Faculty of Medicine Chulalongkorn University Bangkok Thailand 1 © 2019 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 October 2019 doi:10.20944/preprints201910.0239.v1 https://scholar.google.co.th/citations?user=1wzMZ7UAAAAJ&hl=th&oi=ao [email protected]. 2 Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 October 2019 doi:10.20944/preprints201910.0239.v1 Abstract In schizophrenia, pathway-genotypes may be constructed by combining interrelated immune biomarkers with changes in specific neurocognitive functions that represent aberrations in brain neuronal circuits. These constructs provide insight on the phenome of schizophrenia and show how pathway-phenotypes mediate the effects of genome X environmentome interactions on the symptomatology/phenomenology of schizophrenia.
    [Show full text]
  • Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models
    San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-20-2019 STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS Pratik Patil San Jose State University Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Other Computer Sciences Commons Recommended Citation Patil, Pratik, "STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS" (2019). Master's Projects. 692. DOI: https://doi.org/10.31979/etd.38nc-j52r https://scholarworks.sjsu.edu/etd_projects/692 This Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [email protected]. STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS A Project Report Presented to Dr. Ching seh Wu Department of Computer Science San José State University In Partial Fulfillment Of the Requirements for the Class CS 298 By Pratik Patil May 2019 © 2019 Pratik Patil ALL RIGHTS RESERVED The Designated Thesis Committee Approves the Thesis Titled STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS by Pratik Patil APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE SAN JOSÉ STATE UNIVERSITY May 2019 Dr. Ching seh Wu Department of Computer Science Dr. Katerina Potika Department of Computer Science Dr. Marjan Orang Department of Economics ACKNOWLEDGEMENT This has been one long and arduous journey, but nevertheless a worthwhile life experience because of the many great Professors at SJSU and beloved friends.
    [Show full text]
  • Jise.Org/Volume31/N1/Jisev31n1p72.Html
    Journal of Information Volume 31 Systems Issue 1 Education Winter 2020 Experiences in Using a Multiparadigm and Multiprogramming Approach to Teach an Information Systems Course on Introduction to Programming Juan Gutiérrez-Cárdenas Recommended Citation: Gutiérrez-Cárdenas, J. (2020). Experiences in Using a Multiparadigm and Multiprogramming Approach to Teach an Information Systems Course on Introduction to Programming. Journal of Information Systems Education, 31(1), 72-82. Article Link: http://jise.org/Volume31/n1/JISEv31n1p72.html Initial Submission: 23 December 2018 Accepted: 24 May 2019 Abstract Posted Online: 12 September 2019 Published: 3 March 2020 Full terms and conditions of access and use, archived papers, submission instructions, a search tool, and much more can be found on the JISE website: http://jise.org ISSN: 2574-3872 (Online) 1055-3096 (Print) Journal of Information Systems Education, Vol. 31(1) Winter 2020 Experiences in Using a Multiparadigm and Multiprogramming Approach to Teach an Information Systems Course on Introduction to Programming Juan Gutiérrez-Cárdenas Faculty of Engineering and Architecture Universidad de Lima Lima, 15023, Perú [email protected] ABSTRACT In the current literature, there is limited evidence of the effects of teaching programming languages using two different paradigms concurrently. In this paper, we present our experience in using a multiparadigm and multiprogramming approach for an Introduction to Programming course. The multiparadigm element consisted of teaching the imperative and functional paradigms, while the multiprogramming element involved the Scheme and Python programming languages. For the multiparadigm part, the lectures were oriented to compare the similarities and differences between the functional and imperative approaches. For the multiprogramming part, we chose syntactically simple software tools that have a robust set of prebuilt functions and available libraries.
    [Show full text]