An Econometric Study: The Cost of Mobile Broadband
|
|
|
- Rudolph McDaniel
- 6 years ago
- Views:
Transcription
1 An Econometric Study: The Cost of Mobile Broadband Zhiwei Peng, Yongdon Shin, Adrian Raducanu IATOM13 ENAC January 16, 2014 Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
2 Overview 1 Introduction Our Topic Data 2 Econometric analysis First Model Second Model 3 Conclusion Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
3 Our Topic Mobile Broadband Mobile broadband services can be accessed through a computer-based connection, using a USB-modem/dongle to connect to the mobile network, or through a handset-based connection. By 2016, more than 80 percent of broadband connections will be mobile.(source: Wireless Intelligence Database) Our Topic The impact of different factors on mobile broadband price. Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
4 Parameters Dependent Variable The mobile broadband price on a country level Explanatory Variables GDP per capita Competition between service providers (ISP) Level of urbanization (Urban population ratio) Education level (Years of schooling) Population Number of mobile broadband subscribers Area of each country Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
5 Data Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
6 Rules for collecting price data We choose 500MB postpaid handset-based plans (most affordable) Rules Mobile data price from the operator with largest market share in the country Prices include TAX On a monthly basis Price in original currency, then converted into USD Sources International Telecommunication Union UNESCO World Bank Wikipedia Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
7 Postpaid handset-based prices (500 MB), Series: PRICE Sample Observations 112 Mean Median Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera Probability Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
8 First Model Price i =β 0 + β 1 GDP i + β 2 ISP i + β 3 urbr i + β 4 edu i + β 5 pop i + β 6 msub i + β 7 area i i = 1, 2,..., 118 Descriptions Normalization: x new = x x min x max x min GDP: GDP per capita, ISP: service providers, Urbr: urban population ratio, edu: years of schooling, pop: population, msub: mobile broadband subscribers, area: area of each country Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
9 First Model Regression results Variable Coefficient Std. Error t-stastistics Prob. C GDP ISP URBR EDU POP MSUB AREA R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
10 Hypothesis Test F-test Null: H 0 : c(1) = c(2) =... = c(k) = 0 Prob. = < 0.05 Reject the null hypothesis t-test, null hypothesis: C(6) = 0 Test Statistic Value df Probability t-statistic > 0.05, we accept the null hypothesis Same test is done for other coefficients Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
11 Second Model Price i = β 0 + β 1 GDP i + β 2 ISP i Variable Coefficient Std. Error t-stastistics Prob. C GDP ISP R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
12 Second Model Interpretations The two probabilities are almost zero Prob(F-statistic) < 0.05 R 2 = , too small. Regression equation Price i = GDP i ISP i i = 1, 2, 3,..., 118 Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
13 Heteroscedasticity Test: White F-statistic Prob. F(5,106) Obs*R-squared Prob. Chi-Square(5) Scaled explained SS Prob. Chi-Square(5) Interpretation Prob. = > 0.05, we accept the homoscedasticity hypothesis The residuals have the same finite variance Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
14 Normality test of residuals Series: RESID Sample Observations 112 Mean -2.48e-18 Median Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera Probability Mean, E(ɛ) = 0 Prob. = 0, reject normality Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
15 Actual, Fitted, Residual Graph Residual Actual Fitted Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
16 Elasticity Price i = GDP i ISP i Estimated price elasticity of GDP per capita Price GDP = A 10 percent increase in GDP p.c. results in a 3 percent increase in price Estimated price elasticity of ISP Price ISP = A 10 percent increase in number of service providers results in a 6 percent increase in price hiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
17 Conclusion Our model GDP p.c. and ISP are good indicators of mobile broadband price Not good fitness of data, R 2 too small Only two explanatory variables Residuals are not normally distributed A lot of factors are statically insignificant Further work Mobile broadband price seems not to be influenced by the area of each country, but we believe that for developing countries, it would have significant influence Conduct econometric study inside developing countries and developed countries to make a comparison Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
18 The End Zhiwei Peng, Yongdon Shin, Adrian Raducanu (UCLA) The Cost of Mobile Broadband January 16, / 18
Lab Session 1. Introduction to Eviews
Albert-Ludwigs University Freiburg Department of Empirical Economics Time Series Analysis, Summer 2009 Dr. Sevtap Kestel To see the data of m1: 1 Lab Session 1 Introduction to Eviews We introduce the basic
INTRODUCTION TO PANEL DATA ANALYSIS
INTRODUCTION TO PANEL DATA ANALYSIS USING EVIEWS FARIDAH NAJUNA MISMAN, PhD FINANCE DEPARTMENT FACULTY OF BUSINESS & MANAGEMENT UiTM JOHOR PANEL DATA WORKSHOP-23&24 MAY 2017 1 OUTLINE 1. Introduction 2.
Model Diagnostic tests
Model Diagnostic tests 1. Multicollinearity a) Pairwise correlation test Quick/Group stats/ correlations b) VIF Step 1. Open the EViews workfile named Fish8.wk1. (FROM DATA FILES- TSIME) Step 2. Select
LOADS, CUSTOMERS AND REVENUE
EB04-06 Corrected: 0 Jan Page of LOADS, CUSTOMERS AND REVENUE 4 Toronto Hydro s total load, customer and distribution revenue forecast is summarized in Table. The revenue forecast is calculated based on
Adaptive spline autoregression threshold method in forecasting Mitsubishi car sales volume at PT Srikandi Diamond Motors
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Adaptive spline autoregression threshold method in forecasting Mitsubishi car sales volume at PT Srikandi Diamond Motors To cite
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
Data Mining. ❷Chapter 2 Basic Statistics. Asso.Prof.Dr. Xiao-dong Zhu. Business School, University of Shanghai for Science & Technology
❷Chapter 2 Basic Statistics Business School, University of Shanghai for Science & Technology 2016-2017 2nd Semester, Spring2017 Contents of chapter 1 1 recording data using computers 2 3 4 5 6 some famous
22s:152 Applied Linear Regression
22s:152 Applied Linear Regression Chapter 22: Model Selection In model selection, the idea is to find the smallest set of variables which provides an adequate description of the data. We will consider
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 55555555555555555555555555555555555555555555555555 NYYNNYNNNYNYYYYYNNYNNNNNYNYYYYYNYNNNNYNNYNNNYNNNNN 01 CAEADDBEDEDBABBBBCBDDDBAAAECEEDCDCDBACCACEECACCCEA
Source engine marketing: a preliminary empirical analysis of web search data
Source engine marketing: a preliminary empirical analysis of web search data Abstract Bruce Q. Budd Alfaisal University The purpose of this paper is to empirically investigate a website performance and
Chapter 15: Forecasting
Chapter 15: Forecasting In this chapter: 1. Forecasting chicken consumption using OLS (UE 15.1, Equation 6.8, p. 501) 2. Forecasting chicken consumption using a generalized least squares (GLS) model estimated
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
Bivariate Linear Regression James M. Murray, Ph.D. University of Wisconsin - La Crosse Updated: October 04, 2017
Bivariate Linear Regression James M. Murray, Ph.D. University of Wisconsin - La Crosse Updated: October 4, 217 PDF file location: http://www.murraylax.org/rtutorials/regression_intro.pdf HTML file location:
Stat 500 lab notes c Philip M. Dixon, Week 10: Autocorrelated errors
Week 10: Autocorrelated errors This week, I have done one possible analysis and provided lots of output for you to consider. Case study: predicting body fat Body fat is an important health measure, but
Intro to E-Views. E-views is a statistical package useful for cross sectional, time series and panel data statistical analysis.
Center for Teaching, Research & Learning Research Support Group at the CTRL Lab American University, Washington, D.C. http://www.american.edu/provost/ctrl/ 202-885-3862 Intro to E-Views E-views is a statistical
EViews 6 Tutorial. by Manfred W. Keil. to Accompany. Introduction to Econometrics. by James H. Stock and Mark W. Watson
EViews 6 Tutorial by Manfred W. Keil to Accompany Introduction to Econometrics by James H. Stock and Mark W. Watson ---------------------------------------------------------------------------------------------------------------------
Week 4: Simple Linear Regression II
Week 4: Simple Linear Regression II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Algebraic properties
What are important factors for the successful implementation of IPTV across countries?
What are important factors for the successful implementation of IPTV across countries? Jiwhan Kim*, Changi Nam*, Min Ho Ryu** 0900-1000 *KAIST School of Business and Technology Management *Hoseo University
Optimization of loading curve in tube hydroforming using the multilevel response surface. Mostafa aslani choghiorty
Optimization of loading curve in tube hydroforming using the multilevel response surface Mostafa aslani choghiorty Department of Mechanical enginnering, Najafabad Branch, Islamic Azad University, Najafabad,
22s:152 Applied Linear Regression
22s:152 Applied Linear Regression Chapter 22: Model Selection In model selection, the idea is to find the smallest set of variables which provides an adequate description of the data. We will consider
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
Here is Kellogg s custom menu for their core statistics class, which can be loaded by typing the do statement shown in the command window at the very
Here is Kellogg s custom menu for their core statistics class, which can be loaded by typing the do statement shown in the command window at the very bottom of the screen: 4 The univariate statistics command
Research Methods Workshop Introduction to EViews
Research Methods Workshop Introduction to EViews Today s Workshop Brief Introduction to EViews and Basic Operations Descriptive Statistics with EViews Group Statistics with Eviews Note that the data we
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
Econometrics I: OLS. Dean Fantazzini. Dipartimento di Economia Politica e Metodi Quantitativi. University of Pavia
Dipartimento di Economia Politica e Metodi Quantitativi University of Pavia Overview of the Lecture 1 st EViews Session I: Convergence in the Solow Model 2 Overview of the Lecture 1 st EViews Session I:
Introduction: EViews. Dr. Peerapat Wongchaiwat
Introduction: EViews Dr. Peerapat Wongchaiwat wongchaiwat@hotmail.com Today s Workshop Basic grasp of how EViews manages data Creating Workfiles Importing data Running regressions Performing basic tests
Serial Correlation and Heteroscedasticity in Time series Regressions. Econometric (EC3090) - Week 11 Agustín Bénétrix
Serial Correlation and Heteroscedasticity in Time series Regressions Econometric (EC3090) - Week 11 Agustín Bénétrix 1 Properties of OLS with serially correlated errors OLS still unbiased and consistent
Product Catalog. AcaStat. Software
Product Catalog AcaStat Software AcaStat AcaStat is an inexpensive and easy-to-use data analysis tool. Easily create data files or import data from spreadsheets or delimited text files. Run crosstabulations,
. predict mod1. graph mod1 ed, connect(l) xlabel ylabel l1(model1 predicted income) b1(years of education)
DUMMY VARIABLES AND INTERACTIONS Let's start with an example in which we are interested in discrimination in income. We have a dataset that includes information for about 16 people on their income, their
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA
050 0 N 03 BECABCDDDBDBCDBDBCDADDBACACBCCBAACEDEDBACBECCDDCEA 55555555555555555555555555555555555555555555555555 YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYY 01 CAEADDBEDEDBABBBBCBDDDBAAAECEEDCDCDBACCACEECACCCEA
range: [1,20] units: 1 unique values: 20 missing.: 0/20 percentiles: 10% 25% 50% 75% 90%
![range: [1,20] units: 1 unique values: 20 missing.: 0/20 percentiles: 10% 25% 50% 75% 90%](/thumbs/95/123687023.jpg "range: [1,20] units: 1 unique values: 20 missing.: 0/20 percentiles: 10% 25% 50% 75% 90%")
------------------ log: \Term 2\Lecture_2s\regression1a.log log type: text opened on: 22 Feb 2008, 03:29:09. cmdlog using " \Term 2\Lecture_2s\regression1a.do" (cmdlog \Term 2\Lecture_2s\regression1a.do
Heteroscedasticity-Consistent Standard Error Estimates for the Linear Regression Model: SPSS and SAS Implementation. Andrew F.
Heteroscedasticity-Consistent Standard Error Estimates for the Linear Regression Model: SPSS and SAS Implementation Andrew F. Hayes 1 The Ohio State University Columbus, Ohio hayes.338@osu.edu Draft: January
LIST OF TABLES. Page Title No.
LIST OF TABLES Table 1.1 Growth in Total Subscriber Base of Telecom Industry 5 1.2 Growth in Tele-density 11 2.1 Top 10 Countries with the number of Mobile Phone Subscribers 2011 23 2.2 World Wide Market
Time-Varying Volatility and ARCH Models
Time-Varying Volatility and ARCH Models ARCH MODEL AND TIME-VARYING VOLATILITY In this lesson we'll use Gretl to estimate several models in which the variance of the dependent variable changes over time.
EViews 3.1 Student Version
EViews 3.1 Student Version Copyright 1994 1999 Quantitative Micro Software, LLC All Rights Reserved Printed in the United States of America This software product, including program code and manual, is
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
CHAPTER 3 AN OVERVIEW OF DESIGN OF EXPERIMENTS AND RESPONSE SURFACE METHODOLOGY
23 CHAPTER 3 AN OVERVIEW OF DESIGN OF EXPERIMENTS AND RESPONSE SURFACE METHODOLOGY 3.1 DESIGN OF EXPERIMENTS Design of experiments is a systematic approach for investigation of a system or process. A series
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...
Week 5: Multiple Linear Regression II
Week 5: Multiple Linear Regression II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Adjusted R
Repeated Measures Part 4: Blood Flow data
Repeated Measures Part 4: Blood Flow data /* bloodflow.sas */ options linesize=79 pagesize=100 noovp formdlim='_'; title 'Two within-subjecs factors: Blood flow data (NWK p. 1181)'; proc format; value
Two-Stage Least Squares
Chapter 316 Two-Stage Least Squares Introduction This procedure calculates the two-stage least squares (2SLS) estimate. This method is used fit models that include instrumental variables. 2SLS includes
Data Analysis and Hypothesis Testing Using the Python ecosystem
ARISTOTLE UNIVERSITY OF THESSALONIKI Data Analysis and Hypothesis Testing Using the Python ecosystem t-test & ANOVAs Stavros Demetriadis Assc. Prof., School of Informatics, Aristotle University of Thessaloniki
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
A presentation from the Telemetrics Lab
A presentation from the Telemetrics Lab Telemetrics lab Department of Psychology Northwestern University Evanston, Illinois USA November, 2010 Outline 1 Data from a Correlation Matrix Simulated data Real
Variable selection is intended to select the best subset of predictors. But why bother?
Chapter 10 Variable Selection Variable selection is intended to select the best subset of predictors. But why bother? 1. We want to explain the data in the simplest way redundant predictors should be removed.
A Statistical Analysis of UK Financial Networks
A Statistical Analysis of UK Financial Networks J. Chu & S. Nadarajah First version: 31 December 2016 Research Report No. 9, 2016, Probability and Statistics Group School of Mathematics, The University
ITU Telecommunication/ICT Indicators Tariffs and Traffic
ITU Telecommunication/ICT Indicators Tariffs and Traffic 30 October 2007 Monika Muylkens Market Information and Statistics (STAT)/Policy and Strategies (POL) Bureau for Telecommunication
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
Regression Analysis and Linear Regression Models
Regression Analysis and Linear Regression Models University of Trento - FBK 2 March, 2015 (UNITN-FBK) Regression Analysis and Linear Regression Models 2 March, 2015 1 / 33 Relationship between numerical
STAT 2607 REVIEW PROBLEMS Word problems must be answered in words of the problem.
STAT 2607 REVIEW PROBLEMS 1 REMINDER: On the final exam 1. Word problems must be answered in words of the problem. 2. "Test" means that you must carry out a formal hypothesis testing procedure with H0,
Applied Statistics and Econometrics Lecture 6
Applied Statistics and Econometrics Lecture 6 Giuseppe Ragusa Luiss University gragusa@luiss.it http://gragusa.org/ March 6, 2017 Luiss University Empirical application. Data Italian Labour Force Survey,
Introduction to Hierarchical Linear Model. Hsueh-Sheng Wu CFDR Workshop Series January 30, 2017
Introduction to Hierarchical Linear Model Hsueh-Sheng Wu CFDR Workshop Series January 30, 2017 1 Outline What is Hierarchical Linear Model? Why do nested data create analytic problems? Graphic presentation
PubHlth 640 Intermediate Biostatistics Unit 2 - Regression and Correlation. Simple Linear Regression Software: Stata v 10.1
PubHlth 640 Intermediate Biostatistics Unit 2 - Regression and Correlation Simple Linear Regression Software: Stata v 10.1 Emergency Calls to the New York Auto Club Source: Chatterjee, S; Handcock MS and
Vanuatu Regulatory Statistical Framework An Overview ITU Asia-Pacific ICT Indicator Workshop, Manilla Philippine 2018
Vanuatu Regulatory Statistical Framework An Overview ITU Asia-Pacific ICT Indicator Workshop, Manilla Philippine 2018 The Government of The Republic of Vanuatu Overview of Presentation Vanuatu a geopolitical
EViews 4.1 Tutorial 1. EVIEWS: INTRODUCTION
EViews 4.1 Tutorial 1. EVIEWS: INTRODUCTION This tutorial will introduce you to a statistical and econometric software package called EViews, or Econometric Views. EViews runs on both the Windows (9x,
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
Section 3.2: Multiple Linear Regression II. Jared S. Murray The University of Texas at Austin McCombs School of Business
Section 3.2: Multiple Linear Regression II Jared S. Murray The University of Texas at Austin McCombs School of Business 1 Multiple Linear Regression: Inference and Understanding We can answer new questions
Factorial ANOVA. Skipping... Page 1 of 18
Factorial ANOVA The potato data: Batches of potatoes randomly assigned to to be stored at either cool or warm temperature, infected with one of three bacterial types. Then wait a set period. The dependent
Week 10: Heteroskedasticity II
Week 10: Heteroskedasticity II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Dealing with heteroskedasticy
List of indicators included in the World Telecommunication/ICT Indicators database, December 2017
List of indicators included in the World Telecommunication/ICT Indicators database, December 2017 Economy, Demography Average annual exchange rate per USD Households Percentage of the population in urban
Practical 4: Mixed effect models
Practical 4: Mixed effect models This practical is about how to fit (generalised) linear mixed effects models using the lme4 package. You may need to install it first (using either the install.packages
Standard Errors in OLS Luke Sonnet
Standard Errors in OLS Luke Sonnet Contents Variance-Covariance of ˆβ 1 Standard Estimation (Spherical Errors) 2 Robust Estimation (Heteroskedasticity Constistent Errors) 4 Cluster Robust Estimation 7
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
GRETL FOR TODDLERS!! CONTENTS. 1. Access to the econometric software A new data set: An existent data set: 3
GRETL FOR TODDLERS!! JAVIER FERNÁNDEZ-MACHO CONTENTS 1. Access to the econometric software 3 2. Loading and saving data: the File menu 3 2.1. A new data set: 3 2.2. An existent data set: 3 2.3. Importing
Baruch College STA Senem Acet Coskun
Baruch College STA 9750 BOOK BUY A Predictive Mode Senem Acet Coskun Table of Contents Summary 3 Why this topic? 4 Data Sources 6 Variable Definitions 7 Descriptive Statistics 8 Univariate Analysis 9 Two-Sample
Section 2.3: Simple Linear Regression: Predictions and Inference
Section 2.3: Simple Linear Regression: Predictions and Inference Jared S. Murray The University of Texas at Austin McCombs School of Business Suggested reading: OpenIntro Statistics, Chapter 7.4 1 Simple
How fixed mobile usage interact? Does FTTH deployment. influence the interaction?
How fixed mobile usage interact? Does FTTH deployment influence the interaction? Julienne Liang June 28th 2015 Abstract We estimate the interaction between fixed and mobile usage both for voice and data
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
Panel Data 4: Fixed Effects vs Random Effects Models
Panel Data 4: Fixed Effects vs Random Effects Models Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised April 4, 2017 These notes borrow very heavily, sometimes verbatim,
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)
Robust Linear Regression (Passing- Bablok Median-Slope)
Chapter 314 Robust Linear Regression (Passing- Bablok Median-Slope) Introduction This procedure performs robust linear regression estimation using the Passing-Bablok (1988) median-slope algorithm. Their
PANEL DATA REGRESSION MODELS IN EVIEWS: Pooled OLS, Fixed or Random effect model?
PANEL DATA REGRESSION MODELS IN EVIEWS: Pooled OLS, Fixed or Random effect model? ADESETE, Ahmed Adefemi 12/6/2017 2 PANEL DATA REGRESSION MODELS IN EVIEWS: Pooled OLS, Fixed or Random effect model? Panel
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,
Telecom Sector Outlook and Spectrum Trading: the Case of Pakistan
Telecom Sector Outlook and Spectrum Trading: the Case of Pakistan Shahbaz Nasir Pakistan Telecommunication Authority ITU Workshop on Economic Aspects of Spectrum Management 21 st 23 rd November 2016 Tehran
NAG Library Routine Document G13DPF.1
NAG Library Routine Document Note: before using this routine, please read the Users Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent
THIS IS NOT REPRESNTATIVE OF CURRENT CLASS MATERIAL. STOR 455 Midterm 1 September 28, 2010
THIS IS NOT REPRESNTATIVE OF CURRENT CLASS MATERIAL STOR 455 Midterm September 8, INSTRUCTIONS: BOTH THE EXAM AND THE BUBBLE SHEET WILL BE COLLECTED. YOU MUST PRINT YOUR NAME AND SIGN THE HONOR PLEDGE
Stat 5303 (Oehlert): Response Surfaces 1
Stat 5303 (Oehlert): Response Surfaces 1 > data
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
Review of JDemetra+ revisions plug-in
Review of JDemetra+ revisions plug-in Jennifer Davies and Duncan Elliott, Office for National Statistics March 28, 2018 1 Introduction This provides a review of the JDemetra+revisions plug-in written by
Introduction to STATA 6.0 ECONOMICS 626
Introduction to STATA 6.0 ECONOMICS 626 Bill Evans Fall 2001 This handout gives a very brief introduction to STATA 6.0 on the Economics Department Network. In a few short years, STATA has become one of
Land Cover Stratified Accuracy Assessment For Digital Elevation Model derived from Airborne LIDAR Dade County, Florida
Land Cover Stratified Accuracy Assessment For Digital Elevation Model derived from Airborne LIDAR Dade County, Florida FINAL REPORT Submitted October 2004 Prepared by: Daniel Gann Geographic Information
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"
Conditional and Unconditional Regression with No Measurement Error
Conditional and with No Measurement Error /* reg2ways.sas */ %include 'readsenic.sas'; title2 ''; proc reg; title3 'Conditional Regression'; model infrisk = stay census; proc calis cov; /* Analyze the
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
STAT - Edit Scroll up the appropriate list to highlight the list name at the very top Press CLEAR, followed by the down arrow or ENTER
Entering/Editing Data Use arrows to scroll to the appropriate list and position Enter or edit data, pressing ENTER after each (including the last) Deleting Data (One Value at a Time) Use arrows to scroll
COUNTRY PROFILE. Italy
COUNTRY PROFILE Italy Statistical tables Factor I: Economic Performance WORLD COMPETITIVENESS RANKING 2018 All data are available from the World Competitiveness Online. Visit our eshop 1 COMPETITIVENESS
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.
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
Week 9: Modeling II. Marcelo Coca Perraillon. Health Services Research Methods I HSMP University of Colorado Anschutz Medical Campus
Week 9: Modeling II Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ARR 1 Outline Taking the log Retransformation
Introduction to EViews. Manuel Leonard F. Albis UP School of Statistics
Introduction to EViews Manuel Leonard F. Albis UP School of Statistics EViews EViews provides sophisticated data analysis, regression, and forecasting tools on Windows-based computers. Areas where EViews
Analysis of variance - ANOVA
Analysis of variance - ANOVA Based on a book by Julian J. Faraway University of Iceland (UI) Estimation 1 / 50 Anova In ANOVAs all predictors are categorical/qualitative. The original thinking was to try
- 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
SHIFTING MOBILE DATA PLANS EXTRACT FROM THE ERICSSON MOBILITY REPORT
SHIFTING MOBILE DATA PLANS EXTRACT FROM THE ERICSSON MOBILITY REPORT NOVEMBER 217 Shifting mobile data plans The most popular mobile broadband data plans are those with limits ranging from 2 to 5 GigaBytes
Selected Introductory Statistical and Data Manipulation Procedures. Gordon & Johnson 2002 Minitab version 13.
Minitab@Oneonta.Manual: Selected Introductory Statistical and Data Manipulation Procedures Gordon & Johnson 2002 Minitab version 13.0 Minitab@Oneonta.Manual: Selected Introductory Statistical and Data
Source:
Time Series Source: http://www.princeton.edu/~otorres/stata/ Time series data is data collected over time for a single or a group of variables. Date variable For this kind of data the first thing to do
Using Machine Learning to Optimize Storage Systems
Using Machine Learning to Optimize Storage Systems Dr. Kiran Gunnam 1 Outline 1. Overview 2. Building Flash Models using Logistic Regression. 3. Storage Object classification 4. Storage Allocation recommendation
Learner Expectations UNIT 1: GRAPICAL AND NUMERIC REPRESENTATIONS OF DATA. Sept. Fathom Lab: Distributions and Best Methods of Display
CURRICULUM MAP TEMPLATE Priority Standards = Approximately 70% Supporting Standards = Approximately 20% Additional Standards = Approximately 10% HONORS PROBABILITY AND STATISTICS Essential Questions &
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
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
Linear Model Selection and Regularization. especially usefull in high dimensions p>>100.
Linear Model Selection and Regularization especially usefull in high dimensions p>>100. 1 Why Linear Model Regularization? Linear models are simple, BUT consider p>>n, we have more features than data records