This document provides a summary of key concepts from Chapter 3 of a statistics textbook, including:
- How to calculate measures of central tendency like the mean, median, mode, and weighted mean
- The characteristics and properties of each measure
- How the positions of the mean, median and mode relate to the shape of the distribution
- How to calculate the mean, median and mode for grouped data
- What the geometric mean represents and how it is calculated
001 Lesson 1 Statistical Techniques for Business & EconomicsNing Ding
This document provides an overview of key concepts in statistics that will be covered in chapters 1 and 2 of an introductory statistics course. Chapter 1 defines key terms like population, sample, descriptive statistics, inferential statistics, qualitative and quantitative variables, and the different levels of measurement. Chapter 2 describes how to organize and present qualitative and quantitative data using tools like frequency tables, bar charts, histograms, and frequency distributions.
This document contains a PowerPoint presentation on inductive statistics covering topics like probability distributions, sampling distributions, estimation, hypothesis testing for means and proportions, and two-sample hypothesis tests. It provides an overview of the chapters that will be covered, examples of hypothesis tests for means and proportions when the population standard deviation is known and unknown, and examples of independent and dependent two-sample hypothesis tests for differences in means and proportions with both large and small sample sizes. Step-by-step explanations are given for conducting hypothesis tests.
This document provides a summary of key concepts from Chapter 3 of a statistics textbook, including:
- How to calculate measures of central tendency like the mean, median, mode, and weighted mean
- The characteristics and properties of each measure
- How the positions of the mean, median and mode relate to the shape of the distribution
- How to calculate the mean, median and mode for grouped data
- What the geometric mean represents and how it is calculated
001 Lesson 1 Statistical Techniques for Business & EconomicsNing Ding
This document provides an overview of key concepts in statistics that will be covered in chapters 1 and 2 of an introductory statistics course. Chapter 1 defines key terms like population, sample, descriptive statistics, inferential statistics, qualitative and quantitative variables, and the different levels of measurement. Chapter 2 describes how to organize and present qualitative and quantitative data using tools like frequency tables, bar charts, histograms, and frequency distributions.
This document contains a PowerPoint presentation on inductive statistics covering topics like probability distributions, sampling distributions, estimation, hypothesis testing for means and proportions, and two-sample hypothesis tests. It provides an overview of the chapters that will be covered, examples of hypothesis tests for means and proportions when the population standard deviation is known and unknown, and examples of independent and dependent two-sample hypothesis tests for differences in means and proportions with both large and small sample sizes. Step-by-step explanations are given for conducting hypothesis tests.
The document outlines chapters from a statistics textbook, covering topics such as describing and exploring data through frequency tables, histograms, measures of central tendency, dispersion, correlation, and time series analysis. It also discusses deseasonalizing time series data to study trends and uses an example of predicting quarterly sales figures after removing seasonal fluctuations. The later chapters focus on time series forecasting through techniques like determining a seasonal index and forming a least squares regression line to predict future values.
The document discusses introductory statistics concepts including descriptive statistics, inferential statistics, types of variables, levels of measurement, frequency tables, histograms, and other methods for organizing and presenting data. Chapter 1 covers what statistics is, types of statistics, variables, and levels of measurement. Chapter 2 discusses describing data through frequency tables, bar charts, histograms, and other graphs. The learning goals are to understand descriptive vs inferential statistics and distinguish between different statistical concepts.
This document discusses the history and stratification of higher education, comparing new elite universities to mega universities. It outlines key landmarks in higher education history including the first university in 1088, the Humboldt university model of 1810 establishing research universities, the 1944 GI Bill supporting mass higher education, the first mega-style university in 1973, and more recent developments. It then stratifies higher education into research universities, training universities, mega universities, and new elite universities. The main body compares new elite and mega universities on factors like status, function, disciplines, degrees offered, student size and selection, profit status, costs, pedagogies, and graduate labor markets. It poses a question about differences in their faculty staff to conclude.
The document provides an overview of topics to be covered in Chapter 16 on time series and forecasting, including using trend equations to forecast future periods and develop seasonally adjusted forecasts, determining and interpreting seasonal indexes, and deseasonalizing data using a seasonal index. It also includes examples of calculating seasonal indices and adjusting sales data to remove seasonal variation. The document is a lecture outline and review for a class on international business taught by Dr. Ning Ding at Hanze University of Applied Sciences Groningen.
Lawrence erlbaum2004anintroductiontocriticaldiscourseanalysisineducationthuyussh
This document appears to be the preface or introduction to a book on critical discourse analysis in education. It provides background on how the book came to be, including discussions among the contributors on key issues and questions regarding critical discourse analysis and its application to education. The preface outlines two main directions for critical discourse analysis in education discussed by the contributors: 1) Developing an empirical basis for understanding the relationship between language form and function in conducting analysis, and 2) Developing a theory of learning in relation to critical discourse studies. It notes that the chapters aim to ground critical discourse analysis in educational research by focusing on linguistic structure and learning. The preface also gives an overview of how the book is organized to help teach key concepts
This document discusses common method variance (CMV), also known as common method bias, which occurs when using self-report questionnaires where the same respondent provides data for both the predictor and criterion variables. CMV can inflate or deflate relationships between variables and is attributed to the measurement method rather than the constructs being measured. Four remedies are provided to address CMV: 1) using other data sources, 2) varying question order and scales, 3) more complex data models, and 4) statistical methods like Harman's single factor test and adding a common latent factor or marker variable to models.
WHO SHOULD ATTEND?
University / College lecturers, Ph.D., Research scholars, Post-Doctoral fellows, Post-Graduate students and individuals having interest in SEM.
This document discusses regression models, path models, and the output from AMOS software when conducting structural equation modeling (SEM). Regression models only include observed variables and assume independents are measured without error. Path models allow independents to be both causes and effects, and allow for error terms on endogenous variables. The AMOS output provides standardized and unstandardized regression weights, significance tests, and fit indexes to evaluate how well the specified model fits the sample data.
This document provides an overview of entering and defining variables in SPSS. It discusses opening SPSS, what variables are, defining variables by entering their names and labels, and entering data. It also covers saving data files, importing data from Excel, and handling missing data. Various examples are provided to illustrate defining categorical, ordinal, and continuous variables as well as entering different data types including questionnaires, experiments, and longitudinal studies.
Sebuah pengantar singkat namun komprehensif mengenai Structural Equation Modeling
For detailed training and consultation
contact me at bodhiyawijaya@gmail.com
or
Linkedin: Bodhiya Wijaya Mulya
Structural equation modeling (SEM) is a statistical technique used to establish relationships between variables that can simultaneously test measurement and structural relationships. SEM combines factor analysis and multiple regression to test if a conceptual model fits the data. It is defined by terms like path analysis, path modeling, and causal modeling. At the heart of SEM is covariance, which measures how variables change together, and SEM aims to explain as much variance in a set of variables as possible with a specified model by reproducing the actual covariance matrix. SEM has advantages over regression like allowing for multiple dependent variables, accounting for correlations between variables, and accounting for measurement error. Key uses of SEM include theory testing, mediation analysis, group comparisons, longitudinal modeling, and multilevel
This document provides an overview of structural equation modeling (SEM) using AMOS. It defines key SEM concepts like latent variables, observed variables, path analysis, and model identification. It also explains how to specify and estimate a SEM model in AMOS, including how to draw path diagrams, name variables, set regression weights, and view output. Model fit is discussed along with potential issues like sample size. Confirmatory factor analysis and other SEM models like path analysis and latent growth models are also introduced.
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
The document outlines chapters from a statistics textbook, covering topics such as describing and exploring data through frequency tables, histograms, measures of central tendency, dispersion, correlation, and time series analysis. It also discusses deseasonalizing time series data to study trends and uses an example of predicting quarterly sales figures after removing seasonal fluctuations. The later chapters focus on time series forecasting through techniques like determining a seasonal index and forming a least squares regression line to predict future values.
The document discusses introductory statistics concepts including descriptive statistics, inferential statistics, types of variables, levels of measurement, frequency tables, histograms, and other methods for organizing and presenting data. Chapter 1 covers what statistics is, types of statistics, variables, and levels of measurement. Chapter 2 discusses describing data through frequency tables, bar charts, histograms, and other graphs. The learning goals are to understand descriptive vs inferential statistics and distinguish between different statistical concepts.
This document discusses the history and stratification of higher education, comparing new elite universities to mega universities. It outlines key landmarks in higher education history including the first university in 1088, the Humboldt university model of 1810 establishing research universities, the 1944 GI Bill supporting mass higher education, the first mega-style university in 1973, and more recent developments. It then stratifies higher education into research universities, training universities, mega universities, and new elite universities. The main body compares new elite and mega universities on factors like status, function, disciplines, degrees offered, student size and selection, profit status, costs, pedagogies, and graduate labor markets. It poses a question about differences in their faculty staff to conclude.
The document provides an overview of topics to be covered in Chapter 16 on time series and forecasting, including using trend equations to forecast future periods and develop seasonally adjusted forecasts, determining and interpreting seasonal indexes, and deseasonalizing data using a seasonal index. It also includes examples of calculating seasonal indices and adjusting sales data to remove seasonal variation. The document is a lecture outline and review for a class on international business taught by Dr. Ning Ding at Hanze University of Applied Sciences Groningen.
Lawrence erlbaum2004anintroductiontocriticaldiscourseanalysisineducationthuyussh
This document appears to be the preface or introduction to a book on critical discourse analysis in education. It provides background on how the book came to be, including discussions among the contributors on key issues and questions regarding critical discourse analysis and its application to education. The preface outlines two main directions for critical discourse analysis in education discussed by the contributors: 1) Developing an empirical basis for understanding the relationship between language form and function in conducting analysis, and 2) Developing a theory of learning in relation to critical discourse studies. It notes that the chapters aim to ground critical discourse analysis in educational research by focusing on linguistic structure and learning. The preface also gives an overview of how the book is organized to help teach key concepts
This document discusses common method variance (CMV), also known as common method bias, which occurs when using self-report questionnaires where the same respondent provides data for both the predictor and criterion variables. CMV can inflate or deflate relationships between variables and is attributed to the measurement method rather than the constructs being measured. Four remedies are provided to address CMV: 1) using other data sources, 2) varying question order and scales, 3) more complex data models, and 4) statistical methods like Harman's single factor test and adding a common latent factor or marker variable to models.
WHO SHOULD ATTEND?
University / College lecturers, Ph.D., Research scholars, Post-Doctoral fellows, Post-Graduate students and individuals having interest in SEM.
This document discusses regression models, path models, and the output from AMOS software when conducting structural equation modeling (SEM). Regression models only include observed variables and assume independents are measured without error. Path models allow independents to be both causes and effects, and allow for error terms on endogenous variables. The AMOS output provides standardized and unstandardized regression weights, significance tests, and fit indexes to evaluate how well the specified model fits the sample data.
This document provides an overview of entering and defining variables in SPSS. It discusses opening SPSS, what variables are, defining variables by entering their names and labels, and entering data. It also covers saving data files, importing data from Excel, and handling missing data. Various examples are provided to illustrate defining categorical, ordinal, and continuous variables as well as entering different data types including questionnaires, experiments, and longitudinal studies.
Sebuah pengantar singkat namun komprehensif mengenai Structural Equation Modeling
For detailed training and consultation
contact me at bodhiyawijaya@gmail.com
or
Linkedin: Bodhiya Wijaya Mulya
Structural equation modeling (SEM) is a statistical technique used to establish relationships between variables that can simultaneously test measurement and structural relationships. SEM combines factor analysis and multiple regression to test if a conceptual model fits the data. It is defined by terms like path analysis, path modeling, and causal modeling. At the heart of SEM is covariance, which measures how variables change together, and SEM aims to explain as much variance in a set of variables as possible with a specified model by reproducing the actual covariance matrix. SEM has advantages over regression like allowing for multiple dependent variables, accounting for correlations between variables, and accounting for measurement error. Key uses of SEM include theory testing, mediation analysis, group comparisons, longitudinal modeling, and multilevel
This document provides an overview of structural equation modeling (SEM) using AMOS. It defines key SEM concepts like latent variables, observed variables, path analysis, and model identification. It also explains how to specify and estimate a SEM model in AMOS, including how to draw path diagrams, name variables, set regression weights, and view output. Model fit is discussed along with potential issues like sample size. Confirmatory factor analysis and other SEM models like path analysis and latent growth models are also introduced.
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
Reference: China update on the market for smartphones and mobile security Q4 ...C. Keiko Funahashi
Referenced in presentation, "The Seven Wonders of China's Mobile World"
http://www.slideshare.net/ckeikofunahashi/m-learncon-session-907-ckeikofunahashi
Here are the steps to solve this problem:
1) Code the year as t = 1 for 1999, t = 2 for 2000, etc.
2) Calculate the sums: Σt = 15, ΣY = 211.9, Σt2 = 30, ΣtY = 332.5
3) b = (ΣtY - ΣtΣY/n) / (Σt2 - Σt2/n) = 6.55
4) a = Y - bX = 29.4 - 6.55(1) = 22.85
5) Ŷ = 22.85 + 6.55t
To estimate vending sales
This document provides an overview of simple linear regression and correlation. It discusses key concepts such as dependent and independent variables, scatter diagrams, regression analysis, the least-squares estimating equation, and the coefficients of determination and correlation. Scatter diagrams are used to determine the nature and strength of relationships between variables. Regression analysis finds relationships of association but not necessarily of cause and effect. The least-squares estimating equation models the dependent variable as a function of the independent variable.
This document provides an overview of central tendency measures that will be covered in Chapter 3-A, including the mean, mode, and median for both ungrouped and grouped data. It also includes examples of calculating the mean, weighted mean, and mode. The document reviews key concepts such as the difference between parameters and statistics. Overall, the document previews and reviews important concepts related to measures of central tendency that will be covered in the upcoming chapter.
Lesson 06 chapter 9 two samples test and Chapter 11 chi square testNing Ding
This document is a PowerPoint presentation about hypothesis testing for two samples and chi-square tests. It covers topics like independent and dependent sample tests, testing differences between proportions, one-tailed and two-tailed tests. Examples are provided to demonstrate how to perform two-sample t-tests, tests of proportions, and chi-square tests using contingency tables with 2 rows and 3 rows. Step-by-step instructions and formulas are given. Key chapters from the textbook are reviewed.
This document provides an outline and overview of topics covered in a course on inductive statistics, including probability distributions, sampling distributions, estimation, and hypothesis testing. Key topics discussed include interval estimation for means and proportions, using t-distributions when sample sizes are small and variances are unknown, and the basics of hypothesis testing such as null and alternative hypotheses. Examples are provided to illustrate concepts like confidence intervals for means, proportions, and hypothesis testing.
The document summarizes key concepts from chapters 6 and 7 of a statistics textbook. Chapter 6 discusses sampling and calculating standard error for infinite and finite populations. Chapter 7 introduces estimation, including interval estimates and point estimates. It provides examples of calculating standard error and confidence intervals. The document also lists SPSS tips for t-tests.
This document provides an overview and summary of topics covered in a research methods course. It discusses reviewing concepts from prior lectures, including different types of research and variables. Today's lecture will cover instrumentation, validity and reliability, and threats to internal validity. Instrumentation discusses how to collect and measure data. Validity and reliability refer to the accuracy and consistency of measurements. Threats to internal validity could interfere with determining the true effect of independent variables on dependent variables.
This document provides an overview of content covered in Statistics 2, including a review of chapter 5 on sampling distributions. It includes examples of questions from quizzes on topics like the normal distribution and binomial approximation. The document also provides tips on using SPSS for descriptive statistics, such as inputting and defining variable data, and analyzing frequencies.
This document summarizes a course on research methods and techniques. It outlines the structure and requirements of the course, including reading a textbook and attending lectures. It discusses different types of research and variables. The document covers defining research problems, formulating hypotheses, research ethics, and instrumentation. Self-check exercises are provided to help students understand key concepts.
The document provides an overview of key concepts in probability and statistics, including:
- Definitions of probability distributions, random variables, and expected value
- Explanations and examples of the binomial, Poisson, and normal distributions
- How to calculate probabilities and combine them with monetary values for decision making
The document discusses techniques for analyzing time series data and seasonal trends, including calculating moving averages, determining linear and nonlinear trends, seasonal indexes, and deseasonalizing data. It provides examples of computing seasonal indexes using quarterly sales data and removing seasonal variation to study underlying trends. Key steps include organizing the data, taking moving averages, calculating specific seasonal indexes, and adjusting values using seasonal factors.
This document provides a summary of key concepts from chapters on simple regression and correlation analysis. It defines regression analysis as determining the nature and strength of relationships between variables. Scatter plots are used to visualize these relationships. The regression line estimates the relationship between an independent and dependent variable. Correlation analysis describes the degree of linear relationship between variables using the coefficient of determination and coefficient of correlation. Examples are provided to demonstrate calculating the regression equation and correlation coefficient.
This document contains summaries and examples of key concepts in regression analysis and correlation from Chapter 12, including:
- Regression analysis is used to estimate relationships between variables and predict future values of dependent variables based on independent variables.
- Correlation analysis describes the strength of the linear relationship between two variables from 0 to 1.
- The least squares method is used to fit a regression line that minimizes the squared errors between observed and predicted values.
This document provides instructions for using Excel to conduct descriptive statistical analyses on earnings data. It includes tips for using Excel functions like automatic numbering, summing values, and ranking data. Students are asked to calculate the average earnings for different quarters and years for an individual named M.T. Based on the results, the fourth quarter had the widest range of earnings between quarters, and the third year had the widest range of earnings between years. This suggests M.T.'s earnings varied the most during the fourth quarter and third year.
The document discusses developing a research topic and proposal. It covers generating ideas, refining a topic, writing research questions and objectives, reviewing literature, and drafting a proposal. Key points include choosing a feasible and significant topic, focusing the study with clear and logical objectives, using theory to guide the research, and convincing the audience and organizing ideas in the proposal. The document provides examples and exercises to help develop a strong research topic and questions.
3. GDP
568845
GDP (Million Yuan)
600000
Growth rate(%)
16
14
12
400000
10
8
7.7
6
200000
4
2
Source: National Bureau of Statistics Source: National Bureau of Statistics
资料来源:国家统计局
2012
2013
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
0
1978
0
4. Total Sales of Social Retail Goods
社会消费品零售总额
($ 3.78 trillion, 13.1% growth ) 3.78万亿美元,比上年增长
13.1%
Foreign Exchange Reserves
外汇储备
($ 3.82 trillion, 15.4% growth ) 3.82万亿美元,比上年增长15.4%
Yearly Total of Import & Export
全年进出口总额
($ 4.16 trillion, 7.6% growth, Surplus of $ 0.26 trillion )
4.16万亿美元,比上年增长7.6%,顺差0.26亿美元。
Source: National Bureau of Statistics Source: National Bureau of Statistics
资料来源:国家统计局
6. Car Consumption
汽车消费
Sales has been ranking No. 1 in the world for five consecutive years
销量连续5年居世界第一
China car sales 2012-2013 (units: 10000)
2200
1900
2012
Source: China Association of Automobile Manufacturers
资料来源:中国汽车工业协会
2013
7. Real Estate 房地产
2013年,70个大中城市房价连续18个月呈上涨趋势。
Upward trend for 70 large and medium cities for 18
consecutive months in 2013
北京、上海、广州等一线城市,房价涨幅高达20%。
20% rose on house prices in Beijing, Shanghai and
Guangzhou.
Source: National Bureau of Statistics
资料来源:国家统计局
9. Luxury Consumption 奢侈品消费
2013年,中国贡献了全球奢侈品总支出的29%。
China contributed 29 % of total global luxury spending in 2013
中国人购买奢侈品的年增长率
Annual growth rate on luxury spending
30%
7%
2%
2011
Data sources: Bain & Company Data
来源:贝恩公司
2012
2013
10. Online Shopping 网络购物
2013年中国网络购物交易规模(人民币/元)
China’s online shopping transaction in 2013(¥)
2 Trillion
2万亿
42%
On the Single Day (Nov.11)
Trading Volume = 80 billion
单天交易额800亿
Data sources: China Electronic Commerce Research Center
资料来源:中国电子商务研究中心
11. Mobile Internet 移动互联网
0.5 Billion,19 %
移动网民
智能手机
Mobile users
100 Billion,81.2%
市场规模
Total transaction
Data sources: Ereli Advisory
资料来源:艾瑞咨询
0.58 Billion,60%
Smartphone
9.2 Billion,371%
移动游戏市场
Mobile game
1.2 Trillion,707%
移动支付交易
Mobile Payment
12. Express Delivery 快递
9.19 Billion Units, 61.6% 144.17 Billion RMB,36.3%
业务量
业务收入
Number of Deals
Revenue
2013
Data sources: Chinese State Post Bureau
资料来源:中国国家邮政局
17. Shopping online 网购
77.8%
有
的消费者明确表示有网购经历;其中,淘宝网(67.6%)和京
东商城(35.9%)荣列消费者最信赖购物网站前两位。
77.8% of consumers have confirmed their experiences of online
shopping;
Among them, Taobao (67.6%) and Jingdong mall (35.9%) are the
most trusted consumer shopping websites.
Data sources: Horizon Research Consultancy Group :network shopping satisfaction survey in 2012 .
资料来源:零点研究咨询集团2012年网络购物满意度主题调查。
18. Travel 旅游
2013年国庆期间,有
21.8%受访者有外出旅游计划
13.3%的人选择出国旅游。
其中,
During the Chinese National Day (Oct 1st) in 2013, 21.8% of
respondents have travel plans.
Among them, 13.3% of people choose to travel abroad.
Data sources: Horizon Research Consultancy Group: national tourism consumption survey in 2013.
资料来源:零点研究咨询集团2013年国庆旅游消费调查。
19. Luxury 奢侈品
70后群体,除了房、车之外他们拥有的最昂贵个人物品是
Jewelry(33.0%)& Watch (24.4%)。
Jewelry (33.0%) and watch (24.4%) are the most expensive personal
items of the after 70’s groups besides house and car.
Expenditure on luxuarious watches by salary groups
50.0
不同收入受访者拥有的最昂贵物品是腕表的比例(除车、房)(%)
39.3
40.0
35.5
32.1
30.0
24.9
26.7
20.0
9.7
10.0
0.0
≤4000
4001-10000
≥10001
Jewelry珠宝
Data sources: Horizon Research Consultancy Group
资料来源:零点研究咨询集团2013年七零后群体研究。
Watch腕表
20. Investment of European companies in China
欧洲企业在中国的投资
2013年,欧盟28国对华投资新设立企业1523家,同比下降10.41%,实际投入外资金额72.14亿美元,同比
增长18.07%。
In 2013, 1523 new EU investment in China to set up enterprises, fell 10.41% year on year.
Actual investment value of $7.214 billion, up 18.07% from a year earlier.
Direct investment from European Companies ( 0.1 billions of USD)
中国实际利用欧洲外商直接投资金额(亿美元)
80
74
70
56
60
50
63
59
57
55
2009
2010
2011
55
2008
59
48
44
43
40
30
20
10
0
2003
2004
2005
2006
2007
2012
2013
22. THANKS!
Victor YUAN
Horizon Research & Consultancy Group
Mobile Phone : 0086 13801098824
Fax : (86-10) 57098003
Email: victor@horizonrcg.com
Address: East End Plaza, Building 1,No.24, Jiuxianqiao Middle Road,
Chaoyang District, Beijing,China
www.horizon-china.com