Dr Ethan E YeohRelated papers
Master in YOGA STUDIES. CORPO E MEDITAZIONE NELLE TRADIZIONI DELL'ASIA Brochure 2016/2017
Presentazione Con oltre 20 milioni di praticanti negli Stati Uniti, con centinaia di brevetti registrati e di sistemi di tutela di copyright, con decine di palestre in ognuna delle grandi città europee, con un giro d'affari annuale che supera, tra Nordamerica e Unione Europea, diversi miliardi di dollari, lo yoga è senza dubbio uno dei più straordinari esempi di "commodification" e commercializzazione di beni simbolici dei nostri giorni. A fronte di queste cifre non stupisce che economisti e giuristi in tutta Europa si stiano dedicando alla messa a punto di regolamenti e forme di tutela legislativa atte a governare un siffatto fenomeno, che tocca da vicino la vita e le risorse di milioni di persone. L'Università Ca' Foscari Venezia, sempre attenta ad anticipare e interpretare tendenze come queste, propone qui il primo Master europeo dedicato allo yoga. Unico nel suo genere e pionieristico nei suoi intenti, il Master in Yoga Studies coniuga le più innovative ricerche storiche e filologiche sullo yoga alle più avanzate conoscenze in campo medico-scientifico e giuridico-economico. Il Master in Yoga Studies è un momento formativo irrinunciabile sia per insegnanti e praticanti di yoga che intendano perfezionare le proprie competenze pratico-teoriche, sia per la preparazione di figure professionali competenti, in grado di offrire consulenze altamente qualificate ormai indispensabili. Il Master è stato pensato, dunque, per chiunque si voglia confrontare con uno dei più inattesi fenomeni di massa del ventunesimo secolo, dal singolo privato ai governi regionali e, com'è naturale, per tutti i neolaureati in materie indologiche, filosofiche e storico-religiose. Il Master si rivolge prevalentemente a: laureati di qualsivoglia disciplina-in possesso almeno della laurea triennale-che desiderino arricchire la loro professionalità, le loro conoscenze e competenze relativamente all'ambito sia teorico che pratico dello yoga e delle tradizioni meditative e corporee dell'Asia ad esso collegate.
The Souls of Elite Black Folk: The Strivings of the New Negro and New African
The catastrophic ascension of the Europeans to the stage of world history entailed the launching of a race war against Africans, which is still raging to this day. This race war is multifaceted; however, its ultimate objective is the racial extermination of the Africans. In the interim period before the racial extermination of Africans, the Europeans have chosen to embark on a cruel project to "falsify African consciousness". This falsification is achieved through a process of culturicide. By the latter, we imply a form of "intellectual warfare" waged by the Europeans to confuse Africans, especially their most gifted members, about their African identity and culture to serve global white supremacy. The integrationist "double-consciousness" of the New Negros in America and the "New Africans" in South Africa is the highest stage of this intellectual warfare. The latter has resulted in the emergence of integrationist double-consciousness (which accepts whites and seeks to coexist with them) among the black elite, like Dubois, and revolutionary double-consciousness (which rejects both whites and white supremacy) among ordinary Africans both in America and South Africa. Using African phenomenological framework, this paper will argue that the integrationist double-consciousness of the black elite must be abandoned and replaced with the revolutionary double-consciousness of ordinary Africans such as Garveyites and amaqaba. This is in order to destroy whites and global white supremacy and to restore African Renaissance
BOSQUE TROPICAL SUBCADUCIFOLIO Selva
En general se trata de bosques densos que miden entre 15 a 40 m de altura, y más o menos cerrados por la manera en que las copas de sus árboles se unen en el dosel. Cuando menos la mitad de sus árboles pierden las hojas en la temporada de sequía. Sus temperaturas son de 0°C a 28 °C .
Rebooting the Study of Forager Plant Economies: The Potential of Submerged Archaeological Sites
Environmental Archaeology, 2024
Signe S. Termansen, Peter M. Astrup, Welmoed A. Out & Niels N. Johannsen 2024. Rebooting the Study of Forager Plant Economies: The Potential of Submerged Archaeological Sites. Environmental Archaeology. The contribution of plants to the diets and wider economies of most prehistoric foragers is inadequately studied and poorly understood, also in comparison with other aspects of their economies and with prehistoric plant economies from the Neolithic onwards. This situation even applies to the Mesolithic of southern Scandinavia, which is a particularly intensely studied prehistoric forager sequence. In this paper we consider taphonomic, methodological and ideological reasons for the relative lack of research focus, after which we discuss the potential of strategic bulk sampling for plant macroremains at submerged sites. We then briefly present a pilot study of such macroremains obtained from the submerged site Hjarnø Sund, Denmark, and discuss next steps, including methodological initiatives to improve the distinction between anthropogenic and non-anthropogenic material. We conclude that the potential of this type of site in the effort to understand the dietary and wider economic roles of plants in the Mesolithic far exceeds the general level of attention that they have received so far. Accordingly, we argue for increased focus, strategic sampling and a sustained, longer-term research effort in order to improve our understanding of this central aspect of prehistoric forager life and culture.
Organization culture
Organisational culture is a widely used term but one that seems to give rise to a degree of ambiguity. Watson (2006) emphasises that the concept of culture originally derived from a metaphor of the organisation as 'something cultivated'. For the past number of decades, most academics and practitioners studying organisations suggest the concept of culture is the climate and practices that organisations develop around their handling of people, or to the promoted values and statement of beliefs of an organisation (Schein, 2004). Schein (2004) highlights that 'the only thing of real importance that leaders do is to create and manage culture; that the unique talent of leaders is their ability to understand and work with culture; and that it is an ultimate act of leadership to destroy culture when it is viewed as dysfunctional' (p.11). Culture therefore gives organisations a sense of identity and determines, through the organisation's legends, rituals, beliefs, meanings, values, norms and language, the way in which 'things are done around here'. An organisations' culture encapsulates what it has been good at and what has worked in the past. These practices can often be accepted without question by long-serving members of an 4 2 Organisational culture: lessons from the literature ORGANISATIONAL CULTURE: LESSONS FROM THE LITERATURE 5 organisation. One of the first things a new employee learns is some of the organisation's legends. Legends can stay with an organisation and become part of the established way of doing things. Over time the organisation will develop 'norms' i.e. established (normal) expected behaviour patterns within the organisation. A norm is defined as an established behaviour pattern that is part of a culture. Schein (2004) emphasises that 'perhaps the most intriguing aspect of culture as a concept is that it points us to phenomena that are below the surface, that are powerful in their impact but invisible and to a considerable degree unconscious' (p.8). Schein uses an analogy that culture is to a group what personality or character is to an individual. 'We can see the behaviour that results, but often we cannot see the forces underneath that cause certain kinds of behaviour. Yet, just as our personality and character guide and constrain our behaviour, so does culture guide and constrain the behaviour of members of a group through the shared norms that are held in that group' (p.8). Schein (1990) emphasises that there are visible and invisible levels of corporate culture (the 'culture iceberg' analogy-the visible levels (surface manifestations) of the 'culture iceberg' incorporate observable symbols, ceremonies, stories, slogans, behaviours, dress and physical settings. The invisible levels of the 'culture iceberg' include underlying values, assumptions, beliefs, attitudes and feelings. Often, change strategies focus on the visible levels. Deal and Kennedy (1982) emphasise the more visible levels of culture (heroes, rites, rituals, legends and ceremonies) because it is these attributes they believe shape behaviour. But it is the invisible levels that may be of more interest to public sector organisations in terms of their influence in progressing or impeding organisational change.
Quantitative Analysis
Quantitative Decision Making
Eik Den Yeoh
2015
Contents
1.0 Introduction 2
2.0 Objective 3
2.1 Define Hypotheses 3
3.0 Finding and Discussion 4
3.1 Two Sample T-Test for Sample 4
3.2 Correlation and Multiple Linear Regression 5
3.3 Forecasting Techniques 7
4.0 Conclusions & Recommendations 9
5.0 Reference 10
6.0 Appendix 1.0 - VARIABLES 11
6.0 Appendix 2.0 - Data 11
6.0 Appendix 3.0 16
Document History and Version Control
Name
Yeoh Eik Den
Student Id
TP038999
Documents Version
1.0
1.0 Introduction
In this introduction, we give an overview about this project report. As our objective, we will be bisected into different quantitative management techniques to examine and analyses the particular issue. We have defined the objective and explained the data for the past 10 years records that been identified by different areas such as metropolitan, city and town. We will be focus to used two-sample t-test for difference, correlation/multiple linear regression and forecasting techniques to discussed about the issue that been identified in 2.0 aims, objectives and hypotheses.
Firstly, understand the demand of the fixed deposit been deposited by personal wealth from different areas. As Ministry of Finance, this are great to distingue the different of areas for future development and assist the people to improve of standard living.
Secondly, investigation for the relationship of independent variable and multiple dependent variables in this case study. Where will be important to understand the influences of the dependent variables to the dependent variable.
Finally, we would prepared for seasonal behavior for fixed deposit that been deposited to predict the trend analysis of this report. So, that Ministry of Finance can be forecast and predict the trend for the country economical.
We conclude our results and provide the best fitted estimated model for forecast deposit rates of best fitted to the personal wealth at the end of the report.
2.0 Objective
Our main objective in this report is to provide the significant test of data and forecast the deposit rates for different areas which involve in metropolitan, city and town. The outcomes of this study could be useful for Ministry of Finance in providing better insights of forecasting and understand the demand.
We have collected the data and provided in the appendix 2.0. The study for this is to analyze by applying different quantitative management techniques. Basically we collected the data from 10 years since 2005 to 2014.
2.1 Define Hypotheses
We identified the hypotheses for this report as below:
First Hypothesis: Two Sample T-Test for Difference
We use two sample t-test to confirm the assumption for the population variances to compare the average fixed income deposited per account in different areas which involved Metropolitan, City and Town. We test whether the demand is the same across this 3 areas.
H0: p ≤ α, there are same demand across three areas.
H1: p > α, there are different demand across three areas.
Second Hypothesis: Correlation and Multiple Linear Regression
Second hypothesis is to identify the independent variable with multiple dependent variables and the relationship between fixed deposit and government bond whether fixed deposit interest increase then bond decrease or the other way round.
H0: p ≤ α, fixed deposit interest has relationship with government bond interest.
H1: p > α, fixed deposit interest no relationship with government bond interest.
Third Hypothesis: Forecasting Techniques
Third hypothesis is find the quarter index that which quarter have most demand average.
H0: Quarter 4 is the higher demand compare to other quarter for three areas average.
H1: Quarter 4 is not the higher demand compare to other quarter for three areas average.
3.0 Finding and Discussion
This section provided the hypothesis outcome.
3.1 Two Sample T-Test for Sample
Descriptive statistics of all areas are calculated to find that whether the data set are following. The descriptive statistics of all the areas as follows:
First we plot a new table to compare the difference areas, where you can find in Appendix 3. Then, we populated the result via excel and output as figure 3.1.2 below.
Figure 3.1.2 Compare the two-sample test.
Assume that α = 0.05;
and, the hypothesis define as
H0: p ≤ α, there are same demand across three areas.
H1: p > α, there are different demand across three areas.
The p-value for 3 results are almost close to 0.
Therefore, for Metropolitan compare with City. The p-value for two-tail is p < α; we reject the null hypothesis. For City compare with Town and Metropolitan compare with Town, the result won’t be very different as p < α. Thus, we reject the null hypothesis.
As conclusion, we reject the hypothesis for this two sample t-test as confirm our assumption that the population variances are almost equal. Which mean that, the demand for different areas is difference.
3.2 Correlation and Multiple Linear Regression
From the case study, we define dependent variable as total personal wealth. However, the independent variables are average fixed deposit per account, fixed deposit interest and government bond interest.
The population of regression model is:
Multiple Regression: Y = a + b1X1 + b2X2 + b3X3 + u
Whereby
Y = total personal wealth;
X1 = average fixed deposit per account;
X2 = fixed deposit interest;
X3 = government bond interest;
a = the intercept;
b = the slope;
u = the regression residual;
Assumed that the error u is independent with constant variance.
The regression output has three components (Regression statistics, ANOVA, Regression coefficients) as show at figure 3.2.1
Hypothesis define as:
H0: p ≤ α, fixed deposit interest has relationship with government bond interest.
H1: p > α, fixed deposit interest no relationship with government bond interest.
Figure 3.2.1 Summary output by excel statistics analysis for multiple linear regression.
From the regression statistics table given:
R2 = 0.7737
Correlation between Y is 0.8796 (when squared gives 0.7737)
Adjusted R2 = 0.7679
The standard error here refers to the estimated standard deviation of the error term u.
Mean that, 77.37% of the variation of Y around is explained by the regression of X1, X2, and X3. Remaining 22.63% will explained by other unknown factors.
Next we test the confidence intervals for slope coefficients as 95% interval by the hypothesis of zero slope coefficient below:
The coefficient of FD has estimated standard error of 0.6479, t-statistic of 19.7720 and p-value of almost close to 0. It is therefore statistically significant at significance level α = 0.05 as p < 0.05.
For RFDP, we assume α = 0.05 and the p-value < 0.05. Thus, RFDP as well significant.
For RGB, let’s assume α = 0.05 and the p-value > 0.05. Thus, RGB is insignificant.
That proven, the relationship for this government bond is not significant in this multiple linear regression.
The multiple regression for this is:
Y = 1022.7661 + 12.8096X1 -172.1339X2 -50.1923X3
Meaning that, there is no relationship between fixed deposit interest and government bond interest. Therefore, we reject the null hypothesis.
3.3 Forecasting Techniques
The hypothesis for this section is
H0: Quarter 4 is the higher demand compare to other quarter for three areas average.
H1: Quarter 4 is not the higher demand compare to other quarter for three areas average.
The multiple regression that been populated at section 3.2 is Y = 1022.7661 + 12.8096X1 - 172.1339X2 - 50.1923X3
Therefore, linear trend been populated based on the multiple regression and fill in all the variables to generate the result.
Figure 3.3.2 Linear Trend graph for 3 different areas and forecasting by seasonal chart.
Figure 3.3.2 data are extract from table Figure 3.3.3, 3.3.4 & 3.3.5 that based on the linear trend populated by multiple regression.
Figure 3.3.3 Seasonal estimates using a multiplicative model for Metropolitan
For this figure 3.3.3, we know that Q4 average is the higher demand compare to other quarter.
Thus, we do not reject null hypothesis for Metropolitan.
Figure 3.3.4 Seasonal estimates using a multiplicative model for City
For figure 3.3.4, the average forecast for Q4 is higher among other quarter in city. Thus, we do not reject the null hypothesis for city.
Figure 3.3.5 Seasonal estimates using a multiplicative model for Town
For figure 3.3.5, the average forecast for Q4 is higher among other quarter in town. Thus, we do not reject the null hypothesis for town.
As conclusion, the 3 areas null hypothesis are true. Therefore, we do not reject null hypothesis as summary.
4.0 Conclusions & Recommendations
Outcome
Hypothesis 1
Reject
Hypothesis 2
Reject
Hypothesis 3
Do not reject
Based on the outcome, confirm that the demand from different area have different demand. Whereby, there are no relationship between fixed interest rate and government bond. However, we prove that average of this 3 different area have the most demand during quarter 4.
The recommendation for Ministry of Finance, prediction of the personal grow even year to year is increase. In the chart figure 3.3.2. They should focus to provide campaign or any awareness in town, to help town further improve their personal wealth.
There are limitation on data that show the relationship for government bond. Thus, we do not know how much the personal wealth is involve for government bond. As mentioned in section 3.2, there are 22.63% will explained by other factors that influence the relationship for the personal wealth. This is quite large number that government does not able to predict and forecast accurately what will influence the personal wealth.
5.0 Reference
Jon Curwin, Roger Slater and David Eadson. Quantitative Methods for Business Decisions, 7th Edition 2013. Publisher by Andrew Ashwin.
By ET Bureau (12 Jan 2015, 02.31PM IST). Four things to check for in a fixed deposit. Retrieved from http://economictimes.indiatimes.com/wealth/fixed-deposits/four-things-to-check-for-in-a-fixed-deposit/articleshow/45832436.cms
NDTV (02 May 2015). Why you should rethink fixed deposit investments. Retrieved from http://profit.ndtv.com/news/your-money/article-why-you-should-rethink-fixed-deposit-investments-757201
By Robert Brokamp. What is a bond? Retrieved from http://www.fool.com/bonds/bonds01.htm
6.0 Appendix 1.0 - VARIABLES
Area : the area that the banks located
RFDP : primary interest rate on fixed deposit (%)
FD : average of fixed deposit per account (RM ‘000)
PW : average personal wealth (RM ‘000)
RGB : interest rates on government bonds (%)
6.0 Appendix 2.0 - Data
Time
Area
RFDP
FD
PW
RGB
2005Q1
Metropolitan
4.5
28
260
3.2
2005Q1
City
4.5
15
200
3.2
2005Q1
Town
4.5
5
120
3.2
2005Q2
Metropolitan
4.5
27
270
2.9
2005Q2
City
4.5
14
203
2.9
2005Q2
Town
4.5
5
123
2.9
2005Q3
Metropolitan
4.7
26
280
3.2
2005Q3
City
4.7
14
208
3.2
2005Q3
Town
4.7
6
124
3.2
2005Q4
Metropolitan
4.6
25
292
3
2005Q4
City
4.6
13
210
3
2005Q4
Town
4.6
6
127
3
2006Q1
Metropolitan
4.6
27
301
3.1
2006Q1
City
4.6
14
215
3.1
2006Q1
Town
4.6
6
129
3.1
2006Q2
Metropolitan
4.55
25
310
2.75
2006Q2
City
4.55
13
218
2.75
2006Q2
Town
4.55
6
131
2.75
2006Q3
Metropolitan
4.55
26
325
2.95
2006Q3
City
4.55
14
220
2.95
2006Q3
Town
4.55
5
133
2.95
2006Q4
Metropolitan
4.55
25
332
3.2
2006Q4
City
4.55
13
225
3.2
2006Q4
Town
4.55
4
134
3.2
2007Q1
Metropolitan
5.1
30
339
3
2007Q1
City
5.1
18
228
3
2007Q1
Town
5.1
10
135
3
2007Q2
Metropolitan
5.1
32
345
3.1
2007Q2
City
5.1
19
230
3.1
2007Q2
Town
5.1
10
129
3.1
2007Q3
Metropolitan
4.9
31
350
2.75
2007Q3
City
4.9
17
232
2.75
2007Q3
Town
4.9
9
134
2.75
2007Q4
Metropolitan
4.75
23
360
2.9
2007Q4
City
4.75
14
235
2.9
2007Q4
Town
4.75
7
138
2.9
2008Q1
Metropolitan
4.75
24
367
2.95
2008Q1
City
4.75
15
238
2.95
2008Q1
Town
4.75
8
140
2.95
2008Q2
Metropolitan
4.9
32
369
2.75
2008Q2
City
4.9
17
240
2.75
2008Q2
Town
4.9
9
142
2.75
2008Q3
Metropolitan
4.9
29
371
2.95
2008Q3
City
4.9
18
242
2.95
2008Q3
Town
4.9
10
144
2.95
2008Q4
Metropolitan
4.82
24
373
3
2008Q4
City
4.82
17
250
3
2008Q4
Town
4.82
8
145
3
2009Q1
Metropolitan
4.82
23
380
2.9
2009Q1
City
4.82
16
251
2.9
2009Q1
Town
4.82
8
146
2.9
2009Q2
Metropolitan
4.73
22
390
3.1
2009Q2
City
4.73
15
253
3.1
2009Q2
Town
4.73
7
148
3.1
2009Q3
Metropolitan
4.66
22
402
3.2
2009Q3
City
4.66
15
255
3.2
2009Q3
Town
4.66
6
149
3.2
2009Q4
Metropolitan
4.75
24
410
2.95
2009Q4
City
4.75
14
256
2.95
2009Q4
Town
4.75
7
150
2.95
2010Q1
Metropolitan
4.6
23
417
3.1
2010Q1
City
4.6
13
257
3.1
2010Q1
Town
4.6
7
151
3.1
2010Q2
Metropolitan
4.58
23
421
2.75
2010Q2
City
4.58
12
260
2.75
2010Q2
Town
4.58
6
152
2.75
2010Q3
Metropolitan
4.7
26
425
2.95
2010Q3
City
4.7
14
262
2.95
2010Q3
Town
4.7
7
153
2.95
2010Q4
Metropolitan
4.7
25
430
3
2010Q4
City
4.7
15
264
3
2010Q4
Town
4.7
7
153
3
2011Q1
Metropolitan
4.75
24
440
2.85
2011Q1
City
4.75
15
265
2.85
2011Q1
Town
4.75
8
154
2.85
2011Q2
Metropolitan
4.65
23
450
3.2
2011Q2
City
4.65
15
266
3.2
2011Q2
Town
4.65
6
155
3.2
2011Q3
Metropolitan
4.5
22
453
2.85
2011Q3
City
4.5
15
267
2.85
2011Q3
Town
4.5
5
157
2.85
2011Q4
Metropolitan
4.45
21
451
2.9
2011Q4
City
4.45
14
270
2.9
2011Q4
Town
4.45
4
158
2.9
2012Q1
Metropolitan
4.45
21
462
2.85
2012Q1
City
4.45
15
271
2.85
2012Q1
Town
4.45
4
160
2.85
2012Q2
Metropolitan
5
33
467
2.75
2012Q2
City
5
18
273
2.75
2012Q2
Town
5
10
162
2.75
2012Q3
Metropolitan
4.75
25
469
3
2012Q3
City
4.75
17
275
3
2012Q3
Town
4.75
7
163
3
2012Q4
Metropolitan
4.58
21
474
2.95
2012Q4
City
4.58
16
276
2.95
2012Q4
Town
4.58
5
163
2.95
2013Q1
Metropolitan
4.5
24
480
2.85
2013Q1
City
4.5
15
278
2.85
2013Q1
Town
4.5
5
164
2.85
2013Q2
Metropolitan
4.3
22
482
2.9
2013Q2
City
4.3
15
280
2.9
2013Q2
Town
4.3
3
165
2.9
2013Q3
Metropolitan
4.6
23
485
3
2013Q3
City
4.6
14
281
3
2013Q3
Town
4.6
6
166
3
2013Q4
Metropolitan
4.6
24
490
2.85
2013Q4
City
4.6
13
282
2.85
2013Q4
Town
4.6
5
167
2.85
2014Q1
Metropolitan
4.48
23
493
3.2
2014Q1
City
4.48
12
284
3.2
2014Q1
Town
4.48
4
168
3.2
2014Q2
Metropolitan
4.48
24
496
2.95
2014Q2
City
4.48
12
286
2.95
2014Q2
Town
4.48
4
170
2.95
2014Q3
Metropolitan
4.75
24
501
2.85
2014Q3
City
4.75
14
288
2.85
2014Q3
Town
4.75
7
171
2.85
2014Q4
Metropolitan
4.6
24
503
3
2014Q4
City
4.6
14
292
3
2014Q4
Town
4.6
6
172
3
6.0 Appendix 3.0
Staffordshire University
12
Prepared by Yeoh Eik Den
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