Educ213 E-Portfolio of Toledo

Statistics as a PLATFORM for Sustainable Development

We see the expression “sustainable development” everywhere these days, but what does it mean? Can we really pursue sustainable economic growth without harming our societies and the environment? And if we pursue prosperity today, are we condemning our children and grandchildren to poverty tomorrow? 

As global inequality and climate change become mainstream concerns, it asks 

the questions our generation needs to ask in terms everyone can understand.

Here are some issues that there should be a development that we need to do in order to sustain the fast growth and changes of our world today.

1. If we want to maintain economic growth, reduce poverty and save our environment, we must think hard about the choices we make.

2.  No country on its own can tackle global issues like climate change and managing our planet’s resources.

3.  The decisions we take today will affect not only our world, but also the world we pass on to future generations.

4.  All the things we buy have to be grown, extracted or manufactured and then transported and – sooner or later – disposed of.

5.  Change for the better rarely just “happens”. It needs the support and involvement of many different groups and institutions.

Statistics is a study which deals with the collections, tabulation or presentation, analysis, and interpretation of numerical or quantitative data.

Sustainable Development in the Philippines

http://www.cadi.ph/sustainable_development.htm

           As the 2010 National Statistics Office (NSO) survey declares that the present population of the Philippines raises up to 94M. Since, this statistics have been continue to have its development and growth through population, economy, health, infrastructure, politics and alike. The following data shows the demographic.   

The age structure of the Philippines population is as follows:

• 0-14 years: 34.9% (male 17,809,044/female 17,104,237)
• 15-64 years: 60.9% (male 30,384,504/female 30,410,691
• 65 years and over: 4.2% (male 1,808,966/female 2,382,735) (2010 est.)
• Birth rate: 25.68 per 1000 citizens
• Death rate: 5.06 deaths per 1000 citizens
• Net migration rate: -1.31 migrants per 1000 citizens
• Infant mortality rate: 19.94 deaths per 1000 births
• Fertility rate: 3.23 children per woman
• Literacy rate: 92.6% for those 15 and older

NSO records show that Philippine Population has been projected to grow by 1.95 percent during a five-year period beginning 2005. From 2005 the Pilipinos was numbered as 84.2 Million, each year the population grows rapidly with these growth the country has to provide needs of its people. There is great changes with economy, government, politics and alike. The provision of government ordinances and laws with regards with wastes management, reforestation, recycling and implementation of smoke belching scheme has become overcoming campaign within the departments. (DepED, DPWH, DENR,DA, Bureau of Fisheries and other sectors related to it) 

From Youtube

           With these number of population in the Philippines, we cannot deny the fact that amidst the great population here comes the great loads of problems not only economically but the physical aspects of the country, we think of great loads of wastes, pollution, unemployment, corruption  and a like. The next video shows that there is still remedy to sustain the enjoyment in living in our Mother Earth. 

SAN VICENTE, BANGA, SOUTH COTABTO
a barangay situated 13kms. from City of Koronadal and 7kms. from Banga Poblacion, having a land area of 1,970 hectares.

STATISTICAL PROFILE of its POPULATION
Male= 2,779
Female=2,814
               5,593
Total number of Household=1,379
Registered Voters= 2,493
Number of Churches= 12
Number of Puroks=6
Number of Schools= 5
1- Annex High School
3- Complete Elementary
1-Day Care Center
Number of Sitios= 8
Industrial:
Biotech Farms Inc- 37 hectares (Piggery)
JVJ Milling- 4 hectares (Feed Mill)
Fordson Rice Mill- 2 hectares (Rice Mill)


Brgy. San Vicente having those above statistics have gain confidence through labor force and it production through farming. 
One that makes the barangay polluted is having the Biotech Farms Inc. It is own by Koronadal Commercial Center (KCC) it a piggery and fowl raiser, where there wastes are being dumped in the river that passes to the barangay, they also produce bad smell during rainy hours that makes the whole barangay punggy.

The following documentaries is taken in our barangay (San Vicente, Banga, South Cotabato) with population of.....having six puroks and seven sitios.

This river is located at the Purok 1
 where nearer to the plant of Biotech Farms.

This part is the outlet where it is nearer
in the national highway going to Koronadal City.

                             This video shows the movement of water of the river which flows from the Biotech Farms located 
                                                                      at Sitio Upong of San Vicente, Banga, South Cotabato.


In San Vicente Elementary School, the pupils are taught to segregate wastes to recycle those materials which are recyclable.

This black cellophane are for recyclable materials. 

The children are taught to segregate as
non-recyclable and recyclable. 

A MRF (Material Recovery Facility) is a facility at which components of a mixed waste stream, in this case of co-mingled dry recyclables are extracted by the use of mechanical separation techniques. MRFs may be high and low technology facilities, depending on the sophistication of plant and equipment employed and the numbers of staff working in the operation of the process.Local government units have been showing an improved compliance to the Solid Waste Management Act or Republic Act 9003, citing an increase in the number of established materials recovery facilities nationwide to 4,260 as of June 15,2011.

http://lmp.org.ph/default/index.php?option=com_content&view=article&id=23:more-barangays-now-have-own-mrf-denr&catid=2:latest-updates&Itemid=3

This are the recyclable materials which stored
in the MRF Room, where the trash truck of the
Municipality take it after a month. (above& below)

This building is for the Material Recovery Faciltiy (MRF)
of San Vicente Elementary School 

This boy put his candy cellophane in the MRF Cellophane.

This painted portion of the school are made
 for Biodegradable materials,they located
it in the lower portion of the area.

This two boys takes the biodegradable materials
which will be thrown in the Biodegradable material
 dumped site. 

As teachers of our own school we cannot immediately teach older

people to do things which is good for them, but we have our own pupils and students who listen to us and do things what we want them to do. SEGREGATION of wastes start in the room where you are leading. Its a simple things to teach them to contribute saving our MOTHER EARTH.

Games on Waste Management

http://www.calrecycle.ca.gov/RecycleRex/Activities/CanIt.swf

EDUCATION 213- ADVANCED EDUCATIONAL STATISTICS

1: Introduction to Educational Statistics       

               Statistics is a set of tools used to organize and analyze data. Data must either be numeric in origin or transformed by researchers into numbers. For instance, statistics could be used to analyze percentage scores English students receive on a grammar test: the percentage scores ranging from 0 to 100 are already in numeric form. Statistics could also be used to analyze grades on an essay by assigning numeric values to the letter grades, e.g., A=4, B=3, C=2, D=1, and F=0.

Employing statistics serves two purposes, (1) description and (2) prediction. Statistics are used to describe the characteristics of groups. These characteristics are referred to as variables. Data is gathered and recorded for each variable. Descriptive statistics can then be used to reveal the distribution of the data in each variable.

Statistics is also frequently used for purposes of prediction. Prediction is based on the concept of generalizability: if enough data is compiled about a particular context (e.g., students studying writing in a specific set of classrooms), the patterns revealed through analysis of the data collected about that context can be generalized (or predicted to occur in) similar contexts. The prediction of what will happen in a similar context is probabilistic. That is, the researcher is not certain that the same things will happen in other contexts; instead, the researcher can only reasonably expect that the same things will happen.

Prediction is a method employed by individuals throughout daily life. For instance, if writing students begin class every day for the first half of the semester with a five-minute freewriting exercise, then they will likely come to class the first day of the second half of the semester prepared to again freewrite for the first five minutes of class. The students will have made a prediction about the class content based on their previous experiences in the class: Because they began all previous class sessions with freewriting, it would be probable that their next class session will begin the same way. Statistics is used to perform the same function; the difference is that precise probabilities are determined in terms of the percentage chance that an outcome will occur, complete with a range of error. Prediction is a primary goal of inferential statistics.

2: Central Tendency 
3: Measures of Correlation
4: Variables
5: Normal Probability Distribution
6: Sampling Theories and Hypothesis Testing

               SAMPLING means is the target population is the entire group a researcher is interested in; the group about which the researcher wishes to draw conclusions.

 Bias

Bias is a term which refers to how far the average statistic lies from the parameter it is estimating, that is, the error which arises when estimating a quantity. Errors from chance will cancel each other out in the long run, those from bias will not.
The following illustrates bias and precision, where the target value is the bullseye:

	Precise	Imprecise
Biased
Unbiased

7. T-test for Independent Sampling
8. T-test for Dependent Sampling
9. Z-Test
10. Chi-Square Test
11. ANOVA
12. Linear Regression
13. Multiple Regression
14.The Normal Probability Distribution

Examples of Normal Curve

Click the image for animations

The 10 Deutschmark Bill

The normal curve is often called the Gaussian distribution, after Carl Friedrich Gauss, who discovered many of its properties.  Gauss, commonly viewed as one of the greatest mathematicians of all time (if not the greatest), is properly honored by Germany on their 10 Deutschmark bill.

Johann Carl Fredrich Gauss              
   1777 - 1855

There are problems to whose solution I would attach an infinitely greater importance than to those of mathematics, for example touching ethics, or our relation to God, or concerning our destiny and our future; but their solution lies wholly beyond us and completely outside the province of science.
Quoted in J R Newman, The World of Mathematics (New York 1956).One of the famous quotations of Gauss

June 18, 2011

Today is the second meeting of our class, the 1st group reported about the CENTRAL TENDENCY, it was delivered well, they defined first what are the components of the central tendency, after that Prof. Ava gave us a short quiz.

In the afternoon the 2nd group reported on the MEASURES of Variation. The group had discussed it well but Prof. Ava made some clarifications with regards to the Range and Quartile Deviation.
June 25,2011
It was the third day of Stat. class and it was so fortunate that we were able to transfer to an air-con room,it was so cool. This day Prof. Ava introduce how to solve Pearson R, Spearman Rho, Deviations using the MS-Excel, this day also we checked our assignments and have some arguments getting the range.

July 2, 2011

Today was a nose bleeding day for me, we had our Mid-terms. It was an easy test IF WE HAD PRACTICE and STUDY our lessons...But sad to say I didn't done it well. Two groups reported in the afternoon on the Measures of Correlations and Normal Probability Distribution.
July 9, 2011
Today, we plan of what are we going to do in the last meeting we have next Saturday. Prof. Ava discuss the ANOVA and the lessons she had included in the final exams. She gave also time in fixing our blogs.
July 16, 2011
Its a day of accomplishments, that,s what I thought. We had our final exams, then had our Graduate School Orientation at the SMC Hall. Then, we had our lunch at EMR Center where we had short fellowship with Prof. Ava. After that we went back to our Room and continue the final exam. Then, after the finals we had a short presentation of our blogs.