China and USA GDP

It is clearly seen in the figure that Macroeconomic trends - Economic growth - Evolution of GDP of China was more influential than USA. This means that China is more profitable institution than USA. It also notable that China’s GDP in every succeeding year was increasing. This means that most investors believed that China is more profitable than United States of American. On the other hand it is also notable that USA has decreasing GDP from 2004 to 2008 which is not very favorable to their institutional result the USA government have to work hard to improved the said data or else it will lead to a more worse scenario for their country.

Recommendation
1. The USA government has to develop reputations and impose a good image to the market and to their neighboring country to gain positive feedback.
2. The USA CEO has to review basic knowledge on accountancy and make new strategy to gain investors trust. This is because reputation was not fully satisfied.
3. For China government your patients and new trends of marketing really rock the world keep it up and continue to develop new trends and invention. (Science and technology really help)

Note: The figure was formulated from the OECD data.

The enough misery stamps before a star bankrupt.

Investing from a certain company or firm is one of the risks done by the investors but before engaging with the institution precise measurement for the profitability, earnings per share, equity valuation, market value, Okun laws, and etc were done.
Though this measure was done by the investors, the risk is still enveloped in investment. Bankruptcy of institution is still not measurable because forecasting of profits and earnings for future is still imaginary. Though there is a formula for forecasting or trends for the future earnings, 100% efficiency are still unmet. Meaning if bankruptcy was declared investors are the losers of the battle field and they are stamps and misery is not enough to overcome their failure.
Generally success and failure of the firm or company depends on the tactics or strategy of the CEO however the CEO can also manipulate everything. They can make a good balance sheet for presentation but the realities are behind the actual settings. Investors have to be more observant and be keen on the balancing the financial statements. Good accountant and econometrician will guide investors in the nice track of the battle of business.

United Kingdom hours worked by period

Frequency distribution of hours worked by period

-Period--------Hours worked
---1995---------------1739
---1996---------------1738
---1997---------------1737
---1998---------------1731
---1999---------------1719
---2000--------------1708
---2001---------------1711
---2002--------------1692
---2003--------------1683
---2004--------------1669

The table reveals the frequency distribution of hours worked by period of the United Kingdom. As you can see on the data above there were a decreased in hours worked in every succeeding period as evidenced by the value of 1739, 1738, 1737, 1731, 1719, 1708, 1711, 1692, 1683, and 1669 respectively. The sudden decrease of number of hours per period indicates that there was an increased in number of employee. This implies that employees recieved a beninificial rewards for their working force area because their time of work were minimized without any deduction from tehir salary.

Figure: Frequency distribution of hours worked by period

Legend

1--------1995--------
2--------1996
3--------1997--------
4--------1998
5--------1999--------
6--------2000
7--------2001--------
8--------2002
9--------2003--------
10--------2004

The Figure shows the frequency distribution of hours worked by period of the United Kingdom. It reveals that there were a decrease in the distribution of hours work in every succeeding period as evidenced by the frequency values of 1739, 1738, 1737, 1731, 1719, 1708, 1711, 1692, 1683, and 1669 respectively. It is also notable that in 2001 there were a slight increased in hours worked.

Tweety Bird

Tweety bird at 60

One of the most common cartoon character is Tweety bird. The character always reminds me my childhood life but I am wondering if Tweety bird can be also an old one like me he he he.

One monday morning my co-employee show me her stuff. I was then amaze because she presented 60 year old Tweety Bird on her desktop screen. She told me that it was send by her younger daugther in Dubai.

At first tell to my whoa! Tweety Bird is at 60. In this very unremarkable picture I ask her to send it to my email so that I can share it to my viewers, and here is now.

This is very funny isnt it. But at the other part of it there is a spark that we will be getting older.

Check out his texture, feather, skin and expression. He is not that energetic and hyper active. Meaning all of us can be that way in due time so count your good deeds and be prepare for your future like seating in a racking chair. Wait for your pension and money from your business. (If you don’t have business engage in stocks but be careful with the company before invest check their earning, profitability and cash on hand).

Radio frequency Idnetification operating range, antenna function, transmissin and reception

Radio Frequency Identification Operating Ranges

The operating ranges of radio frequency identification have been categorized as low frequency, high frequency, ultrahigh frequency, and microwave. These ranges determine the speed and accuracy of the system, and thus establish the inferiority or superiority of the system. Usually, it is in the trade-offs that consumers of tags in the supply chain would base their choices of devices with the able guidance of technologist. Otherwise, full capacity benefits will not be derived.

Antenna Function, Transmission and reception

As a technology, radio frequency identification came about as an outgrowth of electronic designs. Radio waves were discovered and tapped for transistors and microprocessors originally, but, were found to be useful in transponders. Mario Cardullo successfully produced passive, read and write radio frequency identification consisting of antenna coil, transceiver with a decoder, and transponder which made him the first person to have it patented in 1969.

Today as radio antenna function in the transmission and reception of messages or information have been efficiently assisting commerce and industry, the units come in various forms, style, and sizes.

Basically, antennae or aerials are metallic rods or wires that put on the air and receive on the air the data between tags and readers or interrogators. Antennae or aerials come in forms dependent upon radiation, intensity, resistance and gain, as well as direction, width of beam, width of band in accord with consumer requirements.

Fig. 4 Antenna to tag (Backscatter)

The signal from the interrogator is relayed out through its antenna which will in turn be received by the antenna of the tag. In other words, there is an antenna to antenna correspondence in terms of radio waves transmission. The tag uses the interrogators’ signal to gain power until it generates a return signal which can be read by an interrogator as this particular signal is a carrier of information contained in the tag as encrypted identification of the item the tag is attached to.

Fig. 5. Tag to Antenna

Once the tag gains power and the informational data is passed on to the antenna and then received by the antenna of the interrogator, the interrogator will communicate to the appropriate middleware Host computer.

What is Radio frequency Idnetification System

Radio Frequency Identification System

Traditionally, radio frequency identification system is made up of coupled inductance circuit and antenna in a transponder or tag, antenna in a receiver, antenna in a reader or interrogator, and data base computer.

Initially, in the manufacture of radio frequency identification microchip, Silicon, 2% by mass added to 0.4% Carbon, is the magnetized and demagnetized steel used in between inductance circuit as information storage. However, the advent of chip-less tags, polymer tags, as well as advances in printing techniques made silicon-less tags. These silent devices are actually the same capable tags that can send to a reader its identity number as well as the identifier number for the material it is attached to. But the so called common sense or brain of the radio frequency identification is in its middleware and backend system called database. It is the middleware that interprets the data read by an interrogator from the tags, and subsequently transmits this to the database for storage.

Radio frequency Idnetification

Effects of Reflection, Diffraction and Refraction on Radio Frequency

The atmospheric environment with which radio frequency waves may be propagated and transmitted presents itself with a variable of impediments. Thus, waves may be reflected, diffracted or refracted depending upon the nature of the barrier, whether it is a conductor, semi-conductor, or non-conductor, and the frequency of the waves.

Radio Frequency Transmission and Reception

Conventionally, radio frequency waves were used as carriers of information depending upon the couplings used to produce specified series of charges acting as codes. These carrier waves may then be intercepted by a receiver in a reader system. The antenna would again vary depending upon the volume of information it is enabled to receive or transmit.

Radio frequency identification commonly known as tags come in a variety of Company tailor-made style with comparable if not as good as features employed in the market. As a tag the transponder is invented with an integrated circuit or IC fixed to a transmitter. This combination is secured in between shield. The set may be contained to a size desired by the consumer. Sometimes it comes in as large as a credit card, and sometimes as small as a fraction of a centimeter.
It is usually the transmitter size, transmission range, and its role in the merchandize that correspondingly determines its dimension.

What is timeshare industry

Timeshare industry

Trade selling time of shares and ownership of a property provide consumers with rights to have vacation to the places specified in the package as shareholders and part owners was so called timeshare industry. This was mainly observed in hotels as resorts. The idea was derived from ski resorts and acquired band wagon of timeshare holding business. From then it was discovered that there were benefits of having timeshare it was actually enjoyed by the United States of America, Africa, etc. This was very beneficial because timeshare selling and holdings became a mainstream source of employment as well as income. Developments provide spot to the trade especially with the trade-off. In the long run, the idea of timeshare as a simple purchase of a luxurious vacation home in increments of a week or more became so popular to become globally accepted.

Diabetic article

Here is one of my project in medicine under biostatistics.

It has been found out that there are minimal numbers of diabetics with gene A. Its sensitivity was 0.22222 categories as low, and has specificity of 0.38095. This is almost one third of the probability of without gene A in health and true negative rate. The findings reveal that there is a 30% or 0.30 probability of gene A. This is also called the risk of gene A or the estimated probability. The probability of the exposed cases is equal to 0.4667 while the probability of the exposed control is equal to 0.1333. The total of exposed respondents has a value of 0.3000. With 95% confidence, the interval 1.410185 to 27.15574 contains the unknown mean μ.

United Nation: Overall Work Satisfaction

Overall Satisfaction with my Organization

The Overall Satisfaction with my Organization had mean scored 2.80 and with standard deviation of 1.06. Of the four related to this factor, respondents just agreed with the statement of “I have a good understanding of the mission and the goals of this organization” with the mean score of 4.10 and standard deviation of 0.91. This was followed by the statement “There is excellent teamwork and cooperation among the employees within this organization” with the mean score of 3.90 and standard deviation of 1.02. The participants feel that there service will be last until retiring age. And respondents have strong commitment with their job as evidenced by the percentage value of 45% and 35% (see figure4).

As noted in figure 5, three fourth of the selected respondents does not treated fairly in the United Nation Secretariat Headquarters as evidenced by the responses 8 disagree and 7 strongly disagree.

The respondents agreed with the statement of “I would recommend others to work in this organization” with the responses of 19 out of 20 and approximately 95% of the total sample (see figure 6).

United Nation Work and Life Balance

Work and Life Balance

The eleven (11) respondents of this study believed that “The environment in this organization supports a balance between work and personal life” as evidenced with the mean score of 3.65 and standard deviation of 1.09. There was a big difference between Male and female with respect to this topic, there was 4.00 mean score in male respondents and standard deviation of 0.63 which was classified as Agree while female has 3.22 mean score and standard deviation of 1.39 classified as Undecided.

Analysis of Correlation between male and female work and life balance in United Nation Secretariat

There is no significant correlation between male and female work and life balance in United Nation Secretariat headquarters as evidenced by the computed chi-square value of 8.250 under the degree of freedom 6 and tabulated value of 12.59 with 0.05 level of significant.

Western Bank Financial Statement Analysis

Analysis of the Western Bank Financial Statement
Ernst & Young Auditing
2009 Angelbert D. Morales

I. Introduction
Western Bank is a financial institution with 40,000 account holders and with 15 different branches. Its main location can be found in Southwestern Ontario. It also has 20 different types of deposit account and 20 type of loan.
In order to verify the reasonableness of some of the numbers reported in Western financial statements. The researcher scrutinized the informational data. This is to investigate the lapses of the computed data done by the Western Bank.

The researcher calculated the total interest income by multiplying the interest rate on each type of loan and the loan balance. Then summed over all type of loans and summed over all time periods.

Likewise, the informational data will give the Western Bank manager a broader level of understanding on the computation of the total interest income per month and even per year.

Heart and Lungs Survival ratio

Another finding in my biostatistics project was heart and lungs organ surgery. The issue here is the survival ratio of the patient. It has been found out that there is greater heart operation than lung operation in the study.

Heart Operation survival ratio
There were sixty nine (69) heart organs received, found no complication after surgery or approximately seventy one percent (71.13%) while twenty eight (28) heart organ or twenty eight point eighty seven percent (28.87%) were found to have complications after surgery.

Lungs Operation survival ratio
There were thirty two (32) or seventy eight percent (78%) were found no complication after surgery. However there were 9 lungs organ has a complication after surgery or approximately twenty one point ninety five percent (21.95%).

Overall Implication
This means that two third (2/3)of the heart and lungs organ received after surgery have no complications, however, one third (1/3) had complication. This means that there is ratio of survival was two is to one (2:1).

Conclcusion Large: Equity Valuation using accounting numbers

Large sample

In this paper, the researcher empirically examined also the success of multiple regression model. The researcher investigated if the Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have no effect on the Equity Valuation from the informational data of LUVLE course website: https://luvle.lancs.ac.uk/Acfin/703.nfs. The researcher also used Frequency distribution and analysis of variance (ANOVA).

This paper examined the informational data of LUVLE course website: https://luvle.lancs.ac.uk/Acfin/703.nfs. The researcher discovered that the Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have significant effect on the Return on Equity from the informational data of LUVLE course website.

This means that other variables can affect the result of the equity valuation. The researcher suggested the additional parameters for the greater accuracy of the model.

Conclusion-small: Equity Valuation using accounting numbers

VII. CONCLUSION:
Small sample
In this paper, the researcher empirically examined the success of multiple regression model. The researcher investigated if the R&D to Total Assets, Intangibles to Total Assets, Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have no effect on the Equity Valuation from the informational data of LUVLE course website: https://luvle.lancs.ac.uk/Acfin/703.nfs. The researcher also used Frequency distribution and analysis of variance (ANOVA).
This paper examined the informational data of LUVLE course website: https://luvle.lancs.ac.uk/Acfin/703.nfs. The researcher discovered that the R&D to Total Assets, Intangibles to Total Assets, Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have an effect on the Equity Valuation from the informational data of LUVLE course website. This means that other variables can affect the flaws of the equity valuation. The researcher suggested the additional parameters for the greater accuracy of the model.

Graph 4: Equity Valuation using accounting numbers

The statistical informational data was from LUVLE course website: https://luvle.lancs.ac.uk/Acfin/703.nfs.
It can be gleaned in Graph 4 above, that there were seven Firms who makes a remarkable inflation in terms of ratio analysis. Firm 513 got the highest inflation of Assets turnover, Profit margin and Equity Multiplier which range almost 6000 and followed by Firm 129, range above 5000. Third Firm who got Assets turnover, Profit margin and Equity Multiplier is 671, then 702, 607, 94 and 478.
Summary of the graphs were scrutinized by the researcher. It was found out that only three Firms got the high inflation. These Firms were Firm 702, Firm 671 and Firm 478. This implies that the Firms are performing well in the stock market place and they have good inflation elasticity. It was notable that some companies were also trying their best to be on top such as Firm 513, Firm 129, Firm 607, Firm 624, and Firm 165. This can be seen in their inflation elasticity variables.
The Analysis of Variance was used to determine if the Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have an effect on the Equity Valuation. The null hypothesis is that the Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have no effect on the Equity Valuation.
Thus, all independent variables have no significant effect on the equity valuation: Ho: μ1 = μ2 = μ3= μ4, or Ho. We assume that there should be at most five percent chance of erroneously rejecting a true Ho. Thus we specify a level of significance of 0.05. We used F-distribution and an Analysis of variance (ANOVA)test , and next step is to define the rejection or critical region. The degree of freedom numerator value is 4 and the degree of freedom denominator value is 706. So with α = 0.05, the critical value of F in this analysis of variance test is F0.05 (4,706) = 2.73.
The final statistical decision is rejected the null hypothesis. Since computed F (F c) is greater than Tabulated F (Ft), Ho is rejected and thus, all the independent variables of this study have a significant effect on the equity valuation should be considered as acceptance of the alternative hypothesis. The result of the “Analysis of Variance” (ANOVA) shows that the computed F (21.875) is greater than the tabular values of F-statistics at 0.05 degree of freedom (2.37).
The Regression results are as follows: the unbiased estimator of the variance of the error in the multiple regression model is equal to 2740.285. There is small value of MSE denominator than the MSE numerator (59944.883) so the estimator is a good fit of the regression. Standard error of estimate is equal to 52.34773. Multiple coefficient of determination is .105. (R^2) and an adjusted multiple coefficient of determination is equal to .110 (R2) showed that the data produced a good predictions. This was stated because the adjusted R^2 is closer to the unadjusted R^2.

Large Sample Result: Equity Valuation using accounting numbers

Large sample
This section presents the analysis of the data on the study to find out if the Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have an effect on the Equity Valuation. The Multiple Regression model were used to examined the effect of the other values to the return on equity using LUVLE course website informational data
The descriptive statistics was also used in this study for the presentation purposes. Graphs 3 to 4 show the inflation of the elasticity of the variables.

The statistical informational data was from LUVLE course website: https://luvle/.lancs.ac.uk/Acfin/703.nfs.

From Graph 3, shows that there were 710 selected companies or firm. It is notable that there were 6 firms who got high income before extraordinary items, sales, and assets. Firm 702 got the highest inflation of income before extraordinary items, sales, and assets which range between 400000 to 450000 and followed by Firm 94, range near 40000. Third Firm who got income before extraordinary items, sales, and assets is 671, then 624,165 and 478.

Graph 2:Equity Valuation using accounting numbers

The statistical informational data was from LUVLE course website: https://luvle.lancs.ac.uk/Acfin/703.nfs.
From Graph 2 above, the researcher found out that there were two Firms who makes a remarkable inflation in terms of ratio analysis. The Firm 6 got the highest inflation of RTA, ITTA, Assets turnover, Profit margin and Equity Multiplier which range above 20 and followed by Firm 21, range almost 20. There were three Firms who were almost on range 10. The three Firms were 9, 12, and 24.
Summary of the graphs were scrutinized by the researcher. It was found out that only Firm 6 got high inflation. The Firm 21 got a high Analysis of ratio but slightly lows in terms of R & D. This implies that the only Firm 6 perform well in the stock market place and it has good inflation elasticity. It was notable that some Firms were also trying their best to be on top such as Firm 9, Firm 2, Firm 8, Firm 5, and Firm 4. This can be seen in their inflation elasticity variables.
The Analysis of Variance was used to determine if the R&D to Total Assets, Intangibles to Total Assets, Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have an effect on the Return on Equity. The null hypothesis is that the Assets turnover, Profit margin, Equity Multiplier, R&D to Total Assets, Intangibles to Total Assets, and Return on total assets have no effect on the Equity Valuation.
Thus, Ho: μ1 = μ2 = μ3= μ4= μ5= μ6, or Ho: All independent variables have no significant effect on the equity valuation. The alternative is that not all independent variables have a significant effect on the return on equity. Let’s assume that there should be at most five percent chance of erroneously rejecting a true Ho. Thus we specify a level of significance of 0.05. We used F-distribution and an Analysis of variance (ANOVA)test , and next step is to define the rejection or critical region. The dfnum value is k-1, or 6 and the dfden value is T-k, or 18. So with α = 0.05, the critical value of F in this analysis of variance test is F0.05 (6,18) = 2.66. The decision Rule is Reject Ho in favor of Ha if the value of the computed F is greater than the value of tabulated F. Otherwise, do not reject Ho. The next step is to compute the test statistic. Find the computed F, by dividing the mean sum of square of regression value by the mean sum of square of residual value. The final step now is to make the statistical decision. Since computed F (F c) is greater than Tabulated F (Ft), Ho is rejected and thus, not all the independent variables of this study have a significant effect on the return on equity. The result of the “Analysis of Variance” (ANOVA) shows that the computed F (13.656) is greater than the tabular values of F-statistics at 0.05 degree of freedom (2.66). Hence, Ho must be rejected.
The Regression results are as follows: the unbiased estimator of the variance of the error in the multiple regression model is equal to .010. There is small value of MSE denominator than the MSE numerator (.131) so the estimator is a good fit of the regression. Standard error of estimate is equal to .09777. Multiple coefficient of determination is .820 (R2) and an adjusted multiple coefficient of determination is equal to .760 (R2) showed that the data produced a good predictions. This was stated because the adjusted R^2 is closer to the unadjusted R^2.

Results: Equity Valuation using accounting numbers

VI. RESULTS
Small sample
This section presents the analysis of the data on the study to find out if the R&D to Total Assets, Intangibles to Total Assets, Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have an effect on the Equity Valuation. The Multiple Regression model was used to examine the effect of the other values to the equity valuation using LUVLE course website informational data. The descriptive statistics was also used in this study for the presentation purposes. Graphs 1 to 2 show the inflation of the elasticity of the variables.

The statistical informational data was from LUVLE course website: https://luve.lancs.ac.uk/Acfin/703.nfs.
Graph 1 above shows that out of 25, only 6 companies have a high R & D Range. The Firm number 6 got the highest inflation of R & D value which range between 150,000,000 and followed by Firm 5 and Firm 2, range near 50,000,000. The fourth firm who got R & D lower than 50,000,000was Firm 4. This implies that there were only few Firms who focused on the R & D. This means that many Firms were afraid to gamble or to take the risk in spending too much in Research and Development (R & D). This also shows that even though Firms belong to above £ 10,000 R & D, there were still other factors that can affect the interest of the investors.

Equity Valuation using accounting numbers

CHAPTER-3: METHODOLOGY
3-1 Research Methodology

The main objective of this chapter is to identify how the chosen research methodologies that will match the main objective of the dissertation question and how it will be achieved. Given the two types of research methodology between qualitative and quantitative research, this paper has to make a choice of one over the other or a combination of both. This paper admits that the quantitative research is carried out through obtaining primary data such as questionnaire, while qualitative research is a research that may make use of qualitative information through interviews and observations. Given the fact that the purpose of this research is to test the relationship of accounting numbers with the equity valuation, quantitative research is more of the required type for this kind of work. This does not however exclude the use of qualitative research as could be observed in content analysis from statements of various authors. Therefore, a quantitative approach is used here that it will enable the researcher to make use of the numerical and to explore the details of individual perceptions over phenomena. As complement to quantitative research this paper will also use qualitative methods particularly in expounding on the merits of one model over the other for it is in this context that qualitative approach assumes undeniable significance. This paper therefore first uses the quantitative approach by using both the small sample size and large sample size analysis to confirm or the deny the thesis of this dissertation the accounting numbers are good and reliable predictor for stock valuation and to under stand the finer points of the research qualitative research will be used.
Under the large sample size, the researcher used about more than 180 registered companies in UK by extracting relevant accounting numbers such as net income , total assets, etc in relation to total equity valuation of the firms . This paper has chosen multiple regressions to test of degree of relationships for the dependent variable to the independent variables
Under the small sample size approach, the researcher tried to reduce the large sample size from 180 to 50 until it reached 25. The basis for bringing the l80 sample size to 50 is to get only those that have R&D values of £10,000 or more. To get to 25, only those whose balance sheet date was December, 2004 were considered.
The paper will compare the results of analysis between the large and small sample size on whether there is basis to confirm the validity of finding. As a rule if the result of the first is confirmed by the second, then such would be a good sign of the characteristics of the models using accounting numbers.
Before the discussion of the application of the small size sample and large sample size, further discussion of the methodology will also be further discuss to enlighten the reader of this paper.

It's My Birthday!!!

Thank you very much for the warm support and greetings my friends. I am so thankful that I have friends like you. For those persons who greeted me in person Thank you and for those who greeted me through YM (sir_lander2004)thank you and ofcourse my blogging firends thank you for the warms greetings and support. I will treasure you all and may Allah bless us all.

Part 11: Academic achievement, (average test scores in reading and mathematic) and the number of suspensions for each school under study

Table 6: Correlation between total numbers of middle school suspensions
For school year 2006-2007 and academic achievement,
(Average test scores in reading and mathematic)
___________________________________________________
Correlation of total number of
suspensions to the
--------- ---Pearson Correlation
--------- ----- --------- Value of-----Level of Significance---Interpretation
___________________________________________________
Reading
-Below--------------- -0.07--------- 0.89--------- --------- Very Small ------ --------- --------- -------------- --------- ------ --------- ---- --------- --------- ---------Negative Correlation
-Average--------- -----0.18--------- 0.70--------- --------- Very Small
--------- --------- --- --------- --------- --------- --------- -----Negative Correlation
-Proficient-------------0.15--------- 0.74--------- --------- Very Small
--------- --------- --- --------- --------- --------- --------- -----Positive Correlation
-Advanced--------- ----0.11--------- 0.81--------- --------- Very Small
--------- --------- --- --------- --------- --------- --------- -----Positive Correlation
_____________________________________________________
Mathematics
-Below--------- -------0.23--------- 0.61--------- --------- Very Small
--------- --------- --------- --------- --------- --------- --------Negative Correlation
-Average--------- -----0.32--------- 0.49--------- --------- Moderately Small
--------- --------- --------- --------- --------- --------- --------Negative Correlation
-Proficient--------- --0.30--------- 0.51--------- ----------Moderately Small
--------- --------- --------- --------- --------- --------- --------Positive Correlation
-Advanced--------- ---0.19--------- 0.69--------- --------- Very Small
--------- --------- --------- --------- --------- --------- --------Positive Correlation
______________________________________________________
Table 6 shows that there is a significant relationship between total numbers of middle school suspensions for school year 2006-2007 and the academic achievement, (average test scores in reading and mathematic). It also shows that below and average have negative correlations. This means that students who were suspended were most probably along below or average category of scores because the values decreased as seen on the previous table. The result of the comparison shows a good implication for the disciplinary measures because it has a significant relationship.

Part 10: Academic achievement, (average test scores in reading and mathematic) and the number of suspensions for each school under study

Table 5: Correlation between total numbers of middle school suspensions
For school year 2005-2006 and academic achievement,
(Average test scores in reading and mathematic)

Correlation of total numbers of suspensions to the	Value of r	Level of Significance	Interpretation
Reading
Below	-0.27	0.52	Moderately Small Negative Correlation
Average	-0.11	0.79	Very Small Negative Correlation
Proficient	0.16	0.70	Very Small Positive Correlation
Advanced	0.21	0.62	Very Small Positive Correlation
Mathematics
Below	-0.08	0.85	Very Small Negative Correlation
Average	-0.41	0.31	Moderately Small Negative Correlation
Proficient	0.20	0.63	Very Small Positive Correlation
Advanced	0.22	0.60	Very Small Positive Correlation

Table 5 shows that all of the categories in reading and mathematics achievements average scores were slightly affected by the total number suspensions. It reveals that all academic achievements have significant relationship to the total number of suspensions. It also shows that below and average have negative correlations. This means that suspensions of students are good for the results of achievements of every school in Columbia.

Part 9: Academic achievement, (average test scores in reading and mathematic) and the number of suspensions for each school under study

Figure 4: Percentage distribution of average test scores in Mathematics
SY: 2006-2007

Figure 4 shows the percentage distribution of average test scores in Mathematics SY: 2006-2007. It can be gleaned in the figure that school number 3 has the highest scores in proficient and advanced in Mathematics scores. This implies that the school number 3 was consistent in the mathematics program. It is also notable that the trends of the other school were similar. Apparently, the other 7 schools may not have been aware of the accomplishments school number 3 which is obviously worth emulating.

Part 8: Academic achievement, (average test scores in reading and mathematic) and the number of suspensions for each school under study

Table 4: Percentage distribution of average test scores

In Mathematics SY: 2006-2007

School	Below	Average	Proficient	Advanced
1	46	47	7	0
2	40	48	12	0
3	11	31	44	14
5	39	45	16	0
6	39	42	17	2
7	36	48	14	2
8	41	44	15	0
Mean	36.00	43.57	17.86	2.57

Table 4 shows that among the 7 schools of the study, school number 3 has the highest percentage of students who belong to the category ‘proficient and advanced’ on average mathematics scores. This means that this school has more of creativeness and innovation introduced in the learning processes of the students.

Conversely, it is very noticeable that percentages of the numbers of students who are poor in mathematics were high as evidenced by 36.00% and 43.57%. This obviously requires the department of education to plan and develop a new curriculum apt for 8th Graders which would provide prime attention in the development of mathematical skills.