Monday, September 2, 2013

Proto Embryonic Stage Extract (PESE) and its known Benefits

Proto Embryonic Stage Extract is the name we have given to the component in Laminine™ that is deduced from partially the dealt (9 days) enriched hen eggs. PESE controls the most powerful and equilibrated compounding of not only aminoalkanoic acid, but also early known (and unknown) factors such as Fibroblast Growth Factor. We conceive aminoalkanoic acid deduced from such substances combined with the proteins that are capable to raise brain function since they are “exactly” engineered to abide the most complicated stage of birth of a living being, in the first place. This beginning is the most vital stage in our developing just like the deducting is the most complex occasion in fleeing an airplane, or the basis in structure that is most of all important function in the life of a structure.
embryonic-egg-extract-laminine
The wellness gains of the hen egg have been known for hundred years. Recently, further investigating of the mechanism of the growth of an embryo in an egg during brooding exposed the scientific combining weight of the “event of life”. The authority of the nutrients use able to the embryo at the second stage of developing stage has constantly been accepted to be eminent, but it was only recently that the chemical substance had structure of the original egg units for which critical stages were found. During this stage Oligoplites with small relative molecular mass were named. These short chains of aminoalkanoic acid are capable to cross the substance barrier without breaking down or altering the ratios and proportionalities. Amides are far more powerful than other organic compound, requiring only little amounts to develop a profound effect.


Additionally, the consumption of the Fibroblast Growth Factor (FGF) (present in PESE) by the fertilised egg sharply raises and the FGF delivered has been isolated through a secured process precisely at the right stage of brooding, extracted and freeze down dried to bring the “miracle of aliveness” benefits to human being.

Drawing out PESE before the amides and FGF are “used up” to build organs and bones, allows us to provide this constructing, fixing, upkeep mechanism of absolutely equilibrated aminoalkanoic acid, amides and proteins to humans. While almost unknown in the United States until very lately, PESE has been commercialised successfully as a nutritional accessory in Scandinavia for many ages.

Sunday, September 1, 2013

Part I: FDT, Measures of Central Position (Mean) and Measures of Variability (SD)

Frequency Distribution Table

There are many ways of summarizing the data you gathered one, in particular, is the Frequency Distribution Table (FDT). The frequency distribution is a useful summary of most kinds of data. It sorts observations into categories and describes how often observations fall into each category. In simple terms, frequency distribution refers to the tabular arrangement of data by classes or categories together with their corresponding class frequencies. Data presented in the form of a frequency distribution are called grouped data.



In constructing a frequency distribution table, considerations must be given to the number of classes (class size) to be used and the class intervals to be employed. Below is a presentation of a technique in constructing a frequency distribution. 

Step 1: Get the range (R) by subtracting the highest score from the lowest score


  • example: HS=119; LS=35 ---> R=HS-LS --->R=119-35=84 --->R=84
Step 2: Determine the number of classes (k)  using Sturges’ approximation which is given by: 
           k = 1+3.322 logn (n is the number of observations), which is rounded off to the next higher integer

  • example: n=50
  • k=1+3.322 logn
  • k=1+3.322 log50
  • k=1+3.322 (1.70)
  • k=1+5.6474
  • k=6.6474 ≈ 7
Step 3: Find the width of the class interval (c) using this formula: 
     


  • example: c=84/7=12
After computing the class size (k) and class interval (Ci) you can now tally the frequencies (fi) for each class and compute for class marks (x), and class boundaries (Cb).


CLASS FREQUENCY. This refers to the number of observations belonging to a class interval, or the number of items within a category (Pagoso, 1986). To illustrate, consider the following scores of ten pupils in a competitive test: 15, 15, 15, 18, 18, 19, 22, 22, 24, 24.

                                  Scores                 frequency
                                     15                        3
                                     18                        2
                                     19                        1
                                     22                        2
                                     24                        2



            Class frequency can be arranged in different forms. This can be done using cumulative frequency and relative frequency.
  • CUMULATIVE FREQUENCY. This is a tabular arrangement of data by class intervals whose frequencies are cumulated. There are two kinds of cumulative frequency (cf). These are: “less than” cumulative frequency (<cf) whose sum of frequencies for each class interval is less than the upper class boundary (Cb) of the interval they correspond to.



Example:               Ci                 f          <cf           Cb
                         15-16              3             3       14.5-16.5
                         17- 18             2             5       16.5-18.5
                         19-20              1             6       18.5-20.5
                         21-22              2             8       20.5-22.5
                         23-24              2             10     22.5-24.5

Each number in <cf column is interpreted as: three items are less than 16.5; 5 are less than 18.5 and so on. 
On the other hand, the “greater than” cumulative frequency (>cf) whose sum of frequencies for each class interval is greater than the lower class boundary of the interval they correspond to.

Example:               Ci                 f          >cf           Cb
                         15-16              3             10     14.5-16.5
                         17- 18             2             7       16.5-18.5
                         19-20              1             5       18.5-20.5
                         21-22              2             4       20.5-22.5
                         23-24              2             2       22.5-24.5

Each number in >cf column is interpreted as: 10 items are greater than 14.5; 7 items are greater than 16.5 and so on…

  • RELATIVE FREQUENCY. This is a tabular arrangement of the data showing the proportion in percent of each frequency to the total frequency. This can be obtained by dividing the class frequency by the total frequency.



Example:               Ci                 f          rf (%)
                         15-16              3            30
                         17- 18             2            20
                         19-20              1            10
                         21-22              2            20
                         23-24              2            20

            Thus, if we have a class frequency of 3, the relative frequency is 3/10 or 30%




CLASS MARK. This can be obtained by adding the lower limit and upper limit and dividing the resulting sum by 2. Example in the interval 75-79, the lower limit is 75 and the upper limit is 79 gives us the average of 77, thus: x=(lower limit + upper limit) / 2 = (75+79)/2 = 154/2 =77


CLASS BOUNDARY. This refer to the true limits of the distribution, where lower class boundary [Li] is computed by subtracting ½ unit from the lower class limit while the upper class boundary [Ui] is obtained by adding ½ unit to the upper class limit. To show this concept let’s use the interval 75-79 again. In this interval we know that the lower limit is 75 and the upper limit is 79. To get the lower boundary and upper boundary we simply:
  • Lower boundary = 75-0.5=74.5
  • Upper boundary = 79+0.5=79.5
Below is an example of frequency distribution showing frequency, class marks (mid-points) and class boundaries:



Class Interval
f
c (class mark)
Class Boundaries
<cf
>cf
75-79
80-84
85-89
2
14
14
77
82
87
74.5-79.5
79.5-84.5
84.5-89.5
2
16
30
30
28
14

Measure of Central Position: Mean (grouped data)

To find a set of quantitative data, it is indeed necessary to define numerical measures that describe essential characteristics of the data. Further, any measure indicating the center of a set of data, arranged in order of magnitude, is the measure of central position or measure of central tendency. The most commonly used measures of central position are the mean, median, and mode.

Mean
Observe the following achievement scores of pupils in mathematics: 18, 19, 20, 21, 22, 23, 24, 25, 26 and 75. If you add all the score divided by the number of pupils, the mean of all items is 27.3. This figure is no longer a representative value since most scores is less than 27.3 except for the pupil that obtained the score of 75. The example gives one property of mean that is "mean is strongly influenced by extreme value."

For grouped data, the mean can be computed using the long method and the coded deviation method (short method).  
For long method, we use this equation:
, where f is the frequency, x is the class mark, and N is the total frequency or total number of observation or cases.
Example: 


Class Interval
f
x
fx
118 – 126
127 – 135
136 – 144
145 – 153
154 – 162
163 – 171
172 – 180
3
5
9
12
5
4
2
122
131
140
149
158
167
176
366
655
1260
1788
790
688
352

N: 40

Σfx: 5,879

For the coded deviation method, the original observations are converted to coded deviations (d’). Here, you choose for an assumed mean . In choosing for assumed mean, any reasonable value in the distribution will do but generally the highest frequency is taken. We use this equation, 
, whereis the assumed mean,is the sum of the differences of frequency and unit coded deviation, N is the total number of observations, i is the class interval. To understand deeply below is an example:

Class Interval
x
f
d’
fd’
118 – 126
122
3
- 3
- 9
127 – 135
131
5
- 2
- 10
136 – 144
140
9
- 1
- 9
145 – 153
149
12
0
0
154 – 162
158
5
+1
+5
163 – 171
167
4
+2
+8
172 – 180
176
2
+3
+6


  N=40

Σ: -9


Tuesday, August 27, 2013

Part II: Measures of Central Position

Measure of Central Position (grouped data)

To find a set of quantitative data, it is indeed necessary to define numerical measures that describe essential characteristics of the data. Further, any measure indicating the center of a set of data, arranged in order of magnitude, is the measure of central position or measure of central tendency. The most commonly used measures of central position are the mean, median, and mode.

Mean
Observe the following achievement scores of pupils in mathematics: 18, 19, 20, 21, 22, 23, 24, 25, 26 and 75. If you add all the score divided by the number of pupils, the mean of all items is 27.3. This figure is no longer a representative value since most scores is less than 27.3 except for the pupil that obtained the score of 75. The example gives one property of mean that is "mean is strongly influenced by extreme value."

For grouped data, the mean can be computed using the long method and the coded deviation method (short method).  However, for this discussion we will focus on getting the mean using the coded deviation.

Getting the mean using the coded deviation method, the original observations are converted to coded deviations (d’). Here, you choose for an assumed mean . In choosing for assumed mean, any reasonable value in the distribution will do but generally the highest frequency is taken. We use this equation, 
, whereis the assumed mean,is the sum of the differences of frequency and unit coded deviation, N is the total number of observations, is the class interval. To illustrate this see the example below:

Class Interval
x
f
d’
fd’
118 – 126
122
3
- 3
- 9
127 – 135
131
5
- 2
- 10
136 – 144
140
9
- 1
- 9
145 – 153
149
12
0
0
154 – 162
158
5
+1
+5
163 – 171
167
4
+2
+8
172 – 180
176
2
+3
+6


  N=40

Σfd': -9


Median 

Another measure of central position is Median. Observe the following distribution: 

     a). 2, 3, 8, 10, 16, 17, 18
     b). 2, 3, 8, 10, 16, 17 

What is the mid-value of a and b? If your answer is 10 for a and 9 for b , you are correct. The unit 10  and 9 is the median in the distribution for set a and b. This example brings us the description that median is the middle measurement/item/value in a set of measurement arranged in an increasing or decreasing order. For set a, median is easily identified since the set is odd while in set b which is even, there were two middle values (8 & 10). To get the median for set b, add these two values then divide it by 2. Thus, (8+10)÷2=9.

Moreover, the median is a positional measure. The values of the individual items in the distribution do not affect the median. Example, in this distribution: 2, 4, 5, 6, 7, 15, 37… 6 is the median despite of two deviant values (15 and 37). This means that median is not affected by extreme values. Because of this, median can be considered an appropriate measure if you don’t want extreme values to influence the average.


For grouped data, that is when data are given in frequency distribution form, we first determine in what class interval we can find the N/2th case. This means that we have to ascertain the value which divides the distribution into equal parts. To understand deeply, the table below presents the frequency distribution of 38 scores, where half of the scores (that is N/2=38/2=19) lies above the median (in ascending order of distribution this can be identified below the median with larger values in the interval, in this case it is 25) and half below (in ascending order of distribution this can be identified above the median with smaller values in the interval in this case it is 23).



SCORES
F
<cf
>cf
40-44
45-49
50-54
55-59
1
1
4
7
1
2
6
13
38
37
36
32
60-64
10
23
25
65-69
70-74
75-79
80-84
9
3
1
2
32
35
36
38
15
6
3
2

          N=38



 
If median is taken from above, that is considering the >cf, the N/2 which is equal to 19, it would fall between 60-64. This can be done by counting the frequencies upward from the bottom and finding where the N/2 (19) item is found. In our example, N/2 (19) lies in the interval 60-64, whose boundaries are 59.5 and 64.5, thus the following equation can be used:
where U refers to the upper boundary where the median lies; N/2 is the half of the total number of observations; Fub is the sum of all frequencies above the upper boundary; fm is the frequency of the median class; i is the length of the interval. Thus, the median is:




If we take median from below, we consider the <cf that is counting the frequencies from above to bottom. We observe the same procedure as in the median from above, only we use this equation:

 where L is the lower boundary of the class interval; N/2 is the half of the total number of observations; Flb is the sum of all frequencies below the lower boundary; fm is the frequency of the median class; i is the length of the interval. Thus the median from below is:
Mode

The mode on the other hand is the simplest measure of central position, simplest in a sense that it can be easily identified. In an ungrouped data the item that occurs most often is the mode. Which is the mode in this set of scores: 17, 18, 18, 20, 21? The score often occurring in the set is 18, so 18 is the mode. A distribution with one mode is known to be unimodal while a distribution with two or more modes is said to be multimodal. Below are samples showing unimodes and multi-modes:


Sample 1                  Sample 2                  Sample 3
    21                                      21                           16       
    20                                      21                          14
    19                                       19                          16
    19                                       19                          15
    19                                        17                         14
    17                                        16                         15
    15                                        15                         17

Sample 1 is an example of unimode where 19 is the item occurring most often in the distribution. Sample 2 and 3 are examples of multimodes, where there are more than one item occurring frequently in the distribution. What are the modes in sample 2 and 3? For sample 2 the modes are 21 and 19 while in sample 3, the modes are 16, 15 and 14.
When data are grouped, the mode is defined as the midpoint of the interval containing the largest number of cases. Moreover, the modal value can be also computed if data are grouped, thus we use this equation:
where Lmo refers to lower boundary of the modal class (usually obtained in the class interval with the highest frequency); fmo is the frequency of the modal class; f1 is the frequency above the modal class; f2 is the frequency below the modal class; and i refers to the length of class interval.

Let’s find the mode using same data.
SCORES
F
Class Boundary
40-44
45-49
50-54
55-59
1
1
4
7
39.5-44.5
44.5-49.5
49.5-54.5
54.5-59.5
60-64
10
59.5-64.5
65-69
70-74
75-79
80-84
9
3
1
2
64.5-69.5
69.5-74.5
74.5-79.5
79.5-84.5

          N=38