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1.0 Introduction
The main idea of smart statistic is to enable students to collect and process data
in a systematic way and display the information meaningfully. From the task given, we
can see the ability of the students to interpret and make inferences from the visual
representation prepared and how they would be able or not to resource evidence to
substantiate inferences.
Moreover, the purpose of smart statistic is to enable students to represent data
using suitable visual representations. It is also to find the ability to organize information
clearly and neatly by using the suitable representations and provide a reason for each
choice.
Then, to enable them to relate the information with very clear report based solely
on the visual representation. The report must covers all aspect of the data displayed on
the visual representation. They also could appropriate prediction based clearly on
collected data.
It incriminate statistic. Besides that, statistic is a mathematical science pertaining
to the collection, analysis, interpretation or explanation, and presentation of data.
Statisticians improve the quality of data with the design of experiments and survey
sampling. Other than that, statistics provides tools for prediction and forecasting using
data and statistical models. Statistics is applicable to a wide variety of academic
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disciplines, including natural and social sciences, government, and business. Statistical
methods can be used to summarize or describe a collection of data, this is called
descriptive statistics. This is useful in research, when communicating the results of our
obesity survey.
Obesity means having too much body fat. It is different from being overweight,
which means weighing too much. The weight may come from muscle, bone, fat or body
water. Both terms mean that a persons weight is greater than whats considered
healthy for his or her height.
Obesity occurs over time when you eat more calories than you use. The balance
between calories-in and calories-out differs for each person. Factors that might tip the
balance include your genetic makeup, overeating, eating high-fat foods and not being
physically active.
Being obese increases your risk of diabetes, heart disease, stroke, arthritis and
some cancers. If you are obese, losing even 5 to 10 percent of your weight can delay or
prevent some of these diseases.
The terms overweight and obesity refer to a persons overall body weight and
where the extra weight comes from. Overweight is having extra body weight from
muscle, bone, fat and/or water. Obesity is having a high amount of extra body. The
most useful measure of overweight and obesity is the body mass index (BMI). BMI is
based on height and weight and is used for adults, children, and teens.
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Being overweight or obese puts you at risk for many diseases and conditions.
The more body fat that you carry around and the more you weigh, the more likely you
are to develop heart diseases, high blood pressure, type 2 diabetes, gallstones,
breathing problems, and certain cancers.
A persons weight is a result of many factors. These factors include environment,
family history and genetics, metabolism (the way your body changes food and oxygen
into energy), behavior or habits, and other factors.
Certain things, like family history, cant be changed. However, other things-like a
persons lifestyle habits-can be changed. You can help prevent or treat overweight and
obesity if you:
Follow a healthful diet, while keeping your calorie needs in mind.
Are physically active
Limit the time you spend being physically inactive
Weight loss medicines and surgery also are options for some people who need
to lose weight if lifestyle changes dont work.
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2.0 Data collection
In this task, we conduct a survey that we find it more suitable for our tasks than
using other ways such as research or observation. This has been the standard method
of data collection of centuries.
We did the task on Wednesday, 25th August 2010 which is a weekday and not a
public or school holiday. We did the survey between 2.30 p.m to 3.30 p.m of that day.
We did it on that time because it was the time after the lunch hour, and all the students
will be in the class due to their lecture time. So it makes the survey much easier as we
can complete the survey items at this time and ready for the calculation.
As we have 4 persons in a group, two of us make survey in 3 PPISMP
(BC/PJ/PS_2), (BT/PJ/PS), (MT/BI/BC) and another 2 of us make survey in 1 PPISMP
(RBT_1) and (TESL_1). We only choose 10 female students from each of the class to
full fill minimum sample size of 50 respondents. For this survey, we have been using the
table chart to tabulate the collected data and also to count PPISMP female students
BMI result before we nicely reorganized it in frequency distribution table to make a
visual representation.
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2.1 Tabulation of data
We tabulate the data using height and weight of the students. So that its easier to
calculate BMI of the students.
Height and weight of PPISMP students in IPGKTI
Student Height (m) Weight (kg)
1 1.65 52
2 1.68 45
3 1.65 48
4 1.64 45
5 1.58 49
6 1.63 65
7 1.60 56
8 1.63 60
9 1.61 38
10 1.68 57
11 1.60 64
12 1.67 51
13 1.58 45
14 1.58 54
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15 1.64 45
16 1.56 48
17 1.64 45
18 1.53 38
19 1.63 49
20 1.55 42
21 1.65 45
22 1.55 45
23 1.65 59
24 1.54 45
25 1.55 42
26 1.57 45
27 1.70 55
28 1.60 58
29 1.68 50
30 1.63 43
31 1.65 64
32 1.56 60
33 1.55 47
34 1.60 48
35 1.49 55
36 1.58 40
37 1.68 55
38 1.59 45
39 1.50 42
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40 1.61 46
41 1.62 55
42 1.55 40
43 1.64 63
44 1.62 65
45 1.58 38
46 1.52 52
47 1.56 60
48 1.53 45
49 1.68 49
50 1.54 42
2.2 Recording and organizing data
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The collected data was recorded using table chart (as given in 2.0, page 7). After that
all the data was organized in a frequency distribution table for the weights obtained
using a suitable class interval.
Weight (kg) Frequency
36 40 5
41 45 15
46 50 9
51 55 9
56 60 7
61 65 5
Table 1 shows the frequency table for the weights
3.0 Representation of data using visual illustrations
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Since the data is categorical, the visual representations chosen are bar chart,
histogram, frequency polygon, ogive and pictogram. The visual illustrations are shown
below.
3.1 Bar chart to represent the data
This bar chart is drawn using the frequency distribution table (table 1)
The bar chart is chosen because a bar graph displays discrete data in separate
columns. A double bar graph can be used to compare two data sets. Categories are
considered unordered and can be rearranged alphabetically, by size, etc. Bar graphs
are good for showing how data change over time.
The advantages of bar chart are its visually strong. The bar chart shows each
data category in a frequency distribution and clearly displays relative numbers or
proportions of multiple categories. It is very suitable to summarize a large data set in
visual form. So, we already can see that this chart can easily clarify trends better than
do tables.
Besides that, bar chart can estimate key values at a glance. It also manages the
permit with a visual check of the accuracy and reasonableness of calculations. So, that
it will be more easily understood due to widespread use in business and the media.
Moreover, bar chart shows beautiful visual representation and neatly constructed
diagrams or charts are more attractive than simple figures. We can create it with
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different colors or sizes of bar. So, the result is when the comparison is made totally
become easier between two or three data sets and it will save time of the user to make
quick comparison of large data. Bar graphs also shows a record in column form so over
a period of time comparisons of the recorded information can be clear to saw.
Although, bar charts have strengths but they also have some weaknesses. First
of all, when the graph categories can be reordered to emphasize certain effects. The
bar graph use only with discrete data. It is also can be easily manipulated to yield false
impressions and sometimes it is fail to reveal key assumptions, causes, effects, or
patterns. Lastly, the bar chart requires additional written or verbal explanation and also
would be inadequate to describe the attribute, behavior, or condition of interest.
But for the some reason, we manage to choose this bar graph to represent our
collected data because it is very suitable for our task as it is a quantitative data.
3.2 Frequency polygon to represent the data
To draw a frequency polygon, we have to use midpoint of the x-value (class interval).
We can calculate midpoint using this method :
Class interval Midpoint
31 35 x + x
2
= 31+35
2
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33
36 40 38
Therefore, the frequency distribution table with midpoint as follows:
Class interval frequency Midpoint
31 35 0 33
36 40 5 38
41 45 15 43
46 50 9 48
51 55 9 53
56 60 7 58
61 65 5 63
66 70 0 68
FIGURE 1
Frequency polygon is the preferred way to graph the frequency distribution of
ungrouped (raw) interval data. A frequency polygon can be made from a line graph by
shading in the area beneath the graph. They represent the frequency of each class of
data points as a line connecting the midpoints of the bars of a histogram. The two end
points of a frequency polygon always lie on the x-axis.
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The normal (bell) curve is the most common type of frequency polygon. A
frequency polygon shows approximately the smooth curve that would describe a
frequency distribution if the class intervals were made as small as possible and the
number of observations was very large. Frequency polygon can describe the behavior
of the same interval variable under different circumstances, as in before-after situations,
if superimposed on each other.
Relative frequencies of class intervals can also be shown in a frequency
polygon. In this chart, the frequency of each class is indicated by points or dots drawn
at the midpoints of each class interval. Those points are then connected by straight
lines. The frequency polygon shown in Figure 1 uses points rather than the bars which
may find in a frequency histogram.
To use bar charts or histograms depends on the data. For example, we may have
qualitative datanumerical information about categories that vary significantly in kind.
For instance, gender (male or female), types of automobile owned (motorcycle, car,
bicycle, van, and lorry), and religious affiliations (Chinese, Hindu, Malays, and others)
are all qualitative data. On the other hand, quantitative data can be measured in
amounts: age in years, annual salaries, and inches of rainfall.
Typically, qualitative data are better displayed in bar charts, quantitative data in
histograms. Unlike histograms, frequency polygons can be superimposed so as to
compare several frequency distributions.
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3.3 Histogram to represent the data
To draw a histogram, we have to use upper boundary to represent x-value (class
interval) with the frequency. We can calculate upper boundary using this method :
Example :-
Therefore,
this is the frequency distribution table with the upper boundary :-
Class interval Frequency Upper boundery
31 35 0 35.5
Class interval Upper boundary
31 35 35+36
2
= 35.5
36 40 40+41
2
= 40.5
41 - 45 = 45.5
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36 40 5 40.5
41 45 15 45.5
46 50 9 50.5
51 55 9 55.5
56 60 7 60.5
61 65 5 65.5
66 70 0 70.5
The Histogram was first implemented by Kaoru Isikawa, one of Japans most
renowned experts on quality improvement. Isikawa spent his life in trying to improve
quality in Japan.His major contributions to quality improvement are known as the basic
seven tools of quality. Included in his basic seven tools of quality is the Histogram.
A Histogram is a variation of a bar chart in which data values are grouped
together and put into different classes. A histogram is a diagram which represents the
class interval and frequency in the form of a rectangle. There will be as many adjoining
rectangles as there are class intervals. This grouping allows us to see how frequently
data in each class occur in the data set. Higher bars represent more data values in a
class and the lower bars represent fewer data values in a class.
The histogram of the frequency distribution can be converted to a probability
distribution by dividing the tally in each group by the total number of data points to give
the relative frequency. The shape of the distribution conveys important information such
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as the probability distribution of the data. In cases in which the distribution is known, a
histogram that does not fit the distribution may provide clues about a process and
measurement problem.
A Histogram can be used to display large amounts of data values in a relatively
simple chart form, to tell relative frequency of occurrence, to easily see the distribution
of the data, to see if there is variation in the data, and to make future predictions based
on the data. The class intervals are made continuous and then the histogram is
constructed. The horizontal scale and vertical scale need not be the same.
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3.4 Ogive to represent the data
Ogive can be sketched using the cumulative frequency and upper boundary. We can
calculate the cumulative frequency using this method :-
Cumulative frequency = Frequency of the class + cumulative frequency of
the class before.
Therefore,
Class
Interval Frequency
Cumulative
frequency
Upper
boundary
31 35 0 0 35.5
36 40 5 5 40.5
41 45 15 20 45.5
46 50 9 29 50.5
51 55 9 38 55.5
56 60 7 45 60.5
61 65 5 50 65.5
66 70 0 0 70.5
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An ogive is a curved shape, figure or feature. In statistics, an ogive is a graph
showing the curve of a cumulative distribution function. Data may be expressed using a
single line. An ogive(a cumulative line graph) is the best used when you want to display
the total at any given time. The relative slopes from point to point will indicate greater or
lesser increases. For example, a steeper slope means a greater increase than a more
gradual slope.
However, an ogive is not the ideal graphic for showing comparisons between
categories because it simply combines the values in each category and thus indicates
an accumulation, a growing or lessening total. The choice of graphic display depends
on what information is important for your purposes, such as percentages (parts of the
whole), running total, comparison of categories and so on.
4.5 Pictograph to represent the data
Weight (kg) Frequency, f
31 35 0
36 40 5
41 45 15
46 50 9
51 55 9
56 60 7
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61 65 5
66 70 0
Pictograph can be drawn using any picture but must represent the number of the
frequency.
Class interval frequency Pictorial representation
31 35 0
36 40 5
41 45 15
46 50 9
51 55 9
56 60 7
61 65 5
66 70 0
Represent 2 pupils
Represent 1 pupil
In graph theory , a pictograph is a graph that shows numerical information by
using picture symbols oricons to represent data sets. A pictograph uses an icon to
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represent a quantity of data values in order to decrease the size of the graph. A key
must be used to explain the icon. This type of chart used to represent comparative
sizes, scales or areas. As with every chart, the pictograph needs a title to describe what
is being presented and how the data are classified as well as the time period and the
source of the data.
There are many advantages and disadvantages of the pictograph. The advantage
is that from pictograph we can easily read it. Another advantage is pictograph is visually
appealing. Pictograph also may help to handle large data sets easily using keyed icons.
The disadvantages are, pictograph is hard to quantify partial icons, and the icons must
be of consistent size. Moreover, it is best for only 2-6 categories. Pictograph is also very
simplistic.
4.0 Measure of Central Tendency
Mean, mode and median are three measures of central tendency which indicates the
data seems to cluster. However, the values of these measures may differ greatly. Thus,
it is necessary to choose that reflects the central value of the data. We
use statistics such as the mean, median and mode to obtain information about
a population from oursample set of observed values.
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4.1 MODE
Mode is only used to represent a set of data containing a large number of values which
take only some specific values and many repeated values. The modeof a set of data
values is the value(s) that occurs most often. Mode is confusing when the data has
more than one mode. There are two methods to find the mode of the 50 female
trainees.
Method 1 :
Class interval Frequency
31 35 0
36 40 5
41 45 15
46 50 9
51 55 9
56 60 7
61 65 5
66 70 0
Therefore,
The class interval with the highest frequency is 41 45
The modal class is 41 45.
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Method 2 :
We also can find the mode using the histogram.
The value lies between upper boundaries of 40.5. The upper boundary refers to the
class interval of41 45.
4.2 MEAN
Mean is considered suitable measure of central tendency for representing a set of data
whose values are quite evenly distributed, meaning there is no extreme value in the set
of data. The mean (or average) of a set of data values is the sum of all of the data
values divided by the number of data values. The mean of a set of data and the mean
from a frequency distribution table will be different.
a) Calculating the mean of a set of data
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b) Calculating the mean from a frequency table
Finding the mean for the weight of the 50 female trainees.
Weight (kg) Frequency
31 35 0
36 40 5
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41 45 15
46 50 9
51 55 9
56 60 7
61 65 5
Solution :-
Weight (kg) Midpoint,x Frequency, fCumulative
frequencyfx
31 - 35 33 0 0 0
36 - 40 38 5 5 190
41 - 45 43 15 20 64546 - 50 48 9 29 432
51 - 55 53 9 38 477
56 - 60 58 7 45 406
61 - 65 63 5 50 315
66 - 70 68 0 50 0
f= 50 fx= 2465
Mean,x = fxf
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= 246550
= 49.3
4.3 MEDIAN
Median is used when there are extreme values because median eliminates the effects
of extreme values in a set of data. The medianof a set of data values is the middle
value of the data set when it has been arranged in ascending order. That is, from the
smallest value to the highest value.
Finding the median of the
Weight (kg) Frequency
31 35 0
36 40 5
41 45 15
46 50 9
51 55 9
56 60 7
61 65 5
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Solution :
Total number of observations, N = 50
Therefore, the median, m
= 50
2
= 25th observation
From the cumulative frequency table,
The class that contains the 25th observation is 46 50
Weight (kg) Frequency Cumulative frequency
31 35 0 0
36 40 5 5
41 45 15 20
46 50 9 29
51 55 9 38
56 60 7 45
61 65 5 50
Median class = 46 - 50
The lower boundary of the median class, L = 45.5
Cumulative frequency before the median class, F = 20
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Frequency of the median class, fm = 29
Width of the median class (class size), C = 5
By using the formula, m = L + N2-Ffm C
= 45.5 + 502-2029 5
= 45.5 + 0.8621
= 46.36
Therefore, the median is 46.36
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4.4 Standard deviation
Standard deviation is a measure of the dispersion of a set of data from its mean. The
more spread apart the data, the higher the deviation. Standard deviation is
calculated as the square root of variance. There are three different methods to calculate
the standard deviation. The methods as follows :-
Method 1 : by calculator
x x fx fx
50 49.3 2465 124365 7.54
Method 2 : by formula
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Mean, x = fxf
= 246550
= 49.3
Standard deviation, = fxf ( x )
= 12436550 - (49.3)
= 56.81
= 7.54 kg
Method 3 : by using excel
Weight
(kg)
Midpoint,
x
Frequency,
F x fx fx
31 - 35 33 0 1089 0 0
36 - 40 38 5 1444 190 7220
41 - 45 43 15 1849 645 27735
46 - 50 48 9 2304 432 20736
51 - 55 53 9 2809 477 25281
56 - 60 58 7 3364 406 23548
61 - 65 63 5 3969 315 19845
f= 50 fx= 2465 fx= 124365
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Key in 50 raw data
Choose INSERT FUNCTION and then choose FUNCTION STATISTICAL
CATEGORY and then click STDEV and highlight the 50 data.
Standard deviation, = 7.54 kg
5.0 Body Mass Index (BMI)
The body mass index (BMI) gives an indication of the physical state of a person as
being underweight, normal, overweight or obese. BMI can be calculated by using the
following formula:
BMI = WEIGHT (kg)HEIGHT m X HEIGHT (m)
The table below shows the BMI and the corresponding physical state of a person:
BMI CATEGORY
Below 18.5 Underweight
18.5 24.9 Normal
25 29.9 Overweight
30 and above Obese
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Using this as a formula, we calculate the BMI of the respondents according to their
height and weight. Then, we categorize their category with the calculation of the BMI,
which we did earlier.
5.1 Tabulation of data
Student Height (m) Weight (kg) BMI
1 1.65 52 19.10
2 1.68 45 15.94
3 1.65 48 17.63
4 1.64 45 16.73
5 1.58 49 19.63
6 1.63 65 24.46
7 1.60 56 21.88
8 1.63 60 22.58
9 1.61 38 14.66
10 1.68 57 20.20
11 1.60 64 25.00
12 1.67 51 18.29
13 1.58 45 18.03
14 1.58 54 21.63
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15 1.64 45 16.73
16 1.56 48 16.72
17 1.64 45 16.73
18 1.53 38 16.23
19 1.63 49 18.44
20 1.55 42 17.48
21 1.65 45 16.53
22 1.55 45 18.73
23 1.65 59 21.67
24 1.54 45 19.97
25 1.55 42 17.48
26 1.57 45 18.26
27 1.70 55 19.03
28 1.60 58 22.66
29 1.68 50 17.72
30 1.63 43 16.18
31 1.65 64 23.51
32 1.56 60 24.65
33 1.55 47 19.56
34 1.60 48 18.75
35 1.49 55 24.77
36 1.58 40 16.02
37 1.68 55 19.49
38 1.59 45 17.80
39 1.50 42 18.67
40 1.61 46 17.75
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41 1.62 55 20.96
42 1.55 40 16.65
43 1.64 63 23.42
44 1.62 65 24.77
45 1.58 38 15.22
46 1.52 52 22.51
47 1.56 60 24.65
48 1.53 45 19.22
49 1.68 49 17.36
50 1.54 42 17.71
The following is the frequency distribution of the BMI category among PPISMP female
trainees of IPG KTI:
Category Frequency
Underweight 22
Normal 27
Overweight 1
Obese 0
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5.2 CALCULATION OF PERCENTAGE OF STUDENTS
To find the percentage of the students, we have to use this method :
FREQUENCYTOTAL FREQUENCY X 100%
Underweight :
50
22
x 100%
= 44%
Normal :
50
27
x 100 %
= 54 %
Overweight :
50
1
x 100 %
= 2 %
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Obese :
50
0
x 100 %
= 0 %
5.3 BAR CHART
The following is the graph bar of the condition of BMI of 50 IPG KTI Trainees:
Based on the bar chart in Diagram 1, the total number of female students
who are underweight is 22 (44%). 54% of the female trainees are in normal
condition. This is the highest frequency of the data, which are 27 female trainees.
However, there is no one in obese state. Another two female trainees are in
overweight condition. The percentage present by the overweight condition is 2%.
From this bar chart, we able to find the second highest frequency is underweight
condition. 22 female trainees from 50 trainees are considered underweight
condition due to their height and weight.
5.4 PIE CHART
The following is a pie chart of the percentage of students condition
Diagram 1
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On the other hand, pie chart is one of the most widely criticized charts and many
statisticians recommend to avoid its use altogether, pointing out in particular that it is
difficult to compare different sections of a given pie chart, or to compare data across
different pie charts. One reason for this is that it is more difficult for comparisons to be
made between the sizes of items in a chart when area is used instead of length.
Other weakness of pie charts, for illustrating the relative proportions between
similar-sized slices can be an advantage when you are really trying to communicate
something much simpler. It's often better to hide irrelevant information in order to more
clearly communicate a key idea. However, if the goal is to compare a given category (a
slice of the pie) with the total (the whole pie) in a single chart and the multiple is close to
25% or 50%, then a pie chart works better than a bar graph. Pie charts have
weaknesses but they also have much strength. Put them back in your bag of tools and
pull them out when appropriate.
6.0 Choosing suitable representation of data
We have drawn many methods of data representation. They are bar chart,
histogram, frequency polygon, pie chart, pictograph, and ogive. Choosing the most
suitable method of data representation is important so that the data can be easily
interpreted and attractively represented. Let us examine some of the advantages and
disadvantages of each method in a table form.
Method Advantages Disadvantages
Visually strong Graph categories
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Bar graph Can easily compare
two or three data
sets
can be reordered to
emphasize certain
effects
Use only with
discrete data
Histogram
Visually strong
Can compare to
normal curve
Usually vertical axis
is a frequency count
of items falling into
each category
Cannot read exact
values because data
is grouped into
categories
More difficult to
compare two data
sets
Use only withcontinuous data
Pictogram
Easy to read
Visually appealing
Handles large data
sets easily using
keyed icons
Icons must be of
consistent size
Best for only 2-6
categories
Very simplistic
Frequency polygon
Visually appealing Anchors at both
ends may imply zero
as data points
Use only with
continuous data
Pie chart
Visually appealing
Shows percent of
total for each
category
Difficult to draw
(especially when the
angle of some
sectors are almost
the same or many
categories are
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involved)
Ogive
summarize a large
data set in visual
form
be easily understood
become more
smooth as data
points or classes are
added
summarize a large
data set in visual
form
complicated to
prepare
fail to reflect all data
points in a data set
7.0 Conclusion
As a conclusion, it is true that statistics use to describe things and it is the
science of collecting and arranging besides concerned with theories and techniques
that have been developed to manipulate data and we agree to this statement.
Its a pleasant surprise to know that most of the PPISMP female students in
IPG Kampus Temenggong Ibrahim are in normal condition which is 54% of them. While
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44% of the 50 them are underweight. And it is only 2% of them are overweight, and 0%
are in obese condition.
This is quite encouraging and everyone should be proud that PPISMP female
students in IPG KTI are concerned and take a good care on their body condition. On the
whole, it is encouraging to see teacher trainee students aware about their body
condition.
Through doing this project we have learnt that obesity has became a major
health problem among us. We also know that most of the students in our research area
which is IPG KTI, Johor Bahru are normal. Therefore, this is a good condition because
obesity can lead to various health problems such as diabetes, heart diseases and
stroke.
A persons weight is a result of many factors. These factors include
environment, family history and genetics, metabolism [the way your body changes food
and oxygen into energy], behavior or habits, and other factors.
Certain things, like family history, cannot be changed. However, other things
like a persons lifestyle habits can be changed. We can help prevent or treat overweight
and obesity if we:
Follow a healthful diet, while keeping our calorie needs in mind
Are physically active
Limit the time we spend being physically inactive
Proper medical check-up and advises from doctors
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Follow food pyramid and eat according it
Weight loss medicines and surgery also are options for some people who
need to lose weight if lifestyle changes do not work.
We will distribute pamphlets, flyers or brochures relating the negative effect of
obesity. This would increase the number of teacher trainee students who know all the
effect of obesity. This also will alert them about the problems faced obese. Thus, they
would start practicing a healthy lifestyle and also encourage other in our family and also
friends to lead a healthy life.
8.0 Reflection
I am Sivasangary A/P Raveendran from 3 PPISMP MT/BI/BT would like to
grab this opportunity to show my gratitude to God who had been giving blessings until I
could complete the task and also to give me such a wonderful chance to improve my
knowledge on Smart statistics.
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Good guidance of our lecture, Madam Repiah binti Singgah is an essential one
for our coursework. She always listens to our problems patiently and gave us the good
explanation even when called. She really makes us work with this task smoothly without
much pressure. Her encouragements produce self-confidence in me to proceed with the
task.
When I first get this assignment, I felt this task a bit tough because we have to do
the survey in our own. We are divided into several groups in the class to present our
task. We have to conduct a survey and process it in a mini project. As everyone knows,
group is tougher compared to individual work. However, I did not felt hard to work with
group members because I have been worked with them before this. My last experience
with them helps me to move smoothly to finish up my task.
This assignment had taught me many new things and knowledge to be adopted in
my life. I got chance to learn more about statistics even tough I already studied in
secondary school. For example, types of statistical graphs, the differences between the
statistical graphs, the way to choose the graph to represent our data, and also variety
ways to find the measure of central tendency. Moreover, not forget the survey that did
by us. This is my first experience in doing the survey. I find this experience quite
interesting and I get to know some teacher trainee too.
Furthermore, I learned new words which definitely help me in improving my
vocabulary. When going through the dictionary, I came across new words also. Apart
from it, the assignment had improved my grammar, pronunciation, constructing
sentence and also fluency of the language.
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Nevertheless, I do face some problems in completing this assignment. Nowadays,
presence of ICT is a must in our life. Therefore, we required to use ICT in this
coursework to complete our graphs. I faced problem because I have not been using
Microsoft excel to draw the graph. However, I gained so much knowledge by the ICT
and also realised the importance of the ICT.
As a future teacher, this assignment made accurate and deep picture of statistics
which may help in our future teaching in school. Hence, I wish this kind of project will be
continued so that greater achievements and talents can be polished in each person and
could help to learn new things. After finish this assignment, my impossible mission had
become possible. My policy nothing is impossible, now changed to everything is
possible and I am very happy to this coursework accomplishment.
NOTHING IS IMPOSSIBLE
Thank you.
SIVASANGARY A/P RAVEENDRAN
910301-01-5728
Im ASHVINI A/P VELAYUDHAM, would like to take this great opportunity to
share my experience doing this Basic Mathematics assignment. I felt very glad and
happy because we could finish this coursework on time. In the beginning of completing
this assignment, we went to the library and got relevant information about given task,
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that is mainly about statistics, which this provides us the opportunity to develop our
ability to work collaboratively among group members.
Firstly, we were divided into several groups to present our tasks. I worked
together with Nanthini, Vinoodini and Sangary. We were required to conduct a survey
on realistic problem among IPGKTI students and present the data collected. Our
lecturers guideline was very helpful to commence on this assignment. After being
briefed by our lecturer, we had a clear vision of how we were going do this assignment.
We faced many problems when we conduct this assignment. Such as finding out the
suitable realistic problem, cooperation of other students to give responses on the
survey, time management and the list goes on. Although we could manage all those
problem, and able to complete the assignment successfully.
By doing this assignment, I gained a lot of knowledge about statistic in
presenting the data. Moreover, I learned how to tabulate the data in a precise and
accurate manner. Apart from that, I also learned to use the Excel to produced bar
chart, histogram, and pie chart, graph and so on. Moreover I also improved my skills on
using computer to do statistic. Besides that, while doing this work, I got to know about
student body mass index. In addition, I also learned about the procedure and format to
carry out a survey and to do the questionnaire. All in all, I gained a great deal of
knowledge about statistic.
As Im a future teacher, I can apply my knowledge on statistic, to teach the
children about presenting the data in table, draw histogram or graph using computers in
order, they able to do statistic using computer since child. Moreover I could explain
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them how to get the others responses on an issue in correct manner by conduct a
survey. Learning math sure makes us smart and adept at solving tricky situations. Not
only does math provide a strong basis for resolving everyday issues, it undoubtedly
helps handle situations with a positive approach. Thank you.
Written by,
Ashvini a/p Velayudham
I am VINOODINI D/O ANNATHURAI from option of 3 PPIMP Mathematics /
English / Tamil would like to tell my activities and experiences that my group members
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and I did based on this basic mathematics assignment. As a trainee teacher at IPG KTI,
I was given a project to conduct a project involve data collection and write a report.
First of all, I was given a simple briefing by my lecturer. The explanation included
with the instructions that must be followed by every trainee teachers who are doing this
assignment. When I received the task I feel confused but our lecturer Madam Repiah
binti Singah give us full support and help us to finish this assignment.
From the given task, I have learned to understand well about statistics. I also
learned how to collect the data and relate it to the chart and graph. Other than that, I
learned to identify two suitable methods to visualize the data. This task makes me
understand and identify the strengths and weaknesses of each visual presentation.
During this time also I can tighten my friendship with my friends.
I am as a fast learner in ICT, I am able visualize the graphs easily. Im very
interested to apply the survey results in graphical and charts forms. From the survey,
awareness about obesity increases among my group members and me.
Although, I can do graphs and charts very well, still I was having still I have some
difficulties in doing Ogive. I learnt to do by asking my friends who knows. After this, I
should have a small group of discussion to master the ICT skills.
In future, I will teach juniors, cousins and siblings to visualize graphs and charts
in computer. In addition, I am going to apply whatever I had learnt in my future studies
and present studies. I also teach students using suitable graph methods and will co-
operate with my colleagues in future.
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And last but not least, this assignment teaches us what the co-operation is
actually. We learned how be co-operative in our group. And the co-operation also can
give a nice relationship between our group members. Besides that, this assignment
teaches us the skills of collection, analysis, interpretation or explanation, and
presentation of data.
VINOODINI A/P ANNATHURAI
910116-01-6358
I am Nanthini A/P Kanapathy from Unit of Mathematics / English / Tamil,
(PPISMP 3) would like to share my experiences with my group members. This
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Although I have some strength I also have some weakness in myself. Im slow in
using computer. In the part of doing this assignment plotting graph in GSP is difficult for
me. I can improve myself in ICT field by build a small group discussion or taking some
ICT course and seminar.
From this, I have learned to organize information clearly by using the suitable
representation. Next, I organized the data using table and make a visual representation.
Then, I also summarized a report. I learnt the characteristics of the graph and the
method to choose the suitable graph.
Best cooperation is the key to success. Co- operative is learned in this group
work. And the co-operation also can give a nice relationship between our group
members. Besides that, this assignment teaches us the skills of collection, analysis,
interpretation or explanation, and presentation ofdata.
As a future mathematics teacher I can share my knowledge about statistics with
my colleagues. I can teach students on how to choose a suitable graph to represent the
data. Last but not least, I would like to show my gratitude to everyone who supports me
economically. I would like to thank my friends who really shares information with us
without showing any depression. I had put my full effort and cooperate with each other
to complete the assignment as we wished. Thank You.
Youre faithfully,
http://en.wikipedia.org/wiki/Datahttp://en.wikipedia.org/wiki/Data8/7/2019 Basic Math fINAL
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Nanthini A/P Kanapathy
9.0 References
BOOK REFERENCES
LIM SWEE HOCK, KOO SENG HER, CHONG GEOK CHUAN, SAMADI BIN HASHIM
(2004), MATHEMATICS FORM 3, KUALA LUMPUR: DARUL FIKIR.
LIM SWEE HOCK, KOO SENG HER, DR. PUMADEVI.S (2006), MATHEMATICS
FORM 4, SELANGOR: PENERBIT FAJAR BAKTI SDN.BHD.
TAN LOWE (2001), VMATHS 10REVISED EDITION, AUSTRALIA: PEARSON
EDUCATION
WEB REFERENCES
Unknown. pie chart. http://www.answers.com/topic/pie-chart . Accessed on
16th August 2010
Unknown. Histogram. http://www.netmba.com/statistics/histogram/ . Accessed on
16th August 2010
Unknown. The way to collect data. http.www.datacollection.co/index.html .Accessed
on 17th August 2010
http://www.answers.com/topic/pie-charthttp://www.netmba.com/statistics/histogram/http://www.datacollection.co/index.htmlhttp://www.answers.com/topic/pie-charthttp://www.netmba.com/statistics/histogram/http://www.datacollection.co/index.html8/7/2019 Basic Math fINAL
50/56
Unknown. Line graph.http.www.mathgoodies.com/lessons/graphsline.html\.
Accessed on 17th August 2010
Unknown.http://www.itl.nist.gov/div898/handbook/eda/section3/scatterp.htm .
Accessed on 18th August 2010
Unknown. http://www.health.state.pa.us/hpa/stats/techassist/piechart.htm .
Accessed on 18th August 2010
Unknown. Advantage and disadvantage of graphs.
http://math.youngzones.org/
stat_graph.html. Accessed on 19th August 2010
Unknown. Standard deviation. http://www.investopedia.com/terms/s/standard
Deviation.asp. Accessed on 20th August 2010
Unknown. Ogive. http://www.preciousheart.net/chaplaincy/Auditor_Manual/
11grphd.pdf. Accessed on 20th August 2010
http://www.itl.nist.gov/div898/handbook/eda/section3/scatterp.htmhttp://www.health.state.pa.us/hpa/stats/techassist/piechart.htmhttp://math.youngzones.org/%20%20%20%20%20%20%20%20%20stat_graph.htmlhttp://math.youngzones.org/%20%20%20%20%20%20%20%20%20stat_graph.htmlhttp://www.investopedia.com/terms/s/standardhttp://www.preciousheart.net/chaplaincy/Auditor_Manual/http://www.itl.nist.gov/div898/handbook/eda/section3/scatterp.htmhttp://www.health.state.pa.us/hpa/stats/techassist/piechart.htmhttp://math.youngzones.org/%20%20%20%20%20%20%20%20%20stat_graph.htmlhttp://math.youngzones.org/%20%20%20%20%20%20%20%20%20stat_graph.htmlhttp://www.investopedia.com/terms/s/standardhttp://www.preciousheart.net/chaplaincy/Auditor_Manual/8/7/2019 Basic Math fINAL
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10.0 Collaboration form
STUDENTS NAME : 1. SIVASANGARY A/P RAVEENDRAN (910301-01-5728)
2. VINOODINI A/P ANNATHURAI (910116-01-6358)
3. ASHVINI A/P VELAYUDHAM (910320-10-5366)
4. NANTHINI A/P KANAPATHY (910818-05-5386)
OPTION : 3 PPISMP ( MATHEMATICS / ENGLISH / TAMIL )
SUBJECT : BASIC MATHEMATICS
LECTURER : PUAN. REPIAH BINTI SINGGAH
DATE ACTIVITY SIGNATURE
12.08.2010 Receive an assignment from our lecturer
about smart statistics.
Our lecturer gave us some explanation on
how to start it and we had to be divided
into three or four in a group, given fourweeks time to complete the assignment.
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14.08.2010
We read through the requirements of
question and tried to understand it.
We also discussed the questions anddistributed the work among us.
16.08.2010
We looked for further information from the
references books and by browsing net.
We discussed about the topic of the
survey.
20.08.2010
We prepared our survey items to this
coursework, which is related to obesity.
Then the questionnaire rechecked to avoid
mistakes before print it out.
23.08.2010
to
27.08.2010
Gathered all the information from the
reference books and internet
On 25th, we did the survey with the selected
classes.
The works divided among our group
members.
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30.08.2010
to
02.09.2010
We did our work by individually in the
room.
We did the graph in Microsoft excel. Combine our works, edited and rechecked
to avoid the mistakes.
09.09.2010 We submitted the assignment to our
lecturer.
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SURVEY ON THE OBESITY AWARENESS
AMONG PPISMP STUDENTS OF IPG K AMPUS
TEMENGGONG IBRAHIM
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INTRODUCTION: We are student from 3 PPISMP MT/BI/BT. We would like to carry out
a survey about obesity awareness among PPISMP female students in IPG KAMPUS
TEMENGGONG IBRAHIM
1. OBJECTIVES:
(a) To find out the students height and weight.
(b) To find out how students opinion about obesity.
2. This survey questionnaire contains two sections.
(i) Section A: Personal details
(ii) Section B: Obesity awareness
3. Please complete the questionnaires as accurately as possible.
4. We sincerely hope that you will answer all the items in the given sections.
5. All your responses will be kept confidential. Thank you for your cooperation.
SECTION A : PERSONAL DETAILS
Please tick ( ) your responses in space provided.
1. Race : Malay ( )
Chinese ( )
Indian ( )
Others ( )
2. Option : 3 PPISMP ( )
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3 PPISMP ( )
3 PPISMP ( )
1 PPISMP ( )
1 PPISMP ( )
SECTION B : OBESITY AWARENESS
1. Height : cm
2. Weight : kg
Answer the following questions with YES/NO.
3. Is that important to calculate BMI of a person?
4. Do you think obesity can be cure if you try to treat it?
5. Is the obesity can lead to various health problems?
6. Is family history is the one of the factors to affect obesity?
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