Basic Math fINAL

8/7/2019 Basic Math fINAL

1/56

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
http://en.wikipedia.org/wiki/Mathematicshttp://en.wikipedia.org/wiki/Datahttp://en.wikipedia.org/wiki/Design_of_experimentshttp://en.wikipedia.org/wiki/Survey_samplinghttp://en.wikipedia.org/wiki/Survey_samplinghttp://en.wikipedia.org/wiki/Statistical_modelhttp://en.wikipedia.org/wiki/Academic_disciplinehttp://en.wikipedia.org/wiki/Datahttp://en.wikipedia.org/wiki/Design_of_experimentshttp://en.wikipedia.org/wiki/Survey_samplinghttp://en.wikipedia.org/wiki/Survey_samplinghttp://en.wikipedia.org/wiki/Statistical_modelhttp://en.wikipedia.org/wiki/Academic_disciplinehttp://en.wikipedia.org/wiki/Mathematics


2/56

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.
http://en.wikipedia.org/wiki/Academic_disciplinehttp://en.wikipedia.org/wiki/Naturalhttp://en.wikipedia.org/wiki/Social_sciencehttp://en.wikipedia.org/wiki/Descriptive_statisticshttp://en.wikipedia.org/wiki/Academic_disciplinehttp://en.wikipedia.org/wiki/Naturalhttp://en.wikipedia.org/wiki/Social_sciencehttp://en.wikipedia.org/wiki/Descriptive_statistics


3/56

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.


4/56

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.


5/56

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


6/56

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


7/56

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


8/56

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


9/56

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


10/56

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


11/56

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.


12/56

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.


13/56

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


14/56

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


15/56

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.


16/56

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


17/56

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
http://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Cumulative_distribution_functionhttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Cumulative_distribution_function


18/56

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
http://whatis.techtarget.com/definition/0,289893,sid9_gci934747,00.htmlhttp://whatis.techtarget.com/definition/0,289893,sid9_gci212310,00.htmlhttp://whatis.techtarget.com/definition/0,289893,sid9_gci934747,00.htmlhttp://whatis.techtarget.com/definition/0,289893,sid9_gci212310,00.html


19/56

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.


20/56

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.


21/56

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


22/56

b) Calculating the mean from a frequency table

Finding the mean for the weight of the 50 female trainees.


31 35 0

36 40 5


23/56

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


24/56

= 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


31 35 0

36 40 5

41 45 15

46 50 9

51 55 9

56 60 7

61 65 5


25/56

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


26/56

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


27/56

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


28/56

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


29/56

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


30/56

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


31/56

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


32/56

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


33/56

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 %


34/56

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


35/56


36/56

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


37/56

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


38/56

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


39/56

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


40/56

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.


41/56

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.


42/56

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,


43/56

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


44/56

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


45/56

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.


46/56

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


47/56


48/56

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/Data


49/56

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.html


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 .


Unknown. http://www.health.state.pa.us/hpa/stats/techassist/piechart.htm .


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/


51/56

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.


52/56

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.


53/56

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.


54/56

SURVEY ON THE OBESITY AWARENESS

AMONG PPISMP STUDENTS OF IPG K AMPUS

TEMENGGONG IBRAHIM


55/56

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 ( )


56/56

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?

Basic Math fINAL

Documents

Transcript of Basic Math fINAL