1. Statistics is
the science of conducting studies to collect, organize, summarize, analyze, present, interpret and draw conclusions from data.
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2. A sample is a part of a population from which the data collected.
A population is a set of entire object that addressed in our research. Table
A variable is a characteristic of a population which can take different values.
A sample is a part of a population from which the data collected.

3. Data are mesurements collected on variable as a result of taking observations
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Quantitive data are numerical measurement expressed by in terms of numbers
Qualitative data are categorical measurement expressed by means of natural language description.

4. Statistics is
the science of conducting studies to collect, organize, summarize, analyze, present, interpret and draw conclusions from data.
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Descriptive statistics are numbers that are used to summarize and describe data
Inferential statistics use data from a sample to answer questions about a population. Inferential statistics involves generalizing beyond the data at hand.

5. Cumulative AIDS Cases by Mode of Transmission
Presenting Data
Cumulative AIDS Cases by Mode of Transmission
Frequency
Table

6. Cumulative AIDS Cases by Age
Presenting Data
Cumulative AIDS Cases by Age
Frequency
Table

7. Presenting Data
Table

8. Cumulative AIDS Cases by Age Group
Presenting Data
Cumulative AIDS Cases by Age Group
Frequency
Age
Table

9. Presenting Data
Cumulative Frequency
Table

10. SINGLE DATA
90, 90, 100, 60, 50, 50, 70, 70, 60, 70, 80, 80, 90, 70, 70, 70, 80, 80, 80, 60, 70, 70, 70, 70, 80, 80, 80, 80, 90, 60, 60, 70, 70 ,80
Score
Number of Students 50 2 60 5 70 12 80 10 90 4
100 1
Total

11. GROUP DATA Marks Number of Students 40-50 6 50-60 11 60-70 19 70-80 17
80-90 13
90-100 4
Total

12. Measuring the central tendency of data
Measuring Data
Measuring the central tendency of data
The mean of a data set is
the statistical name for the arithmetic average and can be found by dividing the sum of the data by the number of data.
SINGLE DATA

13. Measuring the central tendency of data
Measuring Data
Measuring the central tendency of data
The mean of a data set is
the statistical name for the arithmetic average and can be found by dividing the sum of the data by the number of data.
GROUP DATA

14. Measuring Data The mode is Measuring the central tendency of data
the most frequently occurring value in the data set.

15. Measuring Data GROUP DATA The mode is
Measuring the central tendency of data
The mode is
the most frequently occurring value in the data set.
GROUP DATA

16. Measuring Data SINGLE DATA Measuring the central tendency of data
 The median is
the middle value of an ordered data set.
SINGLE DATA

17. Measuring Data GROUP DATA Measuring the central tendency of data
 The median is
the middle value of an ordered data set.
GROUP DATA

18. Frequency Distribution: Different Distribution shapes
This slide shows some examples of different shapes that D can take. The top left = ND which I’ve already described: majority of scores fall around a mid point with fewer and fewer as the scores get more or less extreme. The top right D shows another type of distribution shape: here the majority of scores fall around two values. The bottom two graphs show D shapes that again cluster around a central value: but unlike the top two graphs they are not symmetrical ( =can draw vertical line through middle and one side is mirror image of each other).
Lets describe these D in terms of what they mean for actual score. Lets imagine that these are all graphs of exam results for different exams, so as the x axis goes along the higher the exam results. The TL graph shows an exam where most people got results around the mid point: v few got v low and v few got v high scores. The TR graph shows 2 peaks, so lots of people got exam results either at one particular lower level (left peak) or a higher level (right peak), with less getting v low, v high or scores in the middle. The BL graph shows that most people got very low scores as the peak of the curve is near the beginning of the x axis, with very few getting high scores (v hard exam). The BR graph is the opposite with the peak of scores near the end of the x axis: so most people scored highly on this exam. D where scores pile at one end or another while the tail of the scores taper off to the other end are called skewed D. So the BL is an example of + skew, so called because the tail of scores tapers off towards the + end of the x axis (looks like p facing upwards), - skew so called because tail tapers off towards – end of x axis. This kind of information is clear from looking at a D.

19. Central Tendencies and Distribution Shape
It is possible to represent the CT (whatever type) on the FD polygon by bisecting the curve with a straight line to represent your mean median or mode. With a ND (symmetrical, bell-shaped curve) the mean, median , and mode will all have the same value and will be represented by the exact centre of the D. For symmetrical D the mean and median will always be exactly in the centre, and will always be the same value.
On skewed D shown here the position of the 3 measures differs slightly. The mode will always be at the highest point of the curve (representing the most frequent score); the median will be exactly in the middle of the D (the middle position). On + skew (the L graph) where the majority of scores are at the lower end of the scale, this means that the mode will be the lowest value, followed by the median. Then mean score will be affected by the few extreme higher scores and so will be the highest values of the 3 measures of CT. On the –skew where most of the scores are at the higher end of the scale, the opposite pattern is observed.

20. Population: Students of Inter-classes at MAN 4 Jakarta
Sample: 50 students of inter-classes at MAN 4 Jakarta
No.
Single Data
Group Data 1
Best activity in holiday
Sleeping hours per week 2
Favorite subject at school
Learning hours per week 3
Favorite food at canteen
Internet hours per week 4
Favorite drink at canteen
Minutes of house-school-house taken per day 5
Favorite spot at school
Hours spent with parents per week 6
Extracurricular activity/activities at school
Hours spent with friends per week 7
Hobby
Hours spent to surfing the internets 8
Number of siblings
Body Weights 9
Shoes’ numbers
Body Heights 10
Favourite sport
Hours spent to learn math per week 11
The most frequently illness happened
Numbers of books belongings 12
Favorite place to hang out with friends
Numbers of cd movies belongings

21. The Relationship Between The Mean and The Median For Different Distributions

22. Measuring the variability of data
Measuring Data
Measuring the variability of data