Probability and Statistics

1. PAPER
PROBABILITY AND STATISTICS
( )
JOSHUA STEFANUS RUMAGIT
15108015
SISTEM INFORMASI
TRILOGI UNIVERSITY
2016/2017

2. Abstract
Statistics is one of the sciences needed for a study, not to mention socio-economic field more
qualitative. The role of statistics started from prior research, the study lasted until the
processing of research data. Beginning with the technique of sampling, validity and
reliability, hypothesis testing, data analysis to interpretation. Descriptive statistics is the
beginning of the presentation of data that demonstrated quantitatively in inferential statistics
by using a statistical test specific, tailored to the scale of measurement and types of
hypotheses that are used.
Keywords :
research, descriptive statistics, inferential statistics, data, scale of measurement, sampling
techniques, the validity, reliability, hypothesis, statistical test

3. 1. Preliminary
According to www.wikipedia.org, Statistics is the study of the collection, analysis,
interpretation, presentation, and organization of data. In applying statistics to, e.g., a
scientific, industrial, or social problem, it is conventional to begin with a statistical population
or a statistical model process to be studied. Populations can be diverse topics such as "all
people living in a country" or "every atom composing a crystal". Statistics deals with all
aspects of data including the planning of data collection in terms of the design of surveys and
experiments.
When census data cannot be collected, statisticians collect data by developing specific
experiment designs and survey samples. Representative sampling assures that inferences and
conclusions can safely extend from the sample to the population as a whole. An experimental
study involves taking measurements of the system under study, manipulating the system, and
then taking additional measurements using the same procedure to determine if the
manipulation has modified the values of the measurements. In contrast, an observational
study does not involve experimental manipulation.
In applying statistics to a problem, it is common practice to start with a population or process
to be studied. Populations can be diverse topics such as "all persons living in a country" or
"every atom composing a crystal".
Ideally, statisticians compile data about the entire population (an operation called
census). This may be organized by governmental statistical institutes. Descriptive statistics
can be used to summarize the population data. Numerical descriptors include mean and
standard deviation for continuous data types (like income), while frequency and percentage
are more useful in terms of describing categorical data (like race).
When a census is not feasible, a chosen subset of the population called a sample is
studied. Once a sample that is representative of the population is determined, data is collected
for the sample members in an observational or experimental setting. Again, descriptive
statistics can be used to summarize the sample data. However, the drawing of the sample has
been subject to an element of randomness, hence the established numerical descriptors from
the sample are also due to uncertainty. To still draw meaningful conclusions about the entire
population, inferential statistics is needed. It uses patterns in the sample data to draw
inferences about the population represented, accounting for randomness. These inferences
may take the form of: answering yes/no questions about the data (hypothesis testing),
estimating numerical characteristics of the data (estimation), describing associations within
the data (correlation) and modeling relationships within the data (for example, using
regression analysis). Inference can extend to forecasting, prediction and estimation of
unobserved values either in or associated with the population being studied; it can include
extrapolation and interpolation of time series or spatial data, and can also include data
mining.

4. This article describes the overall sense of understanding of statistics and its
differences with the statistics, why statistics needed in everyday life problems and other types
of statistics.
2. Difference Beetween The Statistical and Statistics
Did you know the basics of statistics and statistical differences? Indeed, we often hear
that the statistics and the statistics are the same in the sense must relate to figures. Statistics
and statistics is a science that can be learned in school and college. The function of the two is
to compare and solve a problem. However, some people are confused about the basic
difference that knowledge resulting errors in interpretation.
The first in this article to know about the statistical sense is the collection of data in
the form of numbers or not the numbers are arrange in tabular form (list) and or a diagram
that depicts or relates to a particular problem. The type of statistical data by way of acquiring
divided into two primary data and secondary data. Primary data is data that directly taken
through the object of study by researchers individually or in groups. For example a live
interview moviegoers XXI to empirically consumer pretensions cinema. While secondary
data is data obtained indirectly from the object of research. Here the researchers got the data
that has been collected by others through a variety of ways, both commercial and non-
commercial. For example researchers use research results from the newspapers that will be
used for statistical research.
Inferential statistics are statistics relating to how to draw conclusions based on the
data obtained from the sample to describe the characteristics or traits of a population. Thus, in
a generalization inferential statistics (Generalizations or memperumum) and the things that
are special (small) to the wider (public). Therefore, inferential statistics also called inductive
statistics or statistical inference. In the usual inferential statistics hypothesis testing and
estimation done on the characteristics (characteristics) of a population, such as mean and t
test (Sugiyono, 2006).
Classification, Types and Data Types In Statistical
A. According to the data type to get it
1. Primary Data
Primary data is directly taken from the object / object of research by individual
researchers and organizations. Example: Interviewing directly moviegoers 21 to
examine consumer preferences cinema.

5. 2. Secondary Data
Secondary data is data obtained indirectly from the object of research. Researchers get
the data that is so collected by others in various ways or methods for commercial and
non-commercial. Examples are the researchers who use statistical data on research
results from a newspaper or magazine.
B. Various Kinds of Data Based on Data Sources
1. Internal Data
Internal data is the data that describes the situation and conditions in an organization
internally. For example: financial data, employee data, production data, and so on.
2. External Data
External data is data describing the situation and conditions that are outside the
organization. An example is the amount of use of a product to consumers, the level of
customer preferences, population distribution, and so forth.
C. Classification by Type of Data The data
1. Quantitative Data
Quantitative data is the data being presented in the form of numbers. For example is
the number of shoppers during the holiday Eid al-Adha, height Grade 3 ips 2, and
others.
2. Qualitative Data
Qualitative data is data that is presented in the form of words that implies. Examples
such as consumer perceptions of bottles of bottled water, assuming the experts on
psychopaths and others.
D. Distribution Based Data Type Properties Data
1. Discrete Data
Discrete data is data whose value is a natural number. An example is the weight
mothers ayu pkk sources, the value of the rupiah from time to time, and so forth.
2. Continuous Data
Continuous data is data whose value is in a certain interval or that are in grades one to
the other values. For example, use of the word about, approximately, roughly, and so
forth. Department of regional agricultural fertilizer factories import raw materials
more than 850 tons.

6. E. Types of Data According to the time it was collected
1. Data Cross Section
Cross-section data is data that shows the specific point in time. For example, the
financial statements as of 31 December 2006, customer data PT. windstorm in May
2004, and so forth.
2. Data Time Series / Periodical
Periodic data is data whose data describe something from time to time or historical
period. Examples of time series data is data the exchange rate against the US dollar
and European euro from 2004 to 2006, the number of followers of pilgrims nurdin m.
Top and Azahari doctorate from month to month, etc.
F. Types of data by measuring levels.
1. Data Rate
Data rate is data High Rankings That paled. Data ratios have the distance between an
exact value and has a value of absolute zero that is not owned by this type of data.
Examples ratio data such as weight, body length, the number of units of objects. IF we
have 10 balls then there theem bodiment 10 balls, and when a person has 0 ball then
someone does not have the ball. Ratio data can be used in mathematical calculations,
for example A and B have 10 balls have 8 balls, then A has two balls (10-8) more
From B.
2. Data Interval
Data interval having a lower level than the data rate. Distance ratio data have
definitive data, but does not have an absolute zero value. Examples of interval data is
the result of a math test scores. If A and B scored 10 got a score of 8, then certainly
more value banyakdari Amempunyai 2 B. But there is no absolute zero value, ie when
C gets a value of 0, does not mean that the C in math ability is null or empty.
3. Ordinal Data
Ordinal data is basically the result of quantifying qualitative data. Examples of ordinal
data is scaling individual attitudes. Scaling the individual attitudes toward something
can be realized in various forms, such as: the attitude Strongly Agree (5), Agree (4),
Neutral (3), Disagree (2), and Strongly Disagree (1). In this ordinal level data did not
have definitive data range, for example: Strongly Agree (5) and Agree (4) is not
known for sure the distance between the value for the distance between Strongly
Agree (5) danSetuju (4) instead of 1 unit (5- 4).
4. Data Nominal

7. Nominal data is the lowest data rates according to the level measurement. The
nominal data on a single individual has no variation at all, so one individual only has
one form of data. Examples of nominal data such as: gender, place of residence, year
of birth etc. Each individual will only have 1 record sex, male or female. Data sexes
will later be labeled in processing, for example, female = 1, male = 2.
There is another type of data that is frequently mentioned in the statistical data is parametric
and non-parametric. If the "NOIR" is the sharing of data by level measurement, distribution
of parametric and non-parametric empirical influenced by the characteristics of the data.
Knowledge of parametric and nonparametric data limits is particularly important because the
analysis process is different for each type of data.
Understanding statistics
Statistics is the study of the statistics, namely the study bagaimanacaranya collect data,
process data, presenting data, analyzing the data, membuatkesimpulan from data analysis and
make decisions based on the conclusions.
division of Statistics
1. Descriptive Statistics are statistics that learn how to collect data, process data,
presenting data, analyzing the data
2. Inductive Statistics (inferences) is a statistical study how to collect data, process
data, presenting data, analyze the data, make conclusions and make decisions
usefulness Statistics
Statistics studied in various fields of science, because statistics is a set of tools that can help
decision-makers based on the conclusions on data analysis of the data collected. In addition,
the statistics we can foresee circumstances that would come Based on past data.
definitions Population
Population is the whole of the research object
definition Samples
The sample is part of the population. Good sample is a representative sample, which is a
representative sample of the population. To be representative, the sampling of the population
have to use sampling techniques (sampling) is correct. There are two sampling techniques:

8. 1. Sampling by chance.
Sampling technique based opportunity is a sampling technique where each observation unit in
the population has the same chance of being selected into the sample. There are 3 sampling
technique by chance:
 Simple Random Sampling is a sampling technique in which samples are taken based
on the random number table
 Sampling Classification is a sampling technique in which the population is first
divided up into sub-sub-populations among sub homogeneous population. Because
homogeneous subpopulations, one sub-population sampled
 Sampling Stratification is a sampling technique in which the population is first
divided up into sub-sub-sub-populations between heterogeneous population. Because
sub heterogeneous population, in each sub polulasi there were sampled
2. The sampling technique is not based on chance.
Not by chance sampling technique is a sampling technique in which every nit observation
role in the population does not have equal opportunity to be elected sampel.Ada some
sampling techniques not based opportunities, including:
 convenience sampling technique (roughing)
 Sampling judgment (judgment)
Differences of Statistics and Statistical
Understanding statistics is a scientific method to learn how to collect, manage,
calculate, analyze, and draw conclusions about the data. Statistics according to the function is
divided into two, namely descriptive statistics and inferential statistics.
Where descriptive statistics (statistics deductive) only as statistics that describe and
analyze the data group without drawing conclusions about larger sets of data. While
inferential statistics (statistics Inductive) is a technique involves a statistical description and
analysis of the data groups to draw conclusions function. Statistical for words alone can we
interpret as a measure that is calculated from a set of data and a representative /
representatives of such data.
an example of an ad that often appears on TV "90% of women use shampoo XX as a
choice". In this case, the percentage of women is a statistical measure called earlier. I take the
example again, Suppose the average height is 159 cm Class A, average is a statistic. There
are still many other examples that we can take but two of these examples are enough to
describe the statistical significance.

9. The difference between the two:
-Statistics Is science, while statistical are resized
-Statistics A scientific method associated with the data, while the statistical is a collection of
figures on a problem, and can give a description of the problem.
DEFINITIONS AND STATISTICS AND STATISTICAL DIFFERENCE
The term statistical comes from the Latin "status" which means a country. A data
collection activities that had to do with the state, for example, data on population, data on
income and so on, the more functions to serve administrative purposes.
In linguistics, statistical mean record numbers (numbers); perangkaan; data of the
numbers compiled, tabulated, grouped, so that it can provide meaningful information about a
problem, symptoms or events (Department of Education, 1994).
According Sutrisno Hadi (1995) statistical is to show the recording figures of an event
or a particular case. In harmony with what is defined by Sudjana (1995: 2) that the statistic is
a collection of facts form of numbers arranged in a list or a table or diagram, illustrating or
describing a problem.
Statistics unlike the case with statistics, statistics that in English "statistics" (the
science of statistics), knowledge about ways to collect, tabulate and classify, analyze and find
meaningful information from the data in the form of numbers.
Statistics is the science which deals with methods for collecting, menabulasi, classify,
analyze, and find meaningful information from the data in the form of numbers or figures, so
it can be drawn a conclusion or a particular decision.
In addition, Statistics is also a branch of applied mathematics that consists of theory
and methods on how to collect, measure, classify, calculate, explain, synthesize, analyze, and
interpret data obtained systematically. While in the world of education, statistics discusses the
principles, methods, and procedures that are used as a means of collecting, analyzing and
interpreting a set of data relating to education.Furthermore, statistics in Special Education can
be defined as the use (application) principles, fundamentals and statistical calculations to
analyze problems-problems PLB. Also on the other hand, Statistics in psychology is defined
as the use (application) principles, fundamentals and statistical calculations to analyze
problems-problems in psychology.

10. 3. Conclusions
In its basic statistical and statistics are different but both are interrelated, with this article the
reader should be able to distinguish between the statistics and the statistical sense. the two
concepts are very useful in almost every life and work everyday human.

11. REFERENCE
http://perbedaanterbaru.blogspot.com/2015/08/kenali-pengertian-dan-perbedaan.html
https://en.wikipedia.org/wiki/Statistics
http://www.kompasiana.com/fatahahmadi/pengertian-statistik_5518943081331127699de89e
Sugiyono. 2003. Statistika untuk Penelitian, Bandung. Alfabeta
Susetyo, Budi. 2010. Statistika Untuk Analisis Data Penelitian. Bandung. PT. Refika
Aditama.