1) Statistics is the study of collecting, organizing, analyzing, and drawing conclusions from data. It involves sampling, hypothesis testing, and using statistical tests tailored to measurement scales and hypothesis types.
2) Descriptive statistics describe and summarize data quantitatively, while inferential statistics allow generalizing from samples to populations through statistical testing and other methods.
3) The document discusses differences between statistics and statistical data, types of data, levels of measurement, sampling techniques, and uses of statistics.
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Types of Data, Difference between Primary and Secondary Data, Collection of Primary Data, Questionnaire, Schedules, Interview, Survey, Observation, Secondary Data, Sources of Secondary Data, Tabulation of Data – Meaning and Types
Get your quality homework help now and stand out.Our professional writers are committed to excellence. We have trained the best scholars in different fields of study.Contact us now at http://www.essaysexperts.net/ and place your order at affordable price done within set deadlines.We always have someone online ready to answer all your queries and take your requests.
Types of Data, Difference between Primary and Secondary Data, Collection of Primary Data, Questionnaire, Schedules, Interview, Survey, Observation, Secondary Data, Sources of Secondary Data, Tabulation of Data – Meaning and Types
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
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This presents an overview about relevance and significance of statistics as a valid tool in enhancing quality of research. It also touches upon some misuse and abuse of statistics.
Fundamentals Of Statistics-Definition of statistics,Descriptive and Inferential Statistics,Major Types of Descriptive Statistics,Statistical data analysis
Statistical methods and analyses are used to communicate research findings and give credibility to research methodology and conclusions. It is important for researchers and also consumers of research to understand statistics so that they can be informed, evaluate the credibility and usefulness of information, and make appropriate decisions.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
This presents an overview about relevance and significance of statistics as a valid tool in enhancing quality of research. It also touches upon some misuse and abuse of statistics.
Fundamentals Of Statistics-Definition of statistics,Descriptive and Inferential Statistics,Major Types of Descriptive Statistics,Statistical data analysis
Statistical methods and analyses are used to communicate research findings and give credibility to research methodology and conclusions. It is important for researchers and also consumers of research to understand statistics so that they can be informed, evaluate the credibility and usefulness of information, and make appropriate decisions.
Statistics as a subject (field of study):
Statistics is defined as the science of collecting, organizing, presenting, analyzing and interpreting numerical data to make decision on the bases of such analysis.(Singular sense)
Statistics as a numerical data:
Statistics is defined as aggregates of numerical expressed facts (figures) collected in a systematic manner for a predetermined purpose. (Plural sense) In this course, we shall be mainly concerned with statistics as a subject, that is, as a field of study
Notes of BBA /B.Com as well as BCA. It will help average students to learn Business Statistics. It will help MBA and PGDM students in Quantitative Analysis.
This is the best reference book for the subject of 'Statistics Math' that is useful for the students of BBA.
It has covered the course contents in a proper understanding way.
Statistics is the scientific methods for collecting, organizing, presenting and analyzing data as well as deriving the valid conclusion and making reasonable decision on the basis of this analysis.
1. Introduction to statistics in curriculum and Instruction
1 The definition of statistics and other related terms
1.2 Descriptive statistics
3 Inferential statistics
1.4 Function and significance of statistics in education
5 Types and levels of measurement scale
2. Introduction to SPSS Software
3. Frequency Distribution
4. Normal Curve and Standard Score
5. Confidence Interval for the Mean, Proportions, and Variances
6. Hypothesis Testing with One and two Sample
7. Two-way Analysis of Variance
8. Correlation and Simple Linear Regression
9. CHI-SQUARE
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
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.