Basic Statistics & Data Analysis

Feb 9, 2015Download as pptx, pdf8 likes10,031 views

Statistics is the methodology used to interpret and draw conclusions from collected data. It provides methods for designing research studies, summarizing and exploring data, and making predictions about phenomena represented by the data. A population is the set of all individuals of interest, while a sample is a subset of individuals from the population used for measurements. Parameters describe characteristics of the entire population, while statistics describe characteristics of a sample and can be used to infer parameters. Basic descriptive statistics used to summarize samples include the mean, standard deviation, and variance, which measure central tendency, spread, and how far data points are from the mean, respectively. The goal of statistical data analysis is to gain understanding from data through defined steps.

What is Statistics ?
“ Statistics is the methodology which scientists and
mathematicians have developed for interpreting and
drawing conclusions from collected data ”

Statistical methods can be used to find answers to the
questions like:
• What kind and how much data need
to be collected?
• How should we organize and
summarize the data?
• How can we analyze the data and
draw conclusions from it?
• How can we assess the strength of
the conclusions and evaluate their
uncertainty?

Statistics provides methods for :
1. Design:
Planning and carrying out research
studies.
2. Description:
Summarizing and exploring data.
3. Inference:
Making predictions and
generalizing about phenomena
represented by the data.

Population and Sample
Population can be characterized as the set of individual persons
or objects in which an investigator is primarily interested during
his or her research problem often only a set of individuals of that
population are observed for measurements such a set of
individuals constitutes a sample.

Parameters and Statistics
 A parameter is an unknown
numerical summary of the
population.
 A statistics is a known
numerical summary of the
sample which can be used to
make inference about
parameters.
 A statistic describes a sample,
while a parameter describes
the population from which the
sample was taken.

Basic Descriptive Statistics
 Mean: The mean (also know as average),
is obtained by dividing the sum of
observed values by the number of
observations.
 Standard Deviation: The standard
deviation gives an idea of how close the
entire set of data is to the average value.
Data sets with a small standard deviation
have tightly grouped. Data sets with large
standard deviations have data spread out
over a wide range of values.
 Variance: Measures how far a set of
numbers is spread out. A variance of
zero indicates that all the values are
identical. Variance is always non-negative,
a small variance indicates that the data
points tend to be very close to the mean
(Average value), while a high variance
indicates that the data points are very
spread out around the mean.

Statistical data analysis
The goal of statistics is to gain understanding from data. Any data
analysis should contain following steps:

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Process Mining at AE - Key success factorsProcess mining Evangelist

Bram Vanschoenwinkel is a Business Architect at AE. Bram first heard about process mining in 2008 or 2009, when he was searching for new techniques with a quantitative approach to process analysis. By now he has completed several projects in payroll accounting, public administration, and postal services. The discovered AS IS process models are based on facts rather than opinions and, therefore, serve as the ideal starting point for change. Bram uses process mining not as a standalone technique but complementary and in combination with other techniques to focus on what is really important: Actually improving the process.

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Process Mining and Official Statistics - CBSProcess mining Evangelist

Johan Lammers from Statistics Netherlands has been a business analyst and statistical researcher for almost 30 years. In their business, processes have two faces: You can produce statistics about processes and processes are needed to produce statistics. As a government-funded office, the efficiency and the effectiveness of their processes is important to spend that public money well. Johan takes us on a journey of how official statistics are made. One way to study dynamics in statistics is to take snapshots of data over time. A special way is the panel survey, where a group of cases is followed over time. He shows how process mining could test certain hypotheses much faster compared to statistical tools like SPSS.

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Process Mining at Dimension Data - Jan vermeulenProcess mining Evangelist

Dimension Data has over 30,000 employees in nine operating regions spread over all continents. They provide services from infrastructure sales to IT outsourcing for multinationals. As the Global Process Owner at Dimension Data, Jan Vermeulen is responsible for the standardization of the global IT services processes. Jan shares his journey of establishing process mining as a methodology to improve process performance and compliance, to grow their business, and to increase the value in their operations. These three pillars form the foundation of Dimension Data's business case for process mining. Jan shows examples from each of the three pillars and shares what he learned on the way. The growth pillar is particularly new and interesting, because Dimension Data was able to compete in a RfP process for a new customer by providing a customized offer after analyzing the customer's data with process mining.

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Deloitte - A Framework for Process Mining ProjectsProcess mining Evangelist

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Process Mining at Rabobank - Organizational challengesProcess mining Evangelist

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Process Mining at AE - Key success factorsProcess mining Evangelist

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Process Mining and Official Statistics - CBSProcess mining Evangelist

定制(意大利Rimini毕业证)布鲁诺马代尔纳嘉雷迪米音乐学院学历认证Taqyea

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Basic Statistics & Data Analysis

1. Basic Statistics and Data Analysis

2. What is Statistics ? “ Statistics is the methodology which scientists and mathematicians have developed for interpreting and drawing conclusions from collected data ”

3. Statistical methods can be used to find answers to the questions like: • What kind and how much data need to be collected? • How should we organize and summarize the data? • How can we analyze the data and draw conclusions from it? • How can we assess the strength of the conclusions and evaluate their uncertainty?

4. Statistics provides methods for : 1. Design: Planning and carrying out research studies. 2. Description: Summarizing and exploring data. 3. Inference: Making predictions and generalizing about phenomena represented by the data.

5. Population and Sample Population can be characterized as the set of individual persons or objects in which an investigator is primarily interested during his or her research problem often only a set of individuals of that population are observed for measurements such a set of individuals constitutes a sample.

6. Parameters and Statistics  A parameter is an unknown numerical summary of the population.  A statistics is a known numerical summary of the sample which can be used to make inference about parameters.  A statistic describes a sample, while a parameter describes the population from which the sample was taken.

7. Basic Descriptive Statistics  Mean: The mean (also know as average), is obtained by dividing the sum of observed values by the number of observations.  Standard Deviation: The standard deviation gives an idea of how close the entire set of data is to the average value. Data sets with a small standard deviation have tightly grouped. Data sets with large standard deviations have data spread out over a wide range of values.  Variance: Measures how far a set of numbers is spread out. A variance of zero indicates that all the values are identical. Variance is always non-negative, a small variance indicates that the data points tend to be very close to the mean (Average value), while a high variance indicates that the data points are very spread out around the mean.

8. Statistical data analysis The goal of statistics is to gain understanding from data. Any data analysis should contain following steps:

9. Thank You

Basic Statistics & Data Analysis

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