This document discusses quantitative and qualitative data analysis. It defines key terms like analysis, hypothesis, descriptive statistics, inferential statistics, and parametric and nonparametric tests. It explains the steps of quantitative data analysis which include data preparation, describing the data through summary statistics, drawing inferences through inferential statistics, and interpreting the results. Common parametric tests include t-tests, ANOVA, and correlation. Common nonparametric tests include chi-square, median, Mann-Whitney, and Wilcoxon tests. The document emphasizes accurate presentation of analyzed data through narratives and tables.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
In any single written message, one can count letters, words or sentences. One can categories phrases, describe the logical structure of expressions, ascertain associations, connotations, denotations, elocutionary forces, and one can also offer psychiatric, sociological, or political interpretations. All of these may be simultaneously valid. In short a message may convey a multitude of contents even to a single receiver.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
In any single written message, one can count letters, words or sentences. One can categories phrases, describe the logical structure of expressions, ascertain associations, connotations, denotations, elocutionary forces, and one can also offer psychiatric, sociological, or political interpretations. All of these may be simultaneously valid. In short a message may convey a multitude of contents even to a single receiver.
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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Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Presentation is made by the student of M.phil Jameel Ahmed Qureshi Faculty of Education Elsa Kazi campus Hyderabad UoS Jamshoron, This presentation is an assignment assign by the Dr. Mumtaz Khwaja
Part of a course I run introducing quantitative methods. One of the slideshows on my site www.kevinmorrell.org.uk please reference the site if you use any of it - hope it is useful.
definition of survey
survey and its type
its purpose and uses.
sampling
approaches
survey methods
research designs
probability and non probability
population
cross sectional design
longitudinal design
successive independent sampling design
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
Data Analysis & Interpretation and Report WritingSOMASUNDARAM T
Statistical Methods for Data Analysis (Only Theory), Meaning of Interpretation, Technique of Interpretation, Significance of Report Writing, Steps, Layout of Research Report, Types of Research Reports, Precautions while writing research reports
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
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
Presentation is made by the student of M.phil Jameel Ahmed Qureshi Faculty of Education Elsa Kazi campus Hyderabad UoS Jamshoron, This presentation is an assignment assign by the Dr. Mumtaz Khwaja
Part of a course I run introducing quantitative methods. One of the slideshows on my site www.kevinmorrell.org.uk please reference the site if you use any of it - hope it is useful.
definition of survey
survey and its type
its purpose and uses.
sampling
approaches
survey methods
research designs
probability and non probability
population
cross sectional design
longitudinal design
successive independent sampling design
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
Data Analysis & Interpretation and Report WritingSOMASUNDARAM T
Statistical Methods for Data Analysis (Only Theory), Meaning of Interpretation, Technique of Interpretation, Significance of Report Writing, Steps, Layout of Research Report, Types of Research Reports, Precautions while writing research reports
CRM 101: What is CRM?
This is a simple definition of CRM.
Customer relationship management (CRM) is a technology for managing all your company’s relationships and interactions with customers and potential customers. The goal is simple: Improve business relationships to grow your business. A CRM system helps companies stay connected to customers, streamline processes, and improve profitability.
When people talk about CRM, they are usually referring to a CRM system, a tool that helps with contact management, sales management, agent productivity, and more. CRM tools can now be used to manage customer relationships across the entire customer lifecycle, spanning marketing, sales, digital commerce, and customer service interactions.
A CRM solution helps you focus on your organization’s relationships with individual people — including customers, service users, colleagues, or suppliers — throughout your lifecycle with them, including finding new customers, winning their business, and providing support and additional services throughout the relationship.
Who is CRM for?
A CRM system gives everyone — from sales, customer service, business development, recruiting, marketing, or any other line of business — a better way to manage the external interactions and relationships that drive success. A CRM tool lets you store customer and prospect contact information, identify sales opportunities, record service issues, and manage marketing campaigns, all in one central location — and make information about every customer interaction available to anyone at your company who might need it.
With visibility and easy access to data, it's easier to collaborate and increase productivity. Everyone in your company can see how customers have been communicated with, what they’ve bought, when they last purchased, what they paid, and so much more. CRM can help companies of all sizes drive business growth, and it can be especially beneficial to a small business, where teams often need to find ways to do more with less.
Here’s why CRM matters to your business.
CRM is the largest and fastest-growing enterprise application software category, and worldwide spending on CRM is expected to reach USD $114.4 billion by the year 2027. If your business is going to last, you need a strategy for the future that’s centered around your customers, and enabled by the right technology. You have targets for sales, business objectives, and profitability. But getting up-to-date, reliable information on your progress can be tricky. How do you translate the many streams of data coming in from sales, customer service, marketing, and social media monitoring into useful business information?
A CRM system can give you a clear overview of your customers. You can see everything in one place — a simple, customizable dashboard that can tell you a customer’s previous history with you, the status of their orders, any outstanding customer service issues, and more. You can even choose to include information
·Quantitative Data Analysis StatisticsIntroductionUnd.docxlanagore871
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Quantitative Data Analysis: Statistics
Introduction
Understanding the use of basic statistical strategies is part of being a critical consumer of published research literature. Unless they plan to conduct research themselves, it is not as important for counselors to understand the mathematical calculations of the statistical techniques as it is to be able to recognize the names of the common ones and what kind of information they provide. There are several commercially-available software packages for analyzing quantitative data, one of which is described in detail in Chapter 14 of
Counseling Research: Quantitative, Qualitative, and Mixed Methods
.
Descriptive and Inferential Statistics
In quantitative studies, statistical techniques are used for data analysis. The two main categories of statistics are descriptive and inferential. Descriptive statistics are used to summarize the data. Some common descriptive statistics are the measures of central tendency: the mean, median, and mode. They provide information about where the middle is in distribution of scores. On the normal distribution, the mean, median, and mode are the same. Distributions are said to be skewed when extreme scores draw the mean away from the middle of the distribution. Measures of variability, such as the range, variance, and standard deviation, provide information about how widely a distribution of scores is dispersed (Erford, 2015, p. 250). The standard deviation is a measure of how the scores cluster around the mean. The greater the standard deviation, the greater the spread of scores.
Toggle DrawerHide Full Introduction
Inferential statistics are used to make inferences from the sample to the population. All inferential statistical procedures are based on probability theory. They are used to test hypotheses. Three commonly used inferential statistics are chi square, t-test, and analysis of variance (ANOVA). Chi square is used with nominal data to determine if the observed expected frequency differs significantly from the expected frequency. A t-test is used to determine whether there is a statistically significant difference between the means of two groups. ANOVA is used to determine whether there is a statistically significant difference between the means of three or more groups.
Statistical Significance
When a quantitative study tests a hypothesis, it is technically the null hypothesis being tested. The null hypothesis says there is no difference between the groups, or relationship between the variables (depending on the research design). If the statistical procedure indicates there is statistical significance, the null hypothesis is rejected, meaning that the probability is high that there really is a group difference or strong relationship between the variables.
Rejecting the null hypothesis is not equivalent to proving the research or alternative hypothesis. Researchers can embrace the research hypothesis as one plausible explanation, but because only .
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. INTRODUCTION.
• Analysis and interpretation of data is the most important phase of
research process.
• Data collection is followed by the analysis and interpretation of data,
where collected data are analyzed and interpreted in accordance with
study objectives.
• Analysis and interpretation of data includes
compilation,editing,coding, classification and presentation of data.
• The collected data are known as raw data, the raw data are
meaningless unless certain statistical treatment are given to them
3. DEFINITONS.
• Analysis is the process of organizing and synthesizing the data so as to
answer research questions and test hypothesis.
• Analysis is referred as a method of organizing data in such a way that
research questions can be answered and hypothesis can be tested.
• Analysis is the process of breaking a complex topic into smaller parts
to gain better understanding of it.
4. HYPOTHESIS
• Hypothesis is a tentative prediction or explanation of the relationship
between two variables or more
5. • Quantitative data: Quantitative research involves analysis of
numerical data, data is collected and analyzed by using descriptive or
inferential statistics.
• Qualitative data: Data is collected in descriptive form rather than
numerical form and analyzed by descriptive coding, indexing and
narrations.
6. ANALYSIS OF QUANTITATIVE DATA.
• Analysis of quantitative data deals with information collected during
research study ,which can be quantified , and statistical calculations
can be computed.
7. STEPS OF QUANTITATIVE DATA ANALYSIS.
• Data analysis process includes the following four steps:
1. Data preparation (cleaning and organizing data for analysis)
2. Describing The Data(Descriptive or Summary Statistic)
3. Drawing the inferences of Data(Inferential Statistics)
4. Interpretation of Data
8. 1. Data preparation (cleaning and organizing
data for analysis)
• It involves logging or checking the data in, checking the data for
correctness, entering the data into the computer, transforming the
data, and documenting as well as developing a database structure to
integrate different measures.
9. Data preparation involves the following steps:
A. Compilation.
B. Editing.
C. Coding.
D. Classification.
E. Tabulation
10. Contd.
1. Compilation: It includes gathering together all the collected data in
a manner that a process analysis can be initiated.
2. Editing: It implies the checking of the gathered data for accuracy,
utility and completeness
3. Coding: Coding is important for analysis as numerous replies can be
reduced to a small number of classes through coding.
4. Classification: The classification of data is necessary as many
researches result in large volumes of raw data which must be
reduced to homogenous groups.
11. Contd…
• The classification of data could be:
Geographic classification: Areas of residence: Urban, semi-urban,
rural etc..
Chronological classification: Classified based on time period, such as
days, months, years,etc…
Qualitative classification: The data are classified based on certain
attributes such as gender, religion,type of diseases, etc….
Quantitative: Such as age, height, weight, income, Hb level, are
classifies based on quantitative classes such as: Monthly income in
rupees: <5000,5001-10000
12. Contd.
5. Tabulation: It is the recording of the classified data in accurate
mathematical terms. A table is a tabular representation of statistical
data. Basically tables are of 4 types:
1.Frequency distribution
2. Contingency Tables
3. Multiple response tables
4. Miscellaneous tables.
13. 2.Describing the data(Descriptive or summary
statistics)
• Descriptive statistics is used to describe the basic features of data to
provide simple summaries about the sample and the measures used
in a study.
• Classification of the descriptive statistics that includes:
1.Measures to condense data(frequency and percentage distribution
through tabulation and graphic presentations
2.Measures of central tendency
3. Measures of dispersion
4.Measure of relationships(Correlation coefficient)
14. 3. Drawing the Inferences of Data(Inferential Statistics)
• Inferential statistics helps in drawing inferences from the data e.g.,
finding the differences, relationships and association between two or
more variables by the help of the parametric and non parametric
statistical tests.
• The most commonly used inferential statistical tests are Z-test,t-test
,ANOVA, chi-square tests,etc
• An inference is a conclusion or judgment based on evidence.
15. Contd.
• Choice of inferential Statistical tests:
1.Type-I and type –II Errors
Type-1 error occurs when null hypothesis is rejected, when it should
have been accepted. It is also called alpha error. Type-II error occur
when null hypothesis is accepted ,when it should actually have been
rejected.
16. • In statistics, a Type I error is a false positive conclusion, while
a Type II error is a false negative conclusion.
Example:
You decide to get tested for COVID-19 based on mild symptoms.
There are two errors that could potentially occur:
• Type I error (false positive): the test result says you have
coronavirus, but you actually don’t.
• Type II error (false negative): the test result says you don’t
have coronavirus, but you actually do.
17. Contd
2.Level of significance:
Probability of making Type –I error is called level of significance. It is
represented by α or p. Level of significance is probability of rejecting
null hypothesis when it is true. In health sciences, we generally
consider the level of significance at either 1%(.01)or 5%(.05).A
significance level of .05 means that the researcher is willing to take
the risk of being wrong 5% times ,or 5 times out of 100,when
rejecting the null hypothesis.
18. Contd
3.Confidence interval(CI):
It is a range of values that with a specified degree of probability is
thought to contain the population value.CI contains a lower and an
upper limit.
4.Degree of Freedom:
The interpretation of a statistical test depends on the degree of
freedom. It is denoted by the abbreviation df and a number(e.g.
df=3)Although degree of freedom indicates the number of values that
can vary, the focus is actually on the number of values that are not
free to vary.
19. Contd.
5.Test of significance: There are several parametric(t-test, Z-test,
ANOVA) and nonparametric tests(chi-square test, median test,
McNemar’s test, Mann-Whitney test,Wilcoxon test, Fisher's exact
test)available to establish the statistical significance.
20. 4.Interpretation of Data
• It refers to the critical examination of the analysed study results to
draw inferences and conclusions. Interpretation of the research
findings of a study involves a search for their meaning in relation to
the research problem, objectives, conceptual framework, and
hypotheses.
21. Strategiesforeffectiveinterpretations:
• Interpretation must be made in light of research problem, objectives,
conceptual framework, and hypotheses, and assumptions.
• Critical examination of each element of study results before framing the
interpretations
• Careful consideration and recognition of the limitations of the research
study so that inappropriate interpretation can be avoided.
• Interpretations must be based on the study results only, so that chances of
misinterpretations or over interpretations of the unstudied facts can be
avoided.
• Each part, aspect, and segment of the analysed result must receive close
attention, so that misinterpretation can be avoided.
22. PARAMETRICTESTS
• These tests are also known as normal distribution statistical tests.
• The statistical methods of inference make certain assumptions about
the populations from which the samples are drawn.
• Parametric tests are the type of inferential statistic tests, which
assume that data have come from a type of normal and makes
inferences about the parameters distribution.
23. Commonly Used Parametric Tests.
Paired t-tests: It is used to compare two quantitative measurements
taken from the same group individuals
Unpaired t-test: It is used to compare means between two
distinct/independent groups.
Z-test: It is used to compare the differences in population mean and a
sample mean or the difference between two independent sample
means
24. • One way ANOVA- It is used to compare means between three or
more distinct/independent groups but may be used for more than
two repeat measures of same group.
• Pearson coefficient of correlation: It is used to estimate the degree of
relationship/association between two quantitative variables.
25. NONPARAMETRICTESTS
• Many times in the observation presented in numerical figures, the
scale of measurements may not be really numerical, such as grading
bedsores, or ranks given to analgesic drug’s effectiveness in cancer
pain management.
• In these situations, parametric tests may not be suitable, and a
researcher may need different types of tests to draw inferences;
those tests are known nonparametric tests.
26. Commonly Used Nonparametric Tests.
• Chi-square test: It is used to find out the association between two
nominal or ordinal sets of data/variables
• The sign test: It is used as an alternative test to t-test where median
is compared rather mean.
• Median test: It is used to test the null hypothesis that two
independent samples have drawn from populations with equal
median.
• Mann-Whitney test: The median test do not make full use of all the
information measured on ordinary scale, therefore, Mann Whitney
test is used for better use of data.
27. • Wilcoxon signed rank test: If a small size sample(n<30) is drawn from
a grossly non-normally distributed population and t-test and Z-test
cannot be applied, then a best alternative non-parametric test is
Wilcoxon signed rank test; because sign test may be used when data
consists of a single sample or have paired data.
• Spearman’s rank correlation: A nonparametric test used to estimate
degree of correlation between two variables measured on ordinal
scale.
28. • The key difference between parametric and nonparametric
test is that the parametric test relies on statistical distributions
in data whereas nonparametric do not depend on any
distribution. Non-parametric does not make any assumptions
and measures the central tendency with the median value.
29. • Mean
• the sum of all measurements divided by the number of observations in the
data set.
• Median
• the middle value that separates the higher half from the lower half of the
data set. The median and the mode are the only measures of central
tendency that can be used for ordinal data, in which values are ranked
relative to each other but are not measured absolutely.
• Mode
• the most frequent value in the data set. This is the only central tendency
measure that can be used with nominal data, which have purely qualitative
category assignments
30.
31. Presentation of Data:
• The final steps of research process are very important. The
presentation can be both in a narrative form and in tables.
• Narrative presentation: The presentation should be clear & concise
as much attention is paid to data that fail to support a particular
study hypothesis as is given to data that support a hypothesis.
Certain information should always be included in the text when
discussing the study hypothesis. The statistical test that was used, the
best result, degrees of freedom & the probability value should be
listed.
32. • Tables: They are a means of organizing data so they may be more
easily understood & interpreted. The discussion of the table should
be as clear as possible in the text. If a table is being used to present
the results of hypothesis testing, the results should be placed in the
table or a footnote added that provides the tests results, degrees of
freedom & the probability level.
33.
34.
35. Interpretation of Data:
• It is the task of drawing conclusions or inferences and of explaining
their significance, after careful analysis of the collected data.
• The process of interpretation is essentially one of stating that what
the findings show.
• The findings of the study are the results, conclusions, interpretations
recommendations, generalisations, implications.
• Interpretation is by no means a mechanical process
• It calls for critical examination of the results of one’s analysis in thr
light of all limitations of data gathering.