This document provides an overview of how to use SPSS to conduct basic statistical analysis and present results. It outlines expectations for the workshop, including learning how to prepare an SPSS file, display and summarize data, and create graphical presentations. The document then covers key SPSS concepts like variables, data types, and examples. It also demonstrates how to perform descriptive statistics, frequency tables, crosstabs, measures of central tendency and dispersion. Finally, it discusses different methods of graphical presentation in SPSS like bar charts, histograms, box plots and more.
univariate and bivariate analysis in spss Subodh Khanal
this slide will help to perform various tests in spss targeting univariate and bivariate analysis along with the way of entering and analyzing multiple responses.
univariate and bivariate analysis in spss Subodh Khanal
this slide will help to perform various tests in spss targeting univariate and bivariate analysis along with the way of entering and analyzing multiple responses.
Exploratory Data Analysis for Biotechnology and Pharmaceutical SciencesParag Shah
This presentation will give perfect understanding of data, data types, level of measurements, exploratory data analysis and more importantly, when to use which type of summary statistics and graphs
Brief overview of basic statistics which migh be useful for MD (Paedatrics -Part 1)
Please note that some images and slides taken from the internet behalf of the readers to have a clear picture.
This presentation was intended for employees of Dubai Municipality. It is about how to use SPSS and other statistical data analysis tools like Excel and Minitab in data analysis. The course presented some statistical concepts and definitions.
Exploratory Data Analysis for Biotechnology and Pharmaceutical SciencesParag Shah
This presentation will give perfect understanding of data, data types, level of measurements, exploratory data analysis and more importantly, when to use which type of summary statistics and graphs
Brief overview of basic statistics which migh be useful for MD (Paedatrics -Part 1)
Please note that some images and slides taken from the internet behalf of the readers to have a clear picture.
This presentation was intended for employees of Dubai Municipality. It is about how to use SPSS and other statistical data analysis tools like Excel and Minitab in data analysis. The course presented some statistical concepts and definitions.
Need a nonplagiarised paper and a form completed by 1006015 before.docxlea6nklmattu
Need a nonplagiarised paper and a form completed by 10/06/015 before 7:00pm. I have attached the documents along the rubics that must be followed.
Coyne and Messina Articles, Part 2 Statistical Assessment
Details:
1) Write a paper of 1,000-1,250 words regarding the statistical significance of outcomes as presented in Messina's, et al. article "The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals."
2) Assess the appropriateness of the statistics used by referring to the chart presented in the Module 4 lecture and the resource "Statistical Assessment."
3) Discuss the value of statistical significance vs. pragmatic usefulness.
4) Prepare this assignment according to the APA guidelines found in the APA Style Guide located in the Student Success Center. An abstract is not required.
5) This assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations for successful completion of the assignment.
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
·
The research question itself
·
The sample size
·
The type of data you have collected
·
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generaliza.
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.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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).
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.
4. ILOs
To describe how to use SPSS for conducting basicstatistical analysis
and interpret the output of the analysis.
Preparing SPSS file for data entry
Displaying data
Methods of presenting and Summarizing data
Graphical presentation of data
By the end of this workshop you will be able to:
االهداف
8. Variable , Data
• Variables :متغيرات
These are observations, which vary from one person to
another or from one group of members to others as: age,
weight, blood pressure, sex.
• Data البيانات: Value of the variable.
• Body weight= 70 KG
9. Example:
You are conducting a research to see if proper
infection control training for nurses decreases
prevalence of needle stick injury.
Dependent variable (DV): Decreases prevalence
of needle stick injury.
Independent variable (ID): proper infection
control training
10.
11. Data
1.Quantitative (numerical):
Continuous quantitative: e.g. age, weight, height
Discrete quantitative: e.g. no of patients, number of
children per family.
2.Qualitative (non numerical)/categorigcal:
Nominal qualitative: e.g.
(blood grouping A, B, AB, O),(sex: male and female)
Ordinal qualitative: e.g. (mild, moderate, severe)
12. An icon next to each variable provides information about data type
Scale (Continuous)
Ordinal
Nominal
13. Session 1 cont.:
How to design questionnaire in SPSS
How to enter data in SPSS
•SPSS Windows
Variable view
Data view
• Coding and Design questionnaire in SPSS
• Data entry
Copy, paste from excel into SPSS
From SPSS, open existing excel file
Create new file directly from SPSS
18. Practical training 1: (10 min.)
•Please each one design the questionnaire with
you on SPSS and enter data
19. N.B How to Code questions with more than one answer
What are risks, adverse effects do you know of oral Isotretinoin therapy? (You can
choose more than one answer):
1. Teratogenicity
2. Dryness
3. Constipation
4. Lipid profile disturbance
5. Hepatic side effects
6. Depression
7. Anemia
8. Others………………….
9. I don’t know
Each item in answer enter as a separate
variable with yes, no answer
20. How to Code open ended questions
• Read through responses
• Create a preliminary code based on
responses
• Put responses into category and code it
• Try not to have more than 10 categories,
with no individual category receiving less
than 5% of responses.
• Also, there is software that can be used to
help you code open-ended responses.
21. Session 2:
How to perform data cleaning/manipulation?
Use data set 1
• Data files are not always organized to meet specific user needs, user
may need to select specific group, split data file into separate group
for analysis
• Copy, paste: age, duration, HbA1C
From Data menu
1. Select case: first 20 cases, male only, Saudi, age <45ys
2. Split file: by sex, nationality
3. Sort cases: duration, HbA1C (ascending, descending)
From SPSS dialog box, go to:
Data
Select cases, Sort cases,
Split file
22. Practical training 2
Data transformation
Use data set 1
From transform menu
1. Recode into different variable:
Age: from number to groups (1- <25y, 2- ≥25y)
Duration 1- <5, 2- ≥5
HBA1C 1- <6.5, 2- ≥6.5
1. Recode into same variable
2. Compute variable: Mean, %, BMI ضرورى االقواس
From SPSS dialog box, go to:
Transform
Recode
Into Same variables
Into different variables
25. Session 3:
Descriptive statistics, Data presentation
• After data entry, it can be analyzed using descriptive statistics
Purpose:
• To find wrong entries, have basic knowledge on the sample,
summarize data
•Tabular presentation
Frequency analysis (simple frequency table)
Crosstabs (2 X 2 table and C x R)
26. 1- Simple frequency table
From the menu choose:
Analyze > Descriptive Statistics > Frequencies...
e.g. (sex, nationality, compliance and comments)
28. So you can do frequency to filter data, detect
missed one
Missed data
Missing Values for a Numeric Variable
• Type 999 in the Value field.
Missing Values for a String Variable
• Type NR (no response) in the Value field.
A value is missing may be important to your analysis. For example, you may
find it useful to distinguish between those respondents, and non respondents
29. Characteristics of good table:
1. Simple
2. Self explanatory
3- Explaining abbreviation
4- Columns and rows labeled clearly
5- Unites of measures should be written
6- Title: Every table should have a title, above the
table, which is clear and answers, as possible as you
can, four questions
(what, who, where and when).
30. 2- Crosstabs
• Crosstabs are used to examine the relationship between two
variables Analyze > Descriptive Statistics >crosstabs
• e.g. 2x2 (sex & nationality)
33. Odds Ratio (OR)
• The odds ratio is the odds of outcome occurrence in
one group divided by the odds of outcome
occurrence in the comparison group
• Analysis of case-control studies
• If the OR = 1 there is no difference between the two
groups
• If the OR >1 this exposure is risk factor for
occurrence of disease
• If the OR <1 this exposure is protective factor for
occurrence of disease
Relative risk (RR)
RR indicates how many times those
exposed are likely to develop the disease
relative to non-exposed.
• Analysis of cohort studies
RR= 1: the exposure is not associated with the
disease.
RR > 1: the exposure is a risk
RR < 1: the exposure is a protective
34. Practical training 3:
•Using data set 1 exercise
•Descriptive statistics: Simple frequency table for
sex, compliance
•Crosstabs: 2 X 2 table: relation between gender and
nationality.
•C x R: Association of compliance to treatment and
gender
44. Practical Training 3
Perform descriptive analysis for the variables:
• Age and HbA1C (mean, median, SD, range, min, max ) and write the
comment on table
45. Percentiles
A percentile or (centile) is the value below which a certain
percentage of observations fall.
For example, the 10th percentile is the value below which 10
percent of the observations may be found.
Often used to compare an individual value with a norm. e.g. physical
growth charts for children e.g. weight for age chart
46. SD, SEM
• SEM measures the variability of the
mean of the sample as an estimate
of the true value of the mean of the
population from which the sample
was drawn
يستخدمالبعضالخطأالمعيارىللمتوسطكأحد
مقاييسالتشتتوهومايعتبرمناألخطاء
الشائعةحيثاليعبرالخطأالمعيارىعنالتباين
والعنمدىاالختالفالموجودداخلالبيانات
50. How to present data by
graph ?
Graphical
Qualitative
Bar
Pie
Quantitative
Histogram
Frequency polygon
Box plot
51. Bar chart
• This type of graph is suitable to represent data of the two subtypes of
qualitative and quantitative discrete type
• Analyze> descriptive statistics> frequency> chart> bar chart
• Graphs > legacy dialogs>bar
52. Types of bar charts:
Simple bar chart
Multiple/grouped bar chart
Segmented/stacked bar chart
54. Box plot ( often called box and whisker plot)
• This is a vertical or horizontal rectangle, with the
ends of the rectangle corresponding to the upper
and lower quartiles of the data values.
• A line drawn through the rectangle corresponds
to the median value.
• Whiskers, starting at the ends of the rectangle,
usually indicate minimum and maximum values.
54
58. Pareto chart (Analyze> quality control> pareto)
• Is a vertical bar graph in which values
are plotted in decreasing order of
relative frequency from left to right.
• Is one of the seven basic tools of quality
control. Useful for analyzing what
problems need attention first.
• Pareto principle (80/20 rule), is a theory
maintaining that 80 percent of the
output from a given situation or system
is determined by 20 percent of the
input.
• Pareto chart guiding how to solve 80%
of the problem.
59. Quiz: which cause could solve 80% of problem
Pareto chart ranking perceived problems of food service providers at
dietary department
60. Area chart
• A way to quickly and easily
visualize how well the students in
your class were doing over the
course of the year.
• A way to show the average exam
scores throughout the course on
an area chart.
• Show a trend over time
61. Practical 3:
• Using data set 2 exercise to make:
• Pie graph for sex , Bar chart for compliance
• Bar graph for compliance with sex
• Bar chart for compliance of females only
• Histogram of age, duration
• Histogram of female height/ male height
• Box plot for age
62. References
• SPSS for the Classroom: the Basics
https://www.ssc.wisc.edu/sscc/pubs/spss/classintro/spss_stud
ents1.htm
• California state university. IBM SPSS statistics 20. part 1
descriptive statistics.
• IBM SPSS Statistics 20 Brief Guide.
• www. spsstests.com.
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