Objective of this Project is to analyze noise free ECG signal and to discriminate the arrhythmia data from normal sinus rhythm data using Teger Energy Operator. ECG data is characterized by its nonlinear dynamic behavior, which shows significant changes between normal and arrhythmia data.
Brent Summers, Director of Marketing at Digital Telepathy Using Data and Design toDrive Your Business June 25, 2015
Data is All Around You 1
Quantitative Data Sales Reports Data is All Around
Quantitative Data Application Performance Data Data is All Around You Quantitative Data Search Engine Optimization Data is All Around
Quantitative Web Analytics Data is All Around You
Qualitative Data Customer Surveys Data is All Around You Qualitative Data Customer Interviews Data is All Around You Get more info at: goo.gl/Jeol7v
Qualitative Data Personas Data is All Around You Get more info at: goo.gl/UW8mgQ
Observation Heat Mapping & Scroll Mapping Data is All Around You Observation User Behavior Data is All Around You
Data Already 
 Informs Design 2
A/B Testing Optimize for conversions. Data Already Informs Design
Eye Tracking People read in F-Shaped Pa erns Data Already Informs Design
Eye Tracking People look where people look. Data Already Informs Design h
Vertical Rhythm There’s a reason paper is ruled. Data Already Informs Design
Color Psychology What does your brand color say about your business?
The Golden Ratio 1.618 —
Consider the Entire 
 User Journey 3
Identify the Friction Evaluate sentiment/friction at each stage of the user journey. Consider the Entire User Journey
Designing for
 Business Objectives 4
Identify the Friction Where can you make the biggest impact? Designing for Business Objectives
User Journey Consideration
Landing Pages Incremental improvements can drive exponential results.
Be er Social Sharing Social sharing + content performance insights.
Animations Scroll is the new click.
Change Language Try different value proposition, calls to action, etc.
Change Layout Use behavior patterns to drive decisions.
User Journey Conversion: The act of purchasing a product or service through self service or a sales process.
Content Marketing Share knowledge to establish trust. Onboarding Step-by-step walkthroughs for new users.
Get the First Click Break through psychological barriers. User Journey Retention: Post-purchase. Activities that drive further product engagement, adoption and upgrades. Designing for Business Objectives
Reduce cognitive load: hide data until a user requests it.
Simplify your user interface for experienced users
Testimonials “Who doesn’t love social proof?” - Brent Summers
Prioritizing Your Backlog
Keep Track of Experiments Practical Advice Use a formula to assess which experiments to do first.
Sample Experiments Which of these experiments should be implemented Paid conversions
What does the data tell you? Identify where can design make the biggest impact.
Rounding Out the Process Your implementation method is unique. Measure the results. Repeat.
Measuring Success 6
Good Design is Great for Business Design lead firms out-perform the S&P 500 by 228%. Measuring Success
First presented at the MSUG Conference on June 4, 2015, this presentation discusses concepts and tools to add to your logistic regression modeling practice and also how to use these concepts and tools.
The presentation is design to provide answer to the very basic question "What is Business Analysis?", it is designed to guide the professionals who want to enter into BA profession or have started working as BA's.
Discover The Top 10 Types Of Colleagues Around YouAnkur Tandon
The best part being with different colleagues is we learn a lot from them. Good or bad, sooner or later, better or best, we learn something unique from the different personalities working with and around us at our workplace. Read more interesting content, at www.thecareermuse.co.in - We intend to inform and inspire recruiters, job seekers and anyone with an interest in the workplace and HR technology.
Hope you enjoyed reading the Infographic.
Feel free to share your feedback with us at @CareerBuilderIn
Objective of this Project is to analyze noise free ECG signal and to discriminate the arrhythmia data from normal sinus rhythm data using Teger Energy Operator. ECG data is characterized by its nonlinear dynamic behavior, which shows significant changes between normal and arrhythmia data.
Brent Summers, Director of Marketing at Digital Telepathy Using Data and Design toDrive Your Business June 25, 2015
Data is All Around You 1
Quantitative Data Sales Reports Data is All Around
Quantitative Data Application Performance Data Data is All Around You Quantitative Data Search Engine Optimization Data is All Around
Quantitative Web Analytics Data is All Around You
Qualitative Data Customer Surveys Data is All Around You Qualitative Data Customer Interviews Data is All Around You Get more info at: goo.gl/Jeol7v
Qualitative Data Personas Data is All Around You Get more info at: goo.gl/UW8mgQ
Observation Heat Mapping & Scroll Mapping Data is All Around You Observation User Behavior Data is All Around You
Data Already 
 Informs Design 2
A/B Testing Optimize for conversions. Data Already Informs Design
Eye Tracking People read in F-Shaped Pa erns Data Already Informs Design
Eye Tracking People look where people look. Data Already Informs Design h
Vertical Rhythm There’s a reason paper is ruled. Data Already Informs Design
Color Psychology What does your brand color say about your business?
The Golden Ratio 1.618 —
Consider the Entire 
 User Journey 3
Identify the Friction Evaluate sentiment/friction at each stage of the user journey. Consider the Entire User Journey
Designing for
 Business Objectives 4
Identify the Friction Where can you make the biggest impact? Designing for Business Objectives
User Journey Consideration
Landing Pages Incremental improvements can drive exponential results.
Be er Social Sharing Social sharing + content performance insights.
Animations Scroll is the new click.
Change Language Try different value proposition, calls to action, etc.
Change Layout Use behavior patterns to drive decisions.
User Journey Conversion: The act of purchasing a product or service through self service or a sales process.
Content Marketing Share knowledge to establish trust. Onboarding Step-by-step walkthroughs for new users.
Get the First Click Break through psychological barriers. User Journey Retention: Post-purchase. Activities that drive further product engagement, adoption and upgrades. Designing for Business Objectives
Reduce cognitive load: hide data until a user requests it.
Simplify your user interface for experienced users
Testimonials “Who doesn’t love social proof?” - Brent Summers
Prioritizing Your Backlog
Keep Track of Experiments Practical Advice Use a formula to assess which experiments to do first.
Sample Experiments Which of these experiments should be implemented Paid conversions
What does the data tell you? Identify where can design make the biggest impact.
Rounding Out the Process Your implementation method is unique. Measure the results. Repeat.
Measuring Success 6
Good Design is Great for Business Design lead firms out-perform the S&P 500 by 228%. Measuring Success
First presented at the MSUG Conference on June 4, 2015, this presentation discusses concepts and tools to add to your logistic regression modeling practice and also how to use these concepts and tools.
The presentation is design to provide answer to the very basic question "What is Business Analysis?", it is designed to guide the professionals who want to enter into BA profession or have started working as BA's.
Discover The Top 10 Types Of Colleagues Around YouAnkur Tandon
The best part being with different colleagues is we learn a lot from them. Good or bad, sooner or later, better or best, we learn something unique from the different personalities working with and around us at our workplace. Read more interesting content, at www.thecareermuse.co.in - We intend to inform and inspire recruiters, job seekers and anyone with an interest in the workplace and HR technology.
Hope you enjoyed reading the Infographic.
Feel free to share your feedback with us at @CareerBuilderIn
(BDT404) Large-Scale ETL Data Flows w/AWS Data Pipeline & DataductAmazon Web Services
"As data volumes grow, managing and scaling data pipelines for ETL and batch processing can be daunting. With more than 13.5 million learners worldwide, hundreds of courses, and thousands of instructors, Coursera manages over a hundred data pipelines for ETL, batch processing, and new product development.
In this session, we dive deep into AWS Data Pipeline and Dataduct, an open source framework built at Coursera to manage pipelines and create reusable patterns to expedite developer productivity. We share the lessons learned during our journey: from basic ETL processes, such as loading data from Amazon RDS to Amazon Redshift, to more sophisticated pipelines to power recommendation engines and search services.
Attendees learn:
Do's and don’ts of Data Pipeline
Using Dataduct to streamline your data pipelines
How to use Data Pipeline to power other data products, such as recommendation systems
What’s next for Dataduct"
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
Habits at Work - Merci Victoria Grace, Growth, Slack - 2016 Habit SummitHabit Summit
Presented at the 2016 Habit Summit at Stanford (see: www.HabitSummit.com)
Merci Victoria Grace leads the Growth team at Slack.
Prior to joining Slack, she started a venture-backed game company, designed The Sims Social at Electronic Arts, and worked at a range of consumer, mobile and enterprise startups.
Here she shares insights on putting "Habits to Work at Work".
7 Tips to Beautiful PowerPoint by @itseugenecEugene Cheng
Short talk about presentations given at Startup Dynamo, a workshop held by Startup@Singapore NUS using the Learn Startup Methodology.
My segment was on Presentation Design to make an impact on VCs. Many thanks to @ryanlou for the invite. And not to forget Emiland De Cubber for his amazing slide deck inspirations and invaluable advice. Disclaimer: this is a reimagination off some of Emiland's presentations. I do not make any money of this.
Download for just a tweet: http://goo.gl/fbM4j
Want something similar done for your next pitch? Contact me at my site: http://itseugene.me/contact/
Conducted Regression Analysis to study the relationship between Horsepower, Displacement, Cylinders, Acceleration on Miles Per Gallon (Mpg).
Performed Multiple Transformations (Log Transformations, Dummy Variables) and found out that the Adjusted R-Squared improved with each model.
Conducted heteroskedasticity checks and corrected the heteroskedasticity problem using robust standard errors.
1Create a correlation table for the variables in our data set. (Us.docxjeanettehully
1
Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)
a. Interpret the results.
What variables seem to be important in seeing if we pay males and females equally for equal work?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample
(Mid,
age, ees, sr, raise, and deg variables.) (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Ha: The regression coefficient for each variable is significant
Sal
The analysis used Sal as the y (dependent variable) and
SUMMARY OUTPUT
mid, age, ees, sr, g, raise, and deg as the dependent
variables (entered as a range).
Regression Statistics
Multiple R
0.99215498
R Square
0.9843715
Adjusted R Square
0.98176675
Standard Error
2.59277631
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
7
17783.7
2540.52
377.914
8.44043E-36
Residual
42
282.345
6.72249
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-4.009
3.775
-1.062
0.294
-11.627
3.609
-11.627
3.609
Mid
1.220
0.030
40.674
0.000
1.159
1.280
1.159
1.280
Age
0.029
0.067
0.439
0.663
-0.105
0.164
-0.105
0.164
EES
-0.096
0.047
-2.020
0.050
-0.191
0.000
-0.191
0.000
SR
-0.074
0.084
-0.876
0.386
-0.244
0.096
-0.244
0.096
G
2.552
0.847
3.012
0.004
0.842
4.261
0.842
4.261
Raise
0.834
0.643
1.299
0.201
-0.462
2.131
-0.462
2.131
Deg
1.002
0.744
1.347
0.185
-0.500
2.504
-0.500
2.504
Interpretation:
Do you reject or not reject the regression null hypothesis?
Do you reject or not reject the null hypothesis for each variable?
What is the regression equation, using only significant variables if any exist?
What does result tell us about equal pay for equal work for males and females?
3
Perform a regression analysis using compa as the dependent variable and the same independent
variables as used in question 2.
Show the result, and interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis statements.
4
Based on all of your results to date, is gender a factor in the pay practices of this company?
Why or why not?
Which is the best variable to use in analyzing pay practices - salary or compa?
Why?
.
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
These are slides I use when teaching my second year undergraduate statistics course. They are designed more for conceptual understanding, and do not have syntax for programs like SPSS or R. So it is a more conceptual and mathematical review, rather than a "how-to" computer guide.
(BDT404) Large-Scale ETL Data Flows w/AWS Data Pipeline & DataductAmazon Web Services
"As data volumes grow, managing and scaling data pipelines for ETL and batch processing can be daunting. With more than 13.5 million learners worldwide, hundreds of courses, and thousands of instructors, Coursera manages over a hundred data pipelines for ETL, batch processing, and new product development.
In this session, we dive deep into AWS Data Pipeline and Dataduct, an open source framework built at Coursera to manage pipelines and create reusable patterns to expedite developer productivity. We share the lessons learned during our journey: from basic ETL processes, such as loading data from Amazon RDS to Amazon Redshift, to more sophisticated pipelines to power recommendation engines and search services.
Attendees learn:
Do's and don’ts of Data Pipeline
Using Dataduct to streamline your data pipelines
How to use Data Pipeline to power other data products, such as recommendation systems
What’s next for Dataduct"
This presenation explains basics of ETL (Extract-Transform-Load) concept in relation to such data solutions as data warehousing, data migration, or data integration. CloverETL is presented closely as an example of enterprise ETL tool. It also covers typical phases of data integration projects.
Habits at Work - Merci Victoria Grace, Growth, Slack - 2016 Habit SummitHabit Summit
Presented at the 2016 Habit Summit at Stanford (see: www.HabitSummit.com)
Merci Victoria Grace leads the Growth team at Slack.
Prior to joining Slack, she started a venture-backed game company, designed The Sims Social at Electronic Arts, and worked at a range of consumer, mobile and enterprise startups.
Here she shares insights on putting "Habits to Work at Work".
7 Tips to Beautiful PowerPoint by @itseugenecEugene Cheng
Short talk about presentations given at Startup Dynamo, a workshop held by Startup@Singapore NUS using the Learn Startup Methodology.
My segment was on Presentation Design to make an impact on VCs. Many thanks to @ryanlou for the invite. And not to forget Emiland De Cubber for his amazing slide deck inspirations and invaluable advice. Disclaimer: this is a reimagination off some of Emiland's presentations. I do not make any money of this.
Download for just a tweet: http://goo.gl/fbM4j
Want something similar done for your next pitch? Contact me at my site: http://itseugene.me/contact/
Conducted Regression Analysis to study the relationship between Horsepower, Displacement, Cylinders, Acceleration on Miles Per Gallon (Mpg).
Performed Multiple Transformations (Log Transformations, Dummy Variables) and found out that the Adjusted R-Squared improved with each model.
Conducted heteroskedasticity checks and corrected the heteroskedasticity problem using robust standard errors.
1Create a correlation table for the variables in our data set. (Us.docxjeanettehully
1
Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)
a. Interpret the results.
What variables seem to be important in seeing if we pay males and females equally for equal work?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample
(Mid,
age, ees, sr, raise, and deg variables.) (Note: since salary and compa are different ways of
expressing an employee’s salary, we do not want to have both used in the same regression.)
Ho: The regression equation is not significant.
Ha: The regression equation is significant.
Ho: The regression coefficient for each variable is not significant
Ha: The regression coefficient for each variable is significant
Sal
The analysis used Sal as the y (dependent variable) and
SUMMARY OUTPUT
mid, age, ees, sr, g, raise, and deg as the dependent
variables (entered as a range).
Regression Statistics
Multiple R
0.99215498
R Square
0.9843715
Adjusted R Square
0.98176675
Standard Error
2.59277631
Observations
50
ANOVA
df
SS
MS
F
Significance F
Regression
7
17783.7
2540.52
377.914
8.44043E-36
Residual
42
282.345
6.72249
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-4.009
3.775
-1.062
0.294
-11.627
3.609
-11.627
3.609
Mid
1.220
0.030
40.674
0.000
1.159
1.280
1.159
1.280
Age
0.029
0.067
0.439
0.663
-0.105
0.164
-0.105
0.164
EES
-0.096
0.047
-2.020
0.050
-0.191
0.000
-0.191
0.000
SR
-0.074
0.084
-0.876
0.386
-0.244
0.096
-0.244
0.096
G
2.552
0.847
3.012
0.004
0.842
4.261
0.842
4.261
Raise
0.834
0.643
1.299
0.201
-0.462
2.131
-0.462
2.131
Deg
1.002
0.744
1.347
0.185
-0.500
2.504
-0.500
2.504
Interpretation:
Do you reject or not reject the regression null hypothesis?
Do you reject or not reject the null hypothesis for each variable?
What is the regression equation, using only significant variables if any exist?
What does result tell us about equal pay for equal work for males and females?
3
Perform a regression analysis using compa as the dependent variable and the same independent
variables as used in question 2.
Show the result, and interpret your findings by answering the same questions.
Note: be sure to include the appropriate hypothesis statements.
4
Based on all of your results to date, is gender a factor in the pay practices of this company?
Why or why not?
Which is the best variable to use in analyzing pay practices - salary or compa?
Why?
.
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
These are slides I use when teaching my second year undergraduate statistics course. They are designed more for conceptual understanding, and do not have syntax for programs like SPSS or R. So it is a more conceptual and mathematical review, rather than a "how-to" computer guide.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
15. What is the p of success or failure? Failure Success Total 1 - p p (1 - p ) + p = 1
16. What is the p of success or failure? Failure Success Total 250 750 = 1000
17. What is the p of success or failure? Failure Success Total 250/1000 750/1000 = 1000/1000
18. What is the p of success? Failure Success Total .25 .75 1
19. What is the p of success? Failure Success Total .25 = 1 - p .75 = p 1 = (1 - p ) + p
20.
21.
22.
23. HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES Group Failure Success Total A (Male) 182 368 550 B (Female) 75 375 450 250 750 1000
24. HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES Group Failure Success Total A (Male) 182/550 368/550 550/500 B (Female) 75/450 375/450 450/450 250 750 1000
25. HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES Group Failure Success Total A (Male) .33 .67 1 B (Female) .17 83 1 250 750 1000
26. HOW CAN WE COMPARE THE ODDS (Ω) OF MALES VERSUS FEMALES Group Failure Success Total A (Male) (1 - p A ) = .33 p A = .67 1 B (Female) (1 - p B ) = .17 p B = .83 1 250 750 1000
![LOGISTIC REGRESSION ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)