Capital Asset Pricing Model, CAPM Assumptions, Borrowing and Lending Possibilities, Risk-Free Lending, Borrowing Possibilities, The New Efficient Set, Portfolio Choice, Market Portfolio, Characteristics of the Market Portfolio, Capital Market Line, The Separation Theorem, Security Market Line, CAPM’s Expected Return-Beta Relationship, How Accurate Are Beta Estimates?,
Capital Asset Pricing Model, CAPM Assumptions, Borrowing and Lending Possibilities, Risk-Free Lending, Borrowing Possibilities, The New Efficient Set, Portfolio Choice, Market Portfolio, Characteristics of the Market Portfolio, Capital Market Line, The Separation Theorem, Security Market Line, CAPM’s Expected Return-Beta Relationship, How Accurate Are Beta Estimates?,
Binary outcome models are widely used in many real world application. We can used Probit and Logit models to analysis this type of data. Specially, dose response data can be analyze using these two models.
Risk Return Trade Off PowerPoint Presentation SlidesSlideTeam
Presenting this set of slides with name - Risk Return Trade Off Powerpoint Presentation Slides. This deck consists of total of twenty nine slides. It has PPT slides highlighting important topics of Risk Return Trade Off Powerpoint Presentation Slides. This deck comprises of amazing visuals with thoroughly researched content. Each template is well crafted and designed by our PowerPoint experts. Our designers have included all the necessary PowerPoint layouts in this deck. From icons to graphs, this PPT deck has it all. The best part is that these templates are easily customizable. Just click the DOWNLOAD button shown below. Edit the colour, text, font size, add or delete the content as per the requirement. Download this deck now and engage your audience with this ready made presentation.
Ragui Assaad- University of Minnesota
Caroline Krafft- ST. Catherine University
ERF Training on Applied Micro-Econometrics and Public Policy Evaluation
Cairo, Egypt July 25-27, 2016
www.erf.org.eg
A good beginners overview of wireframes.
• Why wireframe?
• Types of wireframes
• Where do you start?
• Interactive wireframes/ Prototyping
• Tools
• Wireframe workshop
Binary outcome models are widely used in many real world application. We can used Probit and Logit models to analysis this type of data. Specially, dose response data can be analyze using these two models.
Risk Return Trade Off PowerPoint Presentation SlidesSlideTeam
Presenting this set of slides with name - Risk Return Trade Off Powerpoint Presentation Slides. This deck consists of total of twenty nine slides. It has PPT slides highlighting important topics of Risk Return Trade Off Powerpoint Presentation Slides. This deck comprises of amazing visuals with thoroughly researched content. Each template is well crafted and designed by our PowerPoint experts. Our designers have included all the necessary PowerPoint layouts in this deck. From icons to graphs, this PPT deck has it all. The best part is that these templates are easily customizable. Just click the DOWNLOAD button shown below. Edit the colour, text, font size, add or delete the content as per the requirement. Download this deck now and engage your audience with this ready made presentation.
Ragui Assaad- University of Minnesota
Caroline Krafft- ST. Catherine University
ERF Training on Applied Micro-Econometrics and Public Policy Evaluation
Cairo, Egypt July 25-27, 2016
www.erf.org.eg
A good beginners overview of wireframes.
• Why wireframe?
• Types of wireframes
• Where do you start?
• Interactive wireframes/ Prototyping
• Tools
• Wireframe workshop
View how"out-of-the-box" thinkers David Skok and Mike Volpe define an optimized sales and marketing funnel; and describe how to identify problems and create long-lasting solutions for your organization during this complimentary one-hour online training session: http://www.hubspot.com/webinars/optimize-the-sales-and-marketing-funnel/
Google Analytics Fundamentals: Set Up and Basics for MeasurementOrbit Media Studios
Once you have Google Analytics installed, here are the steps for setting things up and doing basic analysis on your website. This presentation covers:
• Setting up goals
• Adding filters to remove traffic from yourself
• Excluding traffic from bots and spiders
• Setting up "site search" reports
• Annotations and shortcuts
We also jump into the basic report configurations, including secondary dimensions, advanced filters and views. This covers most of what you need to know to start getting value from Google Analytics!
Using Your Growth Model to Drive Smarter High Tempo TestingSean Ellis
In this presentation, Sean Ellis highlights how to use a growth model to inform your high tempo testing efforts. It goes through the key steps for building your growth model including establishing a north star metric, and identifying your "aha moment" and the core benefit that drives retention. Finally he shows how the GrowthHackers team has used a growth model to plan our growth roadmap.
Lean Community Building: Getting the Most Bang for Your Time & MoneyJennifer Lopez
You want to grow your organization's community, but that simply takes more time, money, and general people power than you have access to. Jen walks you through some ways to grow and focus on your community while on a small budget, with limited resources. You'll walk away with tools and tips to help you on your way to community bliss.
10 Mobile Marketing Campaigns That Went Viral and Made MillionsMark Fidelman
How do the best companies and agencies create effective mobile marketing campaigns that have high ROI and awareness? What are the best tools out there for you to use when trying to reach your target audience on mobile? Mobile marketing is becoming an indispensable solution to create awareness, drive sales, and entice users to act. But where do you start? How do you measure success? I'll cover how the best are doing it and reveal their secrets to you for the first time.
We’re going to cover best practices that top brands use to drive referrals and customer acquisition:
The following best practice examples come from companies that actually sell something. !
People always ask: “How do I get more referrals?” ! That’s easy. ! Focus on two things that really move the needle. ! User participation & Optimization
Again, two things really ‘move the needle’ for referral program performance. User Participation Optimization
User Participation Location matters
Homepage & Navigation Best practices: ! • Site-wide • Highly visible (upper-le#)
Order Confirmation Page Best practices: ! • Pop-up overlay • Varied offers
User Account Page Best practices: ! • Embedded • On button click - if you have to • PURLs
Stand Alone Referral Page Promote / drive traffic from: ! • Homepage & Navigation • Dedicated email blasts • Trigger emails & newsletters • Social following • Customer service follow up • Email signature
Dedicated Email Blasts Best practices: ! • Single call to action • Clear offer • Schedule (monthly / quarterly)
Your ‘reach’ may be bigger than you think…
Optimization Increase sharing rates and referral conversion rates
What to measure Shares ! Widget Impressions ! = Sharing Rate Conversions ! Referral Visits ! = Referral Conversion Rate Referral Visits
Sharing Rate - optimize call to action Best practices: ! • Clear call to action • What’s in it for them • What their friends get (if double-sided) • Address book importing
Sharing Rate - optimize design + 42% sharing rate • Red, italic headline • Background image
A B C • Purple bracelets • Green bracelets • “Expires in 24 hours!” • Woman on beach • “Expires in 24 hours!” + 35% Gain
Doubled referral visits and referral sales
Welcome overlay • Friend incentive • Email capture • Reveal code
Session-based • Dynamic message
Dedicated landing page • No distractions
1 Offers for sharer & friend 2 Calls to action for sharer & friend 3 Shared content (copy & images) 4 Shared email subject line Elements for testing 5 Referred visitor experience
Calls to Action - quick tips Tip 1: Verb + What’s in it for me? Tip 2: “I want to ___________.” • Get $10 in store credit. • Get a free month. • Give $10, Get $10 • Share and earn $10
Referral Benchmarks How are you doing?
Sharing
Referral Visits
Referred Visitors
Cost Per Acquisition CPA
Revenue Lift
Examples of Campaign Metrics Example companies by size
Where to start optimizing (30 day snapshot - 3 companies) a Rockin’ b Work on sharing rate c Work on conversion rate
Party Foul ! The biggest mistake people make . . .
Don’t force a login or registration 90% drop off!
Recap Follow best practices and avoid common pitfalls.
Johnathan is the founder of KlientBoost, a no-nonsense, creative kick-ass PPC agency that hustles for results & ROI. He’s been named the 2015 “Conversion Marketer To Watch” by Unbounce’s readers.
1) Single Keyword Ad Groups 2) Ad Group Level Negatives 3) Multi Intent Keywords 4) The Five Ad Tests 5) Aggressive Ad Testing 6) AdWords is Your Carrot 7) Insane Importance of Design 8) Multi Step Landing Pages 9) Your Landing Page Offer 10) The Price Focus CTA
Single Keyword Ad Groups 1
Google’s advice…
That would mean…
But it should be… keyword 1 keyword 2
What happens to your CTR
Higher search-to-ad relevancy = higher CTR = higher quality scores = lower cpc = lower cost per conversion.
Your new ad group structure =
Ad Group Level Negatives 2
Killing off internal competition
What it means… Ad group = “web analytics” Ad group = “web analytics stripe” Ad group = “web analytics braintree” Ad group = “web analytics paypal”
What it means… Ad group = “web analytics” Ad group level negative keywords - stripe - braintree - paypal
Search terms should look like…
See what’s holding you back
Multi Intent Keywords 3
The Search Buying Cycle
Search Buying Cycle Awareness Consideration Action “broken transmission” “whats my car worth” “sell my car”
The Five Ad Tests 4
Proximity
Source: ThinkWithGoogle.com
Proximity Source: Hanapin Source: Engine Ready
30% increase in conversions
Countdowns 32% CTR boost & 3% conv/rate improvement
Specificity How to Get 6,312 Subscribers to Your Business Blog in One Day How to Get Over 6,000 Subscribers to Your Business Blog in One Day How to Get a Torrent of Subscribers to Your Business Blog in One Day
Specificity 88% CTR boost & 23% conv/rate improvement
Timeliness 217% CTR boost & 23% conv/rate improvement
Aggressive Ad Testing 5
Get Aggressive!
Isolate and label Headline Display URL Description 1 Description 2
Let time pass, then filter
GetDataDriven.com/ab-significance-test
6 AdWords is Just Your Carrot
“Cats are your customers, AdWords is your laser pointer”
7 Insane Importance of Design
How fast do people judge you?
Visual & Aesthetic Judgement Research at Google International Journal of Human-Computer Studies, vol. 70(11) (2012), pp. 794-811 |——————————————| 1 full second 50 ms = 0.05 second
Insane Important of Design 6
8 Multi Step Landing Pages
Single step landing pages are threatening
9 Your Landing Page Offer
Conversion Rate Optimization 101 “What makes a good value proposition? An offer that’s differentiated from your competitors.” — Peep Laja, CRO Expert at ConversionXL
A lot more valuable than your competitors Make your offer
10 The Price Focus CTA
Struggling with CTA ideas? Get Pricing & More Info
32 New Hacks To Get More Phone Leads With AdWords & CRO kboo.st/kiss-phone (60 pages deep!)
The Price Focus CTA landingpages@klientboost.com
As designers and developers, we don’t always have access to research to about our end users, or the opportunity to learn about them. This can leave us building products based on our managers personal opinion, or client specifications, and never really knowing how we can serve our users better.
But the good news is there are many opportunities for user research that most designers and developers just aren’t aware of. They are cheap, easy to implement, and can used straight away on almost any project.
Lily will talk you through 3 methods of no excuse user research that you can use immediately on the websites, products, apps and services you work on every day.
User experience doesn't happen on a screen: It happens in the mind.John Whalen
User experience is a vital component of mission-critical projects. The vast majority of experience is digital. We spend insane amounts of time and money designing UX for websites, apps and products to impress users. But the truth is UX isn’t a singular experience we can define. And it doesn’t happen on a screen – it happens in the mind. More specifically, the six minds.
Discover how UX is truly a collection of experiences occurring across six brain concentrations, each with their own processing styles and ideal states. And how, using psychological principles, you can uncover the conscious and subconscious needs of these six minds to appeal to users on cognitive and emotional levels.
Stop Leaving Money on the Table! Optimizing your Site for Users and RevenueJosh Patrice
Conversion Rate Optimization can and will help you get more leads, convert more users, and make more money. So stop leaving money on the table!
Learn tips, tactics, tools, and techniques to build an actionable plan that will help connect with your users. Through case studies, examples, and best practices learn how to:
Understand the basics of User Psychology
Build basic Personas & Action Paths
The importance of User Experience & Page Design
Leverage analytics data
Easy ways to improve Bounce Rate and Time on Site
Using AIDA as part of your online marketing strategy
Crafting effective Calls to Action
Start A/B testing
Technical marketers are in high demand and low supply. Being able to dive into data on your own, with no help from engineering, makes you a much better marketer.
This is why SQL is so powerful - it allows you to see any data you want about anything your customers do. Knowing how to use SQL is literally a marketing superpower.
In this SQL tutorial specifically for marketers, I've pulled together SQL query basics that any marketer or data analyst will need to dig into their customer analytics. This course is the best resource for marketers, growth hackers and product managers who want to get more technical and learn SQL. It's what I wish existed when I was going through tutorial after tutorial, sifting through lots of information that didn't apply to me and trying to learn on my own.
SQL is simple enough that - just by learning a few concepts I cover above - you'll be able to use it for any kind of data analysis, cohort analysis or campaign breakdown.
Want more information? Check out resources on my blog - http://justinmares.com/sql
Statistics is a mathematical science including methods of collecting, organizing, and analyzing data in such a way that meaningful conclusions can be drawn from them. In general, its investigations and analyses fall into two broad categories called descriptive and inferential statistics.
The future is uncertain. Some events do have a very small probabil.docxoreo10
The future is uncertain. Some events do have a very small probability of happening, like an asteroid destroying the earth. So we accept that tomorrow will come as a certain event. But future demand for a business’s goods and services is very uncertain. Yet, the management of a company wants to have some idea of the survival (or growth) of the company in the future. Should they expect to hire more people or let some go? Should they plan to increase capacity? How much investment is needed for future assets, or should they down size?
Forecasting provides some ideas about the future, but how this is accomplished can vary from company to company. And one key factor is how accurate the forecast is. Generally, the further into the future one looks, the more uncertain the information is. How do forecasters reduce their forecasting errors? How much error is tolerable?
Another key factor in forecasting is data availability. Data processing and storage capability have become extremely available and inexpensive. Software and computing power is also very cheap. Collecting real-time sales data via point-of-sales systems is now common at most retail establishments. But couple this with a situation in companies that have a large number of products, such as a retail store or a large manufacturing company with hundreds or thousands of product numbers and/or product lines, forecasting becomes complicated.
Forecasting Methods
There are two main types or genres of forecasting methods, qualitative and quantitative. The former consists of judgment and analysis of qualitative factors, such as scenario building and scenario analysis. The latter is obviously based on numerical analysis. This genre of forecasting includes such methods as linear regression, time series analysis, and data mining algorithms like CHAID and CART, which are useful especially in the growing world of artificial intelligence and machine learning in business. This module will look at the linear regression and time series analysis using exponential smoothing.
Linear Growth
When using any mathematical model, we have to consider which inputs are reasonable to use. Whenever we extrapolate, or make predictions into the future, we are assuming the model will continue to be valid. There are different types of mathematical model, one of which is linear growth model or algebraic growth model and another is exponential growth model, or geometric growth model. The constant change is the defining characteristic of linear growth. Plotting the values, we can see the values form a straight line, the shape of linear growth.
If a quantity starts at size P0 and grows by d every time period, then the quantity after n time periods can be determined using either of these relations:
Recursive form:
Pn = Pn-1 + d
Explicit form:
Pn = P0 + d n
In this equation, d represents the common difference – the amount that the population changes each time n increases by 1. Calculating values using the explicit form and plot ...
16 USING LINEAR REGRESSION PREDICTING THE FUTURE16 MEDIA LIBRAR.docxhyacinthshackley2629
16 USING LINEAR REGRESSION PREDICTING THE FUTURE
16: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Linear Regression
Lightboard Lecture Video
· Multiple Regression
Time to Practice Video
· Chapter 16: Problem 2
Difficulty Scale
(as hard as they get!)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding how prediction works and how it can be used in the social and behavioral sciences
· Understanding how and why linear regression works when predicting one variable on the basis of another
· Judging the accuracy of predictions
· Understanding how multiple regression works and why it is useful
INTRODUCTION TO LINEAR REGRESSION
You’ve seen it all over the news—concern about obesity and how it affects work and daily life. A set of researchers in Sweden was interested in looking at how well mobility disability and/or obesity predicted job strain and whether social support at work can modify this association. The study included more than 35,000 participants, and differences in job strain mean scores were estimated using linear regression, the exact focus of what we are discussing in this chapter. The results found that level of mobile disability did predict job strain and that social support at work significantly modified the association among job strain, mobile disability, and obesity.
Want to know more? Go to the library or go online …
Norrback, M., De Munter, J., Tynelius, P., Ahlstrom, G., & Rasmussen, F. (2016). The association of mobility disability, weight status and job strain: A cross-sectional study. Scandinavian Journal of Public Health, 44, 311–319.
WHAT IS PREDICTION ALL ABOUT?
Here’s the scoop. Not only can you compute the degree to which two variables are related to one another (by computing a correlation coefficient as we did in Chapter 5), but you can also use these correlations to predict the value of one variable based on the value of another. This is a very special case of how correlations can be used, and it is a very powerful tool for social and behavioral sciences researchers.
The basic idea is to use a set of previously collected data (such as data on variables X and Y), calculate how correlated these variables are with one another, and then use that correlation and the knowledge of X to predict Y. Sound difficult? It’s not really, especially once you see it illustrated.
For example, a researcher collects data on total high school grade point average (GPA) and first-year college GPA for 400 students in their freshman year at the state university. He computes the correlation between the two variables. Then, he uses the techniques you’ll learn about later in this chapter to take a new set of high school GPAs and (knowing the relationship between high school GPA and first-year college GPA from the previous set of students) predict what first-year GPA should be for a new student who is just starting out. Pretty nifty, huh?
Here’s another example. A group of kindergarten teachers is interested in finding out how well ex.
16 USING LINEAR REGRESSION PREDICTING THE FUTURE16 MEDIA LIBRAR.docxnovabroom
16 USING LINEAR REGRESSION PREDICTING THE FUTURE
16: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Linear Regression
Lightboard Lecture Video
· Multiple Regression
Time to Practice Video
· Chapter 16: Problem 2
Difficulty Scale
(as hard as they get!)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Understanding how prediction works and how it can be used in the social and behavioral sciences
· Understanding how and why linear regression works when predicting one variable on the basis of another
· Judging the accuracy of predictions
· Understanding how multiple regression works and why it is useful
INTRODUCTION TO LINEAR REGRESSION
You’ve seen it all over the news—concern about obesity and how it affects work and daily life. A set of researchers in Sweden was interested in looking at how well mobility disability and/or obesity predicted job strain and whether social support at work can modify this association. The study included more than 35,000 participants, and differences in job strain mean scores were estimated using linear regression, the exact focus of what we are discussing in this chapter. The results found that level of mobile disability did predict job strain and that social support at work significantly modified the association among job strain, mobile disability, and obesity.
Want to know more? Go to the library or go online …
Norrback, M., De Munter, J., Tynelius, P., Ahlstrom, G., & Rasmussen, F. (2016). The association of mobility disability, weight status and job strain: A cross-sectional study. Scandinavian Journal of Public Health, 44, 311–319.
WHAT IS PREDICTION ALL ABOUT?
Here’s the scoop. Not only can you compute the degree to which two variables are related to one another (by computing a correlation coefficient as we did in Chapter 5), but you can also use these correlations to predict the value of one variable based on the value of another. This is a very special case of how correlations can be used, and it is a very powerful tool for social and behavioral sciences researchers.
The basic idea is to use a set of previously collected data (such as data on variables X and Y), calculate how correlated these variables are with one another, and then use that correlation and the knowledge of X to predict Y. Sound difficult? It’s not really, especially once you see it illustrated.
For example, a researcher collects data on total high school grade point average (GPA) and first-year college GPA for 400 students in their freshman year at the state university. He computes the correlation between the two variables. Then, he uses the techniques you’ll learn about later in this chapter to take a new set of high school GPAs and (knowing the relationship between high school GPA and first-year college GPA from the previous set of students) predict what first-year GPA should be for a new student who is just starting out. Pretty nifty, huh?
Here’s another example. A group of kindergarten teachers is interested in finding out how well ex.
Relationship between Linear Algebra and StatisticsLinear algebra.docxdebishakespeare
Relationship between Linear Algebra and Statistics
Linear algebra can be regarded as the arithmetic of linear substitution (Edwards, H. M., 1995). Matrices and linear substitutions are effectively the same. Statistics, on the other hand, in a broad sense is the science of collecting, organizing, analyzing and interpreting data. Statistics find applications in education, research, business, health, engineering, athletics, medicine and a lot more of the fields. Typical examples of statistics are those that deal with average rainfall and temperature, birth and death rates, average snowfall, crime rates, political popularity and much more.
Even though statistics is usually studied as a course on its own, understanding basic statistical concepts is requisite for any student pursuing any field of study. This is because the student will be required to conduct research in his own field of study. Hence there will be need to know how to design experiments, gather data, organize, analyze and summarize data to draw conclusions or predictions based on the findings of the research. Statistics are encountered by just about anybody for instance in the magazines, news papers, television and so on. Therefore, basic understanding of statistical vocabulary, procedure and concepts is helpful in avoiding getting mislead by misleading data and information especially when you are a consumer of a product.
Statistics as a field has strong relations and dependence on linear algebra. Descriptive statistics, for instance, uses algebraic summation so often (Frank, H., & Althoen, S. C., 1994). The data of various variables are summed up or the probabilities of events are summed. The key areas in statistics that have a stronger bias in linear algebra or applies linear algebra a lot are: problems in multivariate distributions, integrals and distributions, interdependence properties and characterization of distributions, probability inequalities, orderings, and simulations and much more (Johnson, C. R., & American Mathematical Society, 1990). From the look of these statistical topics it is very clear statistics converge with linear algebra in a lot of occasions. In this paper, I am going to study the linear correlation in statistics and show how it uses linear algebra to achieve its statistical objectives.
Variance and Covariance of a Statistical Data
Variance measures spread or variability in a data set. It is the average of the squared deviations from the mean. The formula is
Where
Covariance is the measure how corresponding elements from two ordered data sets seem to grow in a common direction. The formula for covariance is
Variance-Covariance matrix
This is a matrix which presents variances as diagonal elements and co-variances as off-diagonal elements. Variance-Covariance matrix appears as below.
To create the variance-covariance matrix;
· We transform the row scores from matrix X into deviation score for matrix x as
· Computing x’x
· Divide each term in th ...
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
LA HUG - Video Testimonials with Chynna Morgan - June 2024Lital Barkan
Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
We will dig deeper into:
1. How to capture video testimonials that convert from your audience 🎥
2. How to leverage your testimonials to boost your sales 💲
3. How you can capture more CRM data to understand your audience better through video testimonials. 📊
What is the TDS Return Filing Due Date for FY 2024-25.pdfseoforlegalpillers
It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
VAT Registration Outlined In UAE: Benefits and Requirementsuae taxgpt
Vat Registration is a legal obligation for businesses meeting the threshold requirement, helping companies avoid fines and ramifications. Contact now!
https://viralsocialtrends.com/vat-registration-outlined-in-uae/
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
17. Let’s start with a little bit of definition What is econometrics?
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27. If we plotted the data, we would indeed see an upward trend… Time t, in months Product users ‘000 In the 1 st month, we see that there are about 5’000 product users By the 30 th month, the number of users have increased to about 40’000 users
28.
29. To answer this question… … we need to understand first the past relationship between the two variables – time and numbers of users . We will then use this understanding of the past to predict what’s going to happen in the next 12 months The Past The Future
30. What bridges the gap between the past and the future… Once we have identified the equation or the model, we will have a better grasp of (1) the past trends and (2) the potentials of the future Linear regression comes into the picture by bridging that gap between the past and the future The Past The Future Linear regression equation
31. With that in mind, let’s look at the chart again
32. From mere observation, we see an uptrend in users across time… Time t, in months Product users ‘000
33. How do we quantify* that uptrend? Time t, in months Product users ‘000 * Remember: In order to project into the future, we need to create a model that quantifies the relationship between time and number of users
34. There are an infinite number of lines that we could use to characterize the uptrend… Time t, in months Product users ‘000 Different people have different views – even when viewing the same set of data: I can argue that the best line is the grey line, another can argue that the blue line is best, and still another can argue that the best line is the pink line
35. Linear regression insists that there is one (and only one) line that would best characterize the trend and the relationship between the two variables
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38. Let’s go back a few charts… What OLS does is it objectively goes through these infinite number of lines – and finds the best-fitting line such that the distance between the line and the original data-points are at a minimum OLS does this iteratively – that is, through trial-and-error – until it arrives at the values of m, b, and u that define a line with minimum distance between it and the original data. (Think of OLS as a search-algorithm that tries different m-b-u combinations to achieve the best-fitting line.) Remember: Given any data set, there are an infinite number of lines that can be used to describe the trend. One can choose the “pink” to be the best and rationalize it; another person can argue that the yellow line is the best, and still another third person can defend the blue line. We can argue indefinitely about the merits of each of these infinite number of lines.
39. Going back to the data – the best fitting regression line, after applying OLS is… Time t, in months Product users ‘000
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44. Let’s eyeball the model: There seem to be no data-points that are significantly away from the line… Time t, in months Product users ‘000
45. Eyeballing the data, however, brings back subjective interpretations Time t, in months Product users ‘000 One can argue that point at month 11 is significantly away from the line – and so is data for month 24… We therefore need a more accurate, more objective measurement of “fit”
53. Let’s now project what’s going to happen in the next 12 months… Time t, in months Product users ‘000 At the end of the next 12 months [by month 42], we can expect to have 543’000 users – if all things remain equal
54. Since we don’t really know what’s going to happen in the future – and we don’t have a perfect model… We can report ranges instead of just a line… The dashed lines indicate the range of expectations for the next 12 months We can expect that there will be about 470’000 to 616’000 users by month 42