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Intelligent Network Planning and Optimization Demo
 
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This was a fantastic demo of how advanced AI algorithms are driving sophisticated network analysis and simulation efforts, to predict the optimal connectivity for telecom networks. This tech is already boots-on-the-ground, ready and available for purchase!
Design of Experiments - Overview
 
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Six Sigma by Dr. T. P. Bagchi , Department of Management, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 59493 nptelhrd
How to Create an A/B Testing Plan/Strategy - Conversion Rate Optimization
 
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Overview of how to create a proper AB testing plan. You have properly heard the term "A/B testing" before and think its a great idea, but do you know how to properly build out and execute a strategy. This video give you a quick overview of: 1 - Review analytics data 2 - Determine your KPI’s 3 - Determine what and where to test 4 - Create hypotheses 5 - Prioritize those changes in order of greatest ROI 4 - Build out the test plans (audience, base lines, duration, etc) —————————— Learn more about building, managing and growing your online store today by visiting http://www.commercewisely.com. Follow us on social: Facebook: https://www.facebook.com/Commerce-Wisely-845650992235520/ YouTube: https://www.youtube.com/channel/UCVs-Ohz9qTlM2gVNWJRBJHA Instagram: https://www.instagram.com/commerce_wisely/ —————————— Here are some more helpful video tutorials to manage your store: How to create an online store in less than 24 hours: https://www.youtube.com/watch?v=RxCvACnwsq0 The difference between Magento 1x and 2.0: https://www.youtube.com/watch?v=FeQ1PnFSIXU Website Optimization and User Tracking: https://www.youtube.com/watch?v=bnBgwpo8nG4
Views: 341 commercewisely
Manufacturing Work Cell Optimization: Design, Layout and Cycle Time Analysis
 
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http://www.driveyoursuccess.com This video provides insight into the three essentials of manufacturing work cell optimization, design & layout
Views: 69850 Ian Johnson
Planning and Executing In Vitro siRNA Experiments
 
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Functional analysis by mRNA knockdown using siRNAs is now routine in many molecular biology labs. However, many RNAi experiments fail due to diversion from simple, good practices. This webinar will review the steps leading to successful siRNA experiments, including: -Understanding the target transcript -siRNA selection -Choosing the cell type -Validating the assay -Including appropriate biological controls
modeFRONTIER & RSMs: Exploit Your Data and Speed Up Your Optimization
 
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When working with ‘real’ experiment or simulation data the time taken to measure the outputs can often prohibit the use of direct optimization. In this case modeFRONTIER can help the CAE Analyst train Response Surface models to augment ‘real’ data with ‘virtual’ data, which can generate thousands of design configurations in seconds. This approach unleashes the power of the state-of-the-art optimization algorithms available in modeFRONTIER and makes the discovery of optimal solutions possible. With the new release of modeFRONTIER the training and validation of several RSM algorithms can be automated to find the best predictive metamodel. This simplifies the steps required to run a virtual optimization and allows a ‘one click’ approach to a traditionally manual task.
Views: 1622 Webinar EnginSoft
Applications of Mini Fracs DFIT - Diagnostic FractureInjection Test
 
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For more information visit our website: http://petromgt.com/ Services: 1. Reservoir Studies (Conventional/Simulation) 2. Well Test Planning and Analysis 3. Waterflood Design & Performance Monitoring 4. Production Optimization 5. Performance Evaluation of MFHW’s (PTA, RTA, Numerical) 6. Reserves and Economic Evaluations 7. Complete frac design/optimization (Gohfer/KAPPA software) 8. Government Submissions 9. Customized course contents 10. Expert Witness By: Saad Ibrahim, P. Eng. For more information visit our website: http://petromgt.com/
Views: 8031 PetroMgtGrp
Understanding Design of Experiments (DoE) in Protein Purification (Part 2)
 
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For more, download the free handbook, Design of Experiments in Protein Production and Purification at https://www.gelifesciences.com/solutions/protein-research/knowledge-center/protein-handbooks This 2nd session includes discussion on interpreting and evaluating DoE data.
DOE for Process Optimization
 
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A sneak preview of the Durham & Newcastle workshop on understanding chemical processes.
Views: 176 Dennis Lendrem
Taguchi Methods
 
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Six Sigma by Dr. T. P. Bagchi , Department of Management, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 64200 nptelhrd
Planning and Executing In Vivo siRNA Experiments
 
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Functional analysis by mRNA knockdown using siRNAs is now routine in many molecular biology labs. However, many RNAi-related experiments fail due to diversion from simple, good practices. In this webinar, Dr. Garrett Rettig reviews the steps leading to successful siRNA experiments, including: • Understanding the target transcript • siRNA selection • Choosing the cell type • Validating the assay • Including appropriate biological controls About the Speaker Dr. Garrett Rettig is a Research Scientist in Molecular Genetics at Integrated DNA Technologies (IDT). He received a bachelor's degree in biology from Wartburg College and a PhD in Pharmacy -- Medicinal Chemistry from the University of Iowa where he studied synthetic peptide delivery systems for plasmid DNA and siRNA both in vitro and in vivo. At IDT, Garrett has been involved in high-throughput screening of siRNAs in vitro, and has recently co-authored a comprehensive review on siRNA in vivo published in Molecular Therapy.
Pocket-Sized Lab for Dynamics and Control
 
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The 2015 NSF-sponsored report Chemical Engineering Academia-Industry Alignment: Expectations about New Graduates1 identifies a strong industrial need for practical understanding of process control and system dynamics. Industry feedback also suggests more weight on translating process control theory to practice. To meet this need, laboratory experiences are integrated into many process control courses. With the growth of enrollment in chemical engineering, laboratory resources are often strained and scheduling for these labs can be difficult to manage. For this reason, we developed a pocket-sized process control lab that reinforces process control theory and is available to groups of two students each. We give the small and inexpensive process control experiment to students to reinforce concepts in dynamics and control theory. We’ll share our experience with implementing the Arduino-based temperature control lab for the process dynamics and control course. We’ll also share a number of potential pitfalls that limit the learning potential of the lab experience. A few universities are currently testing this pre-built lab for adoption or modification. The objective of this session is to introduce the lab with associated software modules. Software Modules • Set heaters to generate a step response • Estimate parameters to determine a model • Tune a PID controller • Reject disturbances and track setpoint • Develop SISO and MIMO control methods • Introduce advanced control topics such as Model Predictive Control (MPC) This lab was presented at the 2017 ASEE Summer School at NCSU as part of resources for teaching process transient analysis. Lab solutions for instructors are provided in Python and MATLAB. See https://apmonitor.com/heat.htm for additional lab details. Biography: John Hedengren is a Chemical Engineer by training with a B.S. and M.S. degree from Brigham Young University and a Ph.D. from the University of Texas at Austin. He is an Associate Professor at Brigham Young University in the Chemical Engineering Department and leads the PRISM (Process Research and Intelligent Systems Modeling) group. Prior to BYU he worked as a consultant for companies on automation solutions and then full-time for 5 years with ExxonMobil supporting advanced control and optimization solutions. Dr. Hedengren’s area of expertise is in fiber optic monitoring, unmanned aerial systems, automation of production and drilling, and development of new technologies that monitor and control upstream infrastructure. R. Abraham (Abe) Martin is a Ph.D. candidate at Brigham Young University and developed the process control lab. His research focuses on the simultaneous design and control of High Altitude Long Endurance (HALE) aircraft. He recently completed a graduate internship at the Air Force Research Laboratory with a focus on computer vision and aerial vehicles. He previously completed a graduate internship at Idaho National Laboratory in modeling a hybrid nuclear energy system. He is a 3 Minute Thesis award recipient and a National Merit Scholar. He is currently in his 3rd year and planning to graduate in 2018. Jeff Kantor received his B.S. in Chemical Engineering from the University of Minnesota and an M.A. and Ph.D. from Princeton University. He joined the University of Notre Dame in 1981. He was the Vice President & Associate Provost and then became the Vice President for Graduate Studies and Research at Notre Dame. He is currently a professor in the Department of Chemical and Biomolecular Engineering. Dr. Kantor is interested in the analysis and optimization of integrated financial and process operations using methods of stochastic control, convex optimization, and quantitative finance. References [1] Luo, Y., Westmoreland, P.R., et. al., Chemical Engineering Academia-Industry Alignment: Expectations about New Graduates, An NSF-Sponsored Study led by the American Institute of Chemical Engineers.
Views: 1177 APMonitor.com
A/B Experimental Test | Measurement Fundamentals | App Marketing | Udacity
 
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Learn with Udacity and Google in our FREE App Marketing course and check out the Tech Entrepreneur Nanodegree program! ►►► http://bit.ly/Tech-Entrepreneur-Nanodegree ◄◄◄ ----------------------------------------­--------------------------------------------------- Built in partnership with Google, this program mixes theory and practice to show students how to transform ideas into market-ready products. ----------------------------------------­--------------------------------------------------- App Marketing: Your First 1,000 Users & Beyond ----------------------------------------­--------------------------------------------------- ►►► http://bit.ly/App-Marketing-Course ◄◄◄ ----------------------------------------­--------------------------------------------------- ● What's in the course? Without customers, your business does not exist. Marketing helps you understand your potential user and focus your product on their needs. This course will help you organize a strategy of identifying your perfect user, find ways to connect with them and what you’ll say when you find them. This covers research, planning, execution and most importantly how to grow your user base. ● Why take this course? You’re going to come up with a plan to acquire users and grow! By the time you’re done, you will have a robust marketing plan, including a slide deck with a description of your value to your customers, how you stack up against competitors, who your target audience is, and to grow your customer base. ----------------------------------------­---------------------------------------------------- Course Syllabus; App Marketing Lesson 1: Understand the User Learn to define and create a targeted marketing plan for specific user segments. Analyze your competitive advantages and disadvantages through market segmentation and the five level of market competition and learn how to position your product. Conduct competitive analysis in order to create your unique value proposition. Lesson 2: Pre-launch Create marketing goals in order to focus your marketing plan. You’ll start thinking about your distribution plan and consider what keywords work best for your goals. You’ll also create the materials to help prepare you for launch and learn about your app listing page. Finally, you’ll learn about the beta-testing community, beta-testing groups, and prepare your landing pages. Lesson 3: Launch! Prepare, launch, execute, and gain your first users. You’ll outline a go-to-market strategy and gain the know-how to execute on it. You’ll learn SEO (search engine optimization) and ASO (app store optimization) skills as well as growth hacking tips to get your first 1000 users. Lesson 4: Customer Acquisition Learn about paid and free customer acquisition methods from AdWords, social marketing, email marketing and more. Lesson 5: Measurement Fundamentals Learn to use data to iterate and optimize your marketing plan. ----------------------------------------­--------------------------------------------------- ►►►http://bit.ly/App-Marketing-Course ◄◄◄ ----------------------------------------­--------------------------------------------------- Tech Entrepreneur Nanodegree Our Tech Entrepreneur Nanodegree program teaches you the skills you need to create your own revenue-generating app, and build a successful business around it. You’ll learn to succeed the Silicon Valley way! ----------------------------------------­---------------------------------------------------- ►►► http://bit.ly/Tech-Entrepreneur-Nanodegree ◄◄◄ ----------------------------------------­---------------------------------------------------- Udacity | Google | Tech Entrepreneur Nanodegree | App Marketing | Understand the User | Pre-Launch | Launch | Customer Acquisition | Measurement Fundamentals
Views: 1651 Udacity
Sequential Decision Making in Experimental Design and Sustainability via Adaptive Submodularity
 
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Solving sequential decision problems under partial observability is a fundamental but notoriously difficult challenge. I will introduce the new concept of adaptive submodularity, generalizing the classical notion of submodular set functions to adaptive policies. We prove that, if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. The concept allows us to recover, generalize, and extend existing results in diverse applications, including sensor management, viral marketing, and active learning. I will focus on two case studies. In an application to Bayesian experimental design in Behavioral Economics, we show how greedy optimization of a novel adaptive submodular criterion outperforms standard myopic techniques based on information gain and value of information. I will also discuss how adaptive submodularity can help to address problems in computational sustainability by presenting results on conservation planning for three rare species in the Pacific Northwest of the United States. This talk is based on joint work primarily with Daniel Golovin
Views: 401 Microsoft Research
Analytic tools in modeFRONTIER: exploring your Design Space to extract relevant information
 
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When managing complex problems with many input factors and multiple responses it is necessary to apply statistical methods to reduce the complexity and initiate a learning process from dedicated experiments. The fundamental pillars of the statistical methods like Design and Analysis of Experiments are easily accessible in the modeFRONTIER framework. With the new Sensitivity Analysis tool in modeFRONTIER, using the Smoothing Spline ANOVA (SSANOVA) proprietary algorithm, users can now easily perform a variable screening to exclude variables from optimization or RSMs projects.
Views: 2174 Webinar EnginSoft
Plackett Burman Design | Review on Statistica Software
 
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Variable screening tutorial using plackett burman design in Statistica software.
Views: 2617 Selected Solutions
Grasshopper Galapagos Tutorial
 
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In this Grasshopper Galapagos Tutorial, I will show you the basics of the Galapagos tool in Grasshopper. First I will show you a simple example which I will optimize a bounding box for a solid and then I will show you the steps for data visualization. download example at: https://parametric3d.com/en/grasshopper-galapagos-tutorial Want to learn Grasshopper, step by step? Check out our Grasshopper Course at: https://goo.gl/sx51uF
Views: 1504 Parametric House
Experiments 6 - Wrap-up: the course in review, multiple objectives, and references for the future
 
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Videos used in the Coursera course: Experimentation for Improvement. Join the course for FREE at https://www.coursera.org/learn/experimentation These videos are also part of the free online book, "Process Improvement using Data", http://yint.org/pid Full script for the video: http://yint.org/scripts/6 -------------------- We will give a high level overview, of the concepts we have covered in this course related to Design of Experiments for Improvement. We will also mention some topics we did not cover. The area of experimental design, is fairly broad, and the concepts we omitted, can quickly become very mathematical. But, if you choose to study these topics on your own afterwards, you will see that they build on the ideas we did cover in this course. You now have a solid foundation to base your self-learning on. All further, advanced experimental tools build on these concepts. We started the course by looking at some basic terminology: factors, outcomes, variables, low and high levels, and so forth. Very quickly we learned how to interpret - visually - the results from an experiment. And that was crucial; a visual interpretation is so important, and not having to run and rely on software. This is a theme we've seen throughout the course. We've resorted to visual tools the entire way. Cube plots, to visualize the results; Pareto plots to identify, or screen out factors, that appear important and those that are not. And finally, in the response surface module we looked at contour plot, to visualize the surface we are moving on. We also learned along the way, at several point how NOT to run an experiment. Changing one factor at a time, is something we have known for at least the last 8 decades as being inefficient, especially if we want to learn about and exploit interactions to reach that optimum. We learned how to set up our standard order table, to assist us. There are 2 to the k experiments in a full factorial. And once that full factorial was run, we saw how to manually create a simple prediction model. Remember that "high minus low", "high minus low" idea? No software was required. So by the end of the second module we saw that experiments often had more than one outcome. We might want to decrease pollution as much as possible, but also do so cheaply, and obey safety, or regulatory constraints. In systems where this is the case, we must either reformulate our objective to include multiple outcomes - maybe by using a weighted sum, for example - or by visualizing the overlapping, competing criteria on two contour plots. This visual approach is, again, very effective. We can see the trade offs in our system, and communicate with our colleagues effectively that don't understand the terminology of response surfaces and optimization. Furthermore, if things change in our system, we can quickly see how to compensate for them. Now in the third module of the course, we started using software, to speed up our hand calculations. We used a high quality, freely available tool to do that. The R software has many packages available to extend its functionality. But there are though other software tools, and some that are specifically designed for experimental analysis, feel free to download their trial versions and test them out for your own needs. We liked R, because of its traceability in the code. We can always go back, and reproduce our results. See where we've made mistakes and even share that code with our colleagues. You might be wondering about formal statistical tools that you might use to make your work more analytically: such as p-values, confidence intervals, analysis of variance, and so on. These are absolutely available, and have been there all along in the R output. As you've seen, we've been far more reliant on visual tools in this course, and less so on detailed statistical knowledge. In the fourth module of the course we started to look at fractional factorials. We use these when we have a large number of factors, and want to practically reduce the number of experiments to some lower value. We know that there's no free lunch, and that aliasing will occur. But we have this trade off table to help guide us in that choice. We learned about blocking for nuisance factors, and we also covered the idea of covariates in that fourth module. I had also mentioned the concept of definitive screening designs, which are emerging as a more effective design than fractional factorials. Perhaps this is a good time, to mention the book by Peter Goos and Bradley Jones. That book starts where this course ends. It's a great book, written in conversational style, that would help you peer into the minds of statisticians as they actually plan complex experiments. They cover topics, that many of you have asked about: response surface methods with categorical factors, screening designs, mixture designs, blocking and covariates, as well as the very practical requirements of a split plot design. ...
Views: 1252 Kevin Dunn
Codon optimization: Why & how to design DNA sequences for optimal soluble protein expression
 
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Have you struggled with low protein expression levels in your experiments? This webinar will explain the principles of codon optimization and explore case studies showing how it improves protein expression up to 100-fold. Research has revealed dozens of DNA sequence features that influence the efficiency of each step required to achieve soluble target protein expression. We will review the critical publications that inform GenScript's patented algorithm and the data showing how our algorithm compares to our competitors. We will look at peer-reviewed papers that employed codon-optimized synthetic genes for heterologous protein expression in different host systems, including bacteria, yeast, plant, and human cells. Finally, we will see how GenScript's codon optimization can provide clever solutions to molecular biology problems in specialized applications.
Views: 9812 GenScript USA Inc.
Efficient Stochastic Multicriteria Arm Trajectory Optimization
 
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Video attachement to paper: Dmytro Pavlichenko and Sven Behnke: Efficient Stochastic Multicriteria Arm Trajectory Optimization IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, September 2017. http://www.ais.uni-bonn.de/papers/IROS_2017_Pavlichenko.pdf Performing manipulation with robotic arms requires a method for planning trajectories that takes multiple factors into account: collisions, joint limits, orientation constraints, torques, and duration of a trajectory. We present an approach to efficiently optimize arm trajectories with respect to multiple criteria. Our work extends Stochastic Trajectory Optimization for Motion Planning (STOMP). We optimize trajectory duration by including velocity into the optimization. We propose an efficient cost function with normalized components, which allows prioritizing components depending on userspecified requirements. Optimization is done in two stages: first with a partial cost function and in the second stage with full costs. We compare our method to state-of-the art methods. In addition, we perform experiments on real robots: centaur-like robot Momaro and an industrial manipulator.
Views: 217 nimbro
Field Design in Plant Breeding with Dr  Kent Eskridge
 
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Dr. Kent Eskridge discusses Field Design in Plant Breeding during the TCAP Seminar Series 3
Views: 1148 Deanna Leingang
Trajectory and Foothold Optimization using Low-Dimensional Models for Rough Terrain Locomotion 2017
 
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Trajectory and Foothold Optimization using Low-Dimensional Models for Rough Terrain Locomotion Carlos Mastalli, Michele Focchi, Sylvain Calinon, Jonas Buchli, Ioannis Havoutis, Andreea Radulescu, Darwin G. Caldwell, Claudio Semini IEEE Conference on Robotics and Automation 2017 Abstract— We present a trajectory optimization framework for legged locomotion on rough terrain. We jointly optimize the Center of Mass (CoM) motion and the foothold locations, while considering terrain conditions. We use a terrain costmap to quantify the desirability of a foothold location. We increase the gait’s adaptability to the terrain by optimizing the step phase duration and modulating the trunk attitude, resulting in motions with guaranteed stability. We show that the combination of parametric models, stochastic-based exploration and receding horizon planning allows us to handle the many local minima associated with different terrain conditions and walking patterns. This combination delivers robust motion plans without the need for warm-starting. Moreover, we use soft-constraints to allow for increased flexibility when searching in the cost landscape of our problem. We showcase the performance of our trajectory optimization framework on multiple terrain conditions and validate our method in realistic simulation scenarios and experimental trials on a hydraulic, torque controlled quadruped robot. Official paper download link at publisher: coming soon Pre-prints of all our papers can be found here: http://www.iit.it/hyq (Publications) The following publications provide the details about the online computation of the terrain costmap and the whole-body controller C. Mastalli, A. Winkler, I. Havoutis, D. G. Caldwell, C. Semini, On-line and On-board Planning and Perception for Quadrupedal Locomotion, IEEE International Conference on Technologies for Practical Robot Applications (TEPRA), 2015. M. Focchi, A. del Prete, I. Havoutis, R. Featherstone, D. G. Caldwell, C. Semini, High-slope terrain locomotion for torque-controlled quadruped robots, Autonomous Robots, 259-272, 2017.
Relevance in Online Groups | Launch | App Marketing | Udacity
 
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Learn with Udacity and Google in our FREE App Marketing course and check out the Tech Entrepreneur Nanodegree program! ►►► http://bit.ly/Tech-Entrepreneur-Nanodegree ◄◄◄ ----------------------------------------­--------------------------------------------------- Built in partnership with Google, this program mixes theory and practice to show students how to transform ideas into market-ready products. ----------------------------------------­--------------------------------------------------- App Marketing: Your First 1,000 Users & Beyond ----------------------------------------­--------------------------------------------------- ►►► http://bit.ly/App-Marketing-Course ◄◄◄ ----------------------------------------­--------------------------------------------------- ● What's in the course? Without customers, your business does not exist. Marketing helps you understand your potential user and focus your product on their needs. This course will help you organize a strategy of identifying your perfect user, find ways to connect with them and what you’ll say when you find them. This covers research, planning, execution and most importantly how to grow your user base. ● Why take this course? You’re going to come up with a plan to acquire users and grow! By the time you’re done, you will have a robust marketing plan, including a slide deck with a description of your value to your customers, how you stack up against competitors, who your target audience is, and to grow your customer base. ----------------------------------------­---------------------------------------------------- Course Syllabus; App Marketing Lesson 1: Understand the User Learn to define and create a targeted marketing plan for specific user segments. Analyze your competitive advantages and disadvantages through market segmentation and the five level of market competition and learn how to position your product. Conduct competitive analysis in order to create your unique value proposition. Lesson 2: Pre-launch Create marketing goals in order to focus your marketing plan. You’ll start thinking about your distribution plan and consider what keywords work best for your goals. You’ll also create the materials to help prepare you for launch and learn about your app listing page. Finally, you’ll learn about the beta-testing community, beta-testing groups, and prepare your landing pages. Lesson 3: Launch! Prepare, launch, execute, and gain your first users. You’ll outline a go-to-market strategy and gain the know-how to execute on it. You’ll learn SEO (search engine optimization) and ASO (app store optimization) skills as well as growth hacking tips to get your first 1000 users. Lesson 4: Customer Acquisition Learn about paid and free customer acquisition methods from AdWords, social marketing, email marketing and more. Lesson 5: Measurement Fundamentals Learn to use data to iterate and optimize your marketing plan. ----------------------------------------­--------------------------------------------------- ►►►http://bit.ly/App-Marketing-Course ◄◄◄ ----------------------------------------­--------------------------------------------------- Tech Entrepreneur Nanodegree Our Tech Entrepreneur Nanodegree program teaches you the skills you need to create your own revenue-generating app, and build a successful business around it. You’ll learn to succeed the Silicon Valley way! ----------------------------------------­---------------------------------------------------- ►►► http://bit.ly/Tech-Entrepreneur-Nanodegree ◄◄◄ ----------------------------------------­---------------------------------------------------- Udacity | Google | Tech Entrepreneur Nanodegree | App Marketing | Understand the User | Pre-Launch | Launch | Customer Acquisition | Measurement Fundamentals
Views: 824 Udacity
Review of Probability and Statistics - I
 
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Six Sigma by Dr. T. P. Bagchi , Department of Management, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 17262 nptelhrd
Measure Matters Episode 9: Advanced Analysis
 
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Google Analytics Advocates Krista Seiden and Louis Gray demonstrate Advanced Analysis in Google Analytics 360, including Exploration, Funnel Analysis, and Segment Overlap. Additionally, Data Studio leaves beta and becomes generally available. Streamed from Google's Mountain View headquarters.
Views: 7866 Google Analytics
Statistical Significance in A/B Testing - a Complete Guide
 
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Statistical significance is crucial to planning, monitoring and analyzing the results of A/B tests, a.k.a. online controlled experiments. In this 4-part series, we offer an in-depth explanation, cover common misinterpretations and mistakes in applying statistical significance in conversion rate optimization, landing page optimization and user testing in general. Resources and references: 1. Full article: The Complete Guide to Statistical Significance in A/B testing: http://blog.analytics-toolkit.com/2017/statistical-significance-ab-testing-complete-guide/ 2. A/A/ tests in CRO: http://blog.analytics-toolkit.com/2014/aa-aab-aabb-tests-cro/ 3. Reductio ad absurdum: https://en.wikipedia.org/wiki/Reductio_ad_absurdum 4. Non-inferiority A/B tests: http://blog.analytics-toolkit.com/2017/case-non-inferiority-designs-ab-testing/
Views: 2064 Analytics-Toolkit.com
Optimize Food and Beverage Production with Plant Simulation
 
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Simulation projects in the Food and Beverage industry require a specialized solution to handle the complex combination of strong fluctuations in seasonal demand, high turnover, increasing product and packaging diversity, and rising energy and raw material costs. The Siemens PLM Plant Simulation Food and Beverage Library models complex production fast and effectively, allowing planners to investigate planning options and make informed decisions before incurring heavy design, engineering and investments costs. The aim is to find an optimized solution based on compromises between performance, flexibility, and return on investment. By identifying bottlenecks and non-value adding processes early in the development process, simulation can increase plant’s efficiency without increasing the investment risk
Views: 1744 PMC Videos
Final Year Project Ideas for Mechanical Engineering Students
 
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Mechanical Engineering - Final Year Project Ideas Benefits of final year project Your final year project (BE or ME) can help you in the following ways: 1. You can increase your overall percentage by scoring 90 to 95% marks through a good project. 2. An industry-oriented project can add extra weightage to your resume and help you get a good job in the core mechanical industry. 3. A technically good project can give you an added advantage if you are planning for higher studies in US, UK, Germany etc. Hence, make your final-year project valuable even if it takes a little more time and effort. Choose the right industry You can choose one of the following industries for your project: Aerospace, Automobile, Marine, HVAC, Oil & Gas, Industrial, Turbo-machines, Machine-tools, Appliances etc. Choose the right type You can choose one of the following types: a. Experimental Projects: i. This type of projects need good lab-facilities and hi-tech instrumentsfor measuring your experimental set-upaccurately. Only IITs and IISc have such facilities. Students from Tier II and private colleges try their best but mostly end-up in cooking-up the results or change the project at the last moment due to non-availability of the above facilities. ii. Also it involves more time and cost for setting up the project for those students who are already tightly scheduled with final-exams or campus interviews. iii. This type is feasible for those who don't have such constraints. b. Design Projects: i. CAE Projects -- Stress analysis, dynamic analysis etc.of aero, auto or mechanical systems can be done through these projects. New design, Improving performance, optimization can be accomplished. FEM based software like ANSYS, NASTRAN, RADIOSS, HYPERMESH etc. can be used. ii. CFD Projects -- Flow analysis, Thermal analysis, Aerodynamics improvement are the typical projects. This uses software like FLUENT, STAR-CCM+ are used for CFD analysis. iii. Flexibility and accuracy are the major advantages in this type of projects. iv. However it needs some additional training. To know more... Job Opportunities Interviewers give more weightage for good projects. CAE or CFD projects in Aerospace and automobile systems have more job opportunities in India, US, Europe and Japan. At the same time HVAC, Oil & Gas, Marine have good opportunities in Gulf. Choose a RELEVANT project Go aheadand choose the right project using the above factors. All the best.... Some Project Titles. CFD projects in Automobile Industry Design Optimization of Diesel Engine Manifold using CFD Simulations Improving the mileage of a passenger car through aerodynamics re-design using CFD Techniques CFD projects in Aerospace Industry Prediction and minimization of drag on an aircraft wing using CFD Analysis Design of aircraft safety systems using CFD analsyis CAE project in Automobile Industry Design optimization of chassis of a truck using CAE analysis Redesign of a suspension system for passenger car using CAE analysis CAE project in Aerospace Industry Design optimization of nose-cone frame of an aircraft using CAE analysis Performance optimization of wing-box of an aircraft using CAE analysis More projects... http://www.techzilon.com/projectenquiry.php The choice of the industry can be based on your interest or based on future job-opportunities.
Views: 47803 afsar m
Solving Supply Chain Problems with Planning and Optimization Tools
 
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I interviewed Jerry Bendiner who discussed Solving Supply Chain Problems with Planning and Optimization Tools. ...Looking forward to discussing with you Solving Supply Chain Problems with Planning and Optimization Tools. Before we start, can you provide a brief background of yourself and your company?
Views: 47 Dustin Mattison
Experiments 4H - An example of an analyzing an experiment with aliasing
 
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Videos used in the Coursera course: Experimentation for Improvement. Join the course for FREE at https://www.coursera.org/learn/experimentation These videos are also part of the free online book, "Process Improvement using Data", http://yint.org/pid Full script for the video: http://yint.org/scripts/4H -------------------- So let's look at an example to end this module. We said in the prior video that you should always include as many factors as you possibly can in a set of experiments. Do you remember why we recommend that? If not, please review the prior video again. In this example we are going to use 7 factors, and the fewest possible experiments; that's 8 experiments. We are going to screen out which of those 7 factors really affect our outcome. So it is a screening design with 8 experiments and a resolution of III. I could choose more experiments, and then go to higher and higher resolutions. But let's see what happens when we start with just eight experiments and seven factors. With eight experiments, we have factors A, B and C to form a full factorial in eight rows. The tradeoff table tells us to generate factors D, E, F and G. Now notice that this is a 2\^{7 - 4} design. So this design has p=4. These 4 generators, can be used to create the columns for the remaining factors in my system. And here's the completed table. I can go ahead and run the experiments and start my analysis. But the whole purpose of the tools introduced in this module is all about checking your aliasing before you start the analysis. Let's go do that. Our 4 generators are rearranged over here. I equals ABD, I equals ACE, and so on. How many words in our defining relationship? Two to the power of p and with p=4 in this case, that equals 16 words. That's a lot of words to figure out, but let's give it a try. The first few words are easy. Take the rearranged generators individually: I = ABD = ACE = BCF = ABCG That's 5 of them. Now we can add to that the combinations two at a time: (ABD)(ACE) = BCDE. The next combination two at a time is: (ABD)(BCF) = ACDF. You can prove to yourself that those are the remaining four (CDG, ABEF, BEG, AFG). Now we've got 11 words so far in our defining relationship. The next step is to take our generators three at a time: (ABD)(ACE)(BCF) = DEF Try the next three (ADEG, CEFG, BDFG). So, there we have a total of 15. And the final combination is to use all four generators multiplied together. And that simplifies to ABCDEFG. So, here's our complete defining relationship. Now, let's go try and calculate the aliasing for factor A. If we go and do that, we get this very long expression over here. I've highlighted only the two-factor interactions that are confounded with the main effect of A. I can create this list of aliases for the seven main effects in my design. This illustrates the tremendous confounding that takes place in the very dense designs at the far right-hand side of the trade-off table. Remember, instead of doing two to the seven, which equals 128 experiments, we've done 8. There's going to be a steep price to pay for this reduction in work. Now let's go and look at the numbers from the outcome variable, and how to continue on with the analysis. And as you'll see, and this is very typical, the analysis goes much quicker than the planning. Here's the code that you can use to analyze this design. Please copy and paste it from the website. We recommend that you always clear your environment from prior work. This is because you might have a variable with the same name from a different analysis; this will avoid any confusion. Build the linear model in exactly the same way as you created the design on paper. First, define the three variables that you start with: A, B, and C. Next, generate the remaining four factors using the definitions from the tradeoff table. When you inspect these variables in the console, you should get exactly what you had on paper. Now, add the outcome values recorded for the eight experiments. I'm going to take them from the standard order table. When you are ready to visualize your linear model, load the PID package, using the "library" command. You would have installed this package if you had been following prior videos. I will quickly note that R packages are frequently updated. You should check for updates regularly, as demonstrated here. So use the "paretoPlot(...)" command and let's examine the output. We can see here that the factors C, A and G are significant and have a negative, reducing effect, on the outcome variable. Factor E is a little smaller. And factors B, D and F have small to negligible coefficients. Note however, when we say factor A up here is important, it is really A that is aliased with a variety of two factor and higher interactions. As long as the assumption is true that those two factor and higher order interactions are small, or zero, then that bar in the Pareto plot essentially ...
Views: 2928 Kevin Dunn
What Is Optimization Of Media?
 
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Media optimization sheffield bio science sheffieldbioscience media url? Q webcache. Statistical optimization of media components for production rapid development and cell culture conditions the ethanol from wikipedia. Systematic optimization of human pluripotent stem cells media using front end for modern sites catchpoint. Media optimization sheffield bio science. Optimization of cell culture media bioprocess international. Media formulation, media optimisation, slideshareoptimization of criteria for optimization. It should be feb 25, 2014 statistical optimization of media components for production fibrinolytic alkaline metalloproteases from xenorhabdus indica kb 3 jan 1, 2008 while the use doe techniques can improve efficiency screening and subsequent starting with any appropriate in order to obtain high ethanol yield fermentation rate, response surface methodology (rsm) was employed study effect culture medium on social (smo) is a number outlets communities generate publicity increase awareness product, service brand we identify your most valuable customers, then come up comprehensive health buying strategy planning purchasing enhanced streptococcus phocae pi80 its bacteriocin using methodologykanmani composition one important parameters analyzed biotechnological processes industrial purposes, because growth conditions including, incubation period, initial ph, nacl concentration, optimize significant dec 9, 2016 based purification galactosidase solid state by custom cell viral vaccinesvivalis, nantes, france biotechnology company, has developed characteristics, adjustment feed formulations, process cellvento cho 100 series general may 5, 2015 model hpsc design experiments. Health media buying strategy, planning & optimization of components for enhanced production development and a new culture using plackett burman statistical response surface methodology based custom cell sigma aldrich. Design, formulation, and optimization of media. Googleusercontent search. Gov pubmed 7765644 similar design, formulation, and optimization of media. Author information however, some general guidelines can be given for growth and production media considering the experimental evidence available about functions influence of medium components promoting product formation. It ops teams at interactive media companies (think news, sports, streaming services, etc. The most common cell lines encountered in the biotechnology industry include. A) schematics of the rational used on development a completely front end optimization for modern media sites. Optimizing media feeding strategies guidance for emd millipore. Design, formulation, and optimization of mediamedia sheffield bio science. Author information however, some general guidelines can be given for growth and production media considering the experimental evidence available about functions influence of medium components promoting product formation optimization. Medium optimization is an integral part of biopharmaceuti
Views: 29 Your Question I
Complexity Made Simple - DOE Screening followed by Modelling Using DOE Pro
 
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A video tutorial showing you how to conduct 2 DOE's. A Screening L12 followed by a modelling DOE to hit a target. The software is DOE Pro...
Views: 277 Paul Allen
sFly: Terrain Surveillance Coverage using Cognitive Adaptive Optimization
 
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A centralized cognitive‐based adaptive methodology for optimal surveillance coverage using swarms of MAVs has been developed mathematically analyzed and tested using experimental data extracted by a monocular SLAM algorithm. The data used in the experiment presented in this video correspond to the Birmensdorf test area.
Views: 340 sFlyTeam
Start your Marketing Experiments | Measurement Fundamentals | App Marketing | Udacity
 
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Learn with Udacity and Google in our FREE App Marketing course and check out the Tech Entrepreneur Nanodegree program! ►►► http://bit.ly/Tech-Entrepreneur-Nanodegree ◄◄◄ ----------------------------------------­--------------------------------------------------- Built in partnership with Google, this program mixes theory and practice to show students how to transform ideas into market-ready products. ----------------------------------------­--------------------------------------------------- App Marketing: Your First 1,000 Users & Beyond ----------------------------------------­--------------------------------------------------- ►►► http://bit.ly/App-Marketing-Course ◄◄◄ ----------------------------------------­--------------------------------------------------- ● What's in the course? Without customers, your business does not exist. Marketing helps you understand your potential user and focus your product on their needs. This course will help you organize a strategy of identifying your perfect user, find ways to connect with them and what you’ll say when you find them. This covers research, planning, execution and most importantly how to grow your user base. ● Why take this course? You’re going to come up with a plan to acquire users and grow! By the time you’re done, you will have a robust marketing plan, including a slide deck with a description of your value to your customers, how you stack up against competitors, who your target audience is, and to grow your customer base. ----------------------------------------­---------------------------------------------------- Course Syllabus; App Marketing Lesson 1: Understand the User Learn to define and create a targeted marketing plan for specific user segments. Analyze your competitive advantages and disadvantages through market segmentation and the five level of market competition and learn how to position your product. Conduct competitive analysis in order to create your unique value proposition. Lesson 2: Pre-launch Create marketing goals in order to focus your marketing plan. You’ll start thinking about your distribution plan and consider what keywords work best for your goals. You’ll also create the materials to help prepare you for launch and learn about your app listing page. Finally, you’ll learn about the beta-testing community, beta-testing groups, and prepare your landing pages. Lesson 3: Launch! Prepare, launch, execute, and gain your first users. You’ll outline a go-to-market strategy and gain the know-how to execute on it. You’ll learn SEO (search engine optimization) and ASO (app store optimization) skills as well as growth hacking tips to get your first 1000 users. Lesson 4: Customer Acquisition Learn about paid and free customer acquisition methods from AdWords, social marketing, email marketing and more. Lesson 5: Measurement Fundamentals Learn to use data to iterate and optimize your marketing plan. ----------------------------------------­--------------------------------------------------- ►►►http://bit.ly/App-Marketing-Course ◄◄◄ ----------------------------------------­--------------------------------------------------- Tech Entrepreneur Nanodegree Our Tech Entrepreneur Nanodegree program teaches you the skills you need to create your own revenue-generating app, and build a successful business around it. You’ll learn to succeed the Silicon Valley way! ----------------------------------------­---------------------------------------------------- ►►► http://bit.ly/Tech-Entrepreneur-Nanodegree ◄◄◄ ----------------------------------------­---------------------------------------------------- Udacity | Google | Tech Entrepreneur Nanodegree | App Marketing | Understand the User | Pre-Launch | Launch | Customer Acquisition | Measurement Fundamentals
Views: 544 Udacity
State-of-the-Art Normalization of RT-qPCR Data
 
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Setting up a qPCR experiment is so simple that it actually becomes dangerous. Without appropriate controls and data normalization, results can be misleading at best. During this webinar, Dr Vandesompele addresses selection and validation of suitable reference genes as well as the use of the global mean normalization method to obtain accurate data. He also describes tools for data generation and analysis. About the Speaker: Dr Jo Vandesompele Dr Jo Vandesompele is a professor of functional genomics and applied bioinformatics at Ghent University. He has written numerous ground-breaking publications on normalization of gene expression and real-time PCR data analysis, and co-authored the MIQE guidelines for publication of qPCR experiments. With Dr Jan Hellemans, Dr Vandesompele cofounded Biogazelle, a real-time PCR company, built upon a decade of experience in real-time PCR experiment design, assay development, and data-analysis (www.biogazelle.com).
LLC Hexa presetntation ENGLISH
 
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Analysis of linear and nonlinear statics and dynamics, stability, calculation of critical frequencies and vibrations, analysis of frequency characteristics under random load, spectral analysis, analysis of heat transfer and acoustics, seismic analysis, structural optimization, automatic identification of computer calculation model and experiment, experiment planning and check of received experimental data for completeness.
Views: 129 LLCHEXA
Stanford Webinar: Common Pitfalls of A/B Testing and How to Avoid Them
 
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A Stanford Webinar presented by the Stanford Leadership & Management Science (http://stanford.io/2ppiCxy) and Decision Analysis (http://stanford.io/2pByYDC) graduate certificate programs "Common Pitfalls of A/B Testing and How to Avoid Them" Speaker: Ramesh Johari, Stanford University A/B testing is the process of using randomized controlled experiments on a web page or technology platform to determine which of two versions has a higher conversion rate. While this method of testing has become ubiquitous, there remain some common traps to which even the most sophisticated data scientists fall prey. Join Ramesh Johari, Associate Professor, Stanford Department of Management Science & Engineering, as he discusses some common practices and pitfalls in A/B testing. You will learn: -To analyze the output of A/B tests -To balance quick results with statistical significance during continuous monitoring -To maintain data integrity when running simultaneous experiments About the Speaker: Ramesh Johari is interested in the design and management of large-scale complex networks, such as the Internet. Using tools from operations research, engineering, and economics, he has developed models to analyze efficient market mechanisms for resource allocation in networks.
Views: 2200 stanfordonline
Integrated motion planning - posture optimization
 
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Missions of walking robots in distant and dangerous areas require use of the teleoperation mode. However, the capabilities of a human operator to sense the terrain and to control the robot are limited. Thus, a walking robot should have enough autonomy to take an advantage of its high locomotion capabilities in spite of a limited feedback from the remote operator. This paper presents a method for real-time motion planning on a rugged terrain. The proposed method employs several modules for planning of the robot's trunk path, foothold selection, planning trajectories of the robot's feet, and analysis of the robot stability and the workspace of the legs. By using this method the robot can autonomously find a path to the desired position and discriminate between traversable and non-traversable areas. The Rapidly-exploring Random Trees (RRT) algorithm is used as a backbone of the proposed solution. Results of simulations and experiments on the real Messor robot are presented.
Views: 123 Monoscience
Freeslate | Seamless Execution of DOE Designs With Freeslate Lab Automation Systems
 
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Do you find the task of executing DOE experiments daunting? Would you like to automate the execution of DOE designs for Formulation and Process Chemistry experiments? Learn how Freeslate’s Lab Execution and Analysis (LEA) software may be used in conjunction with your chosen DOE package and with Freeslate automation to highly optimize your process. During this webinar, we will review the characteristics and benefits of the DOE import feature of Freeslate's Library Studio. Its capabilities will be highlighted by walking through the import and execution of a representative DOE design. We will further discuss how all the data captured in LEA may be imported back into your design package to quickly inform the next set of experiments, allowing you to automate your experimental planning and execution. 100s of data points may quickly be turned into 1000s, as has already been demonstrated by many Freeslate customers! Speaker: Justin Fisher, MBA (Senior Product Manager - Freeslate)
Views: 302 Unchained Labs
PlanFinding
 
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Using simulated annealing optimization and a VLSI algorithm to experiment with space plan generation for Project Akaba.
Views: 626 Anthony Hauck
Experiments 5F - RSM case study continues: constraints and mistakes
 
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Videos used in the Coursera course: Experimentation for Improvement. Join the course for FREE at https://www.coursera.org/learn/experimentation These videos are also part of the free online book, "Process Improvement using Data", http://yint.org/pid Full script for the video: http://yint.org/scripts/5F -------------------- In the prior video, I left you half way up the mountain. I had asked you to take that ninth step, that ninth experiment on your own. Were you able to find the location of that next run? As we proceed, we will cover two diversions. We will look, what happens, if you have constraints in your experiments. By that, I mean, what happens if you want to take a step and realize that because of safety issues, or for other reasons, that you can't quite go as far as you'd hoped. We'll also look at mistakes. What if you, or your colleagues, run an experiment but use the wrong settings? We'll show that you can easily recover from that. And in the prior video, I ended by asking you to take a step size with delta x_P equal to 1.5. If you did that, you would've found the associated delta x_T equal to 0.718. Now, let's convert these delta lower case x's to their uppercase, real world changes, using the formulas we introduced in the prior video. For throughput, this lower case "delta x_T" corresponds to an increase of 2.87 parts per hour, which we round to 3 parts. For price, it's a 27 cent increase that we would add to the baseline value. Now we can go tell our employees or colleagues that the 9th experiment is at 337 parts per hour, with a price of $1.45. Remember, our colleagues don't speak in coded units. We have to talk with them in actual units, even though we speak in coded units behind their backs, when we deal with the least squares model. Now we should always go predict the outcome of the experiment before running it. In coded units x_P for the ninth experiment is at 1.5, because we selected that. You might presume that the x_T value is 0.718 that you calculated but not quite, because remember, we rounded that value. So we should go recalculate what x_T is for run nine, using the usual formulas that connects real world units to coded units. So that value of x_T = 0.75. When we go use the model, the prediction with these coded values gives us a profit prediction of $731. Now if you go to the website and run the actual experiment, you might get a value close to $717. Our prediction was off by about $13 or $14. You should have been able to do all of the above after watching the prior videos. If not, go back to the prior video and recap with those calculations where they were shown in some detail. Now how bad is that prediction error of $13? One way to tell is by comparing it to the value from the noise in the system. And to calculate the noise, we need some replicated experiments, which we haven't gone and done. But if we had the time and budget, we could certainly do that and verify. But a rough way that we can get an estimate of that noise is by comparing it to the coefficient of the main effects in the model. And it is about half the size of the smallest main effect. So that prediction error is not too bad. Now since the model's predictions are still adequate, we can keep going up this direction of steepest ascent. This is new. In the prior factorial, we had to stop and rebuild after using it single step. But this time our predictions are still okay, so we keep going. This is the general principle of response surface methods: keep going up that path as long as the predictions are consistent with reality. Now we can try stepping to delta x_P=2.5 away from the baseline. Pause the video and try to calculate these quantities at these new 10th experiment yourself. You'll soon become an expert at these calculations, but it will take you several minutes at first. Once you're done with your work, go compare your prediction to the actual experiments using the website. So these are the values that you should have obtained, delta x_T = 1.2, delta T in real world units is a change of 4.8 parts per hour, and we'll round that up to 5. "Delta P" is 0.45 or $0.45. T for the tenth experiment, corresponds to 339 parts an hour; and P is a $1.63. x_T in coded units is 1.25. Just a little bit different from the x_T=1.2 that we had calculated earlier, due to rounding. And x_P=2.5. Using those coded values, we can predict a y-value for the 10th experiment of $784.77, or $785. Now, the actual experimental outcome is around $732. You won't get that exact figure from the website because we add some noise to the prediction just to make things realistic. That's about a $50 deviation though, and it's comparable to the main effect of the largest factor, the price. So it's probably time we rebuild this model. And the 10th experiment can form our baseline. Notice that when we do this, we reset our (0,0) center point to this new location in real world units. We do not use the previous ...
Views: 3370 Kevin Dunn

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