Statistics Cheatsheet by MIT
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This document contains mathematical formulas and calculations related to probability and statistics. It includes formulas for variance, standard deviation, t-tests, chi-squared tests, conditional probability, binomial probability, and Poisson probability. An example calculates the probability of having 1 boy out of 5 children.
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This document provides an overview of how data science can be applied to renewable energy. It discusses how renewable energy data like weather data, power output data, and grid data can be analyzed using predictive modeling, time series analysis, and machine learning to forecast energy generation and optimize systems. Challenges like data quality, integration of different data sources, and uncertainty are addressed. Optimization techniques can be used to determine optimal resource allocation and grid planning to integrate renewable energy. Data-driven insights from performance monitoring and predictive maintenance can improve renewable energy efficiency. Future trends include advancement in machine learning, integration of IoT technologies, and use of big data analytics.All in one picture data science central tutorial at one place
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This document provides concise visual explanations of various math and data science concepts in one picture each. These concepts include deep learning vs machine learning, traditional programming vs machine learning, p tests, support vector machines, logistic regression, regression analysis, naive bayes, bayes theorem, statistics and machine learning, correlation coefficients, r-square metrics, evaluation metrics, type 1 and 2 errors, comparing and ensemble datasets, parametric and non-parametric analyses, determining sample size, ROC curves, z-tests and t-tests, ANOVA, predictive analytics, time series methods, cross validation, confidence intervals, unsupervised learning, KNN, number of clusters selection, AB testing, EM algorithm, and number representation systems.Tutorsquickguidetostatistics
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Quick Tutorial of Statistics
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This document provides a summary of key concepts in probability and statistics, including definitions of sample space, events, axioms of probability, permutations, combinations, conditional probability, Bayes' rule, random variables, distributions, transformations, moments, and common distributions such as binomial, Poisson, uniform, normal, and exponential. Key formulas are given for probability mass and density functions, cumulative distribution functions, expectation, variance, covariance, and other statistical measures.Deep learning made easy
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Deep learning made easy help you to revise concept at speed of lightDeep learning MindMap
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Deep learning MindMap helps you to quick gone through all topic of Deep Learning.
Credit : dofossoMachine learning Mind Map
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This document discusses various machine learning concepts related to data processing, feature selection, dimensionality reduction, feature encoding, feature engineering, dataset construction, and model tuning. It covers techniques like principal component analysis, singular value decomposition, correlation, covariance, label encoding, one-hot encoding, normalization, discretization, imputation, and more. It also discusses different machine learning algorithm types, categories, representations, libraries and frameworks for model tuning.Recently uploaded (20)
Lesson Plan M1 2024 Lesson Plan M1 2024 Lesson Plan M1 2024 Lesson Plan M1...
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Lesson Plan M1 2024 Lesson Plan M1 2024 Lesson Plan M1 2024 EDL 290F Week 3 - Mountaintop Views (2025).pdf
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This presentation delves into the systemic blind spots within pharmaceutical science and regulatory systems, emphasizing the significance of "inactive ingredients" and their influence on therapeutic equivalence. These blind spots, indicative of normalized systemic failures, go beyond mere chance occurrences and are ingrained deeply enough to compromise decision-making processes and erode trust.
Historical instances like the 1938 FD&C Act and the Generic Drug Scandals underscore how crisis-triggered reforms often fail to address the fundamental issues, perpetuating inefficiencies and hazards.
The narrative advocates a shift from reactive crisis management to proactive, adaptable systems prioritizing continuous enhancement. Key hurdles involve challenging outdated assumptions regarding bioavailability, inadequately funded research ventures, and the impact of vague language in regulatory frameworks.
The rise of large language models (LLMs) presents promising solutions, albeit with accompanying risks necessitating thorough validation and seamless integration.
Tackling these blind spots demands a holistic approach, embracing adaptive learning and a steadfast commitment to self-improvement. By nurturing curiosity, refining regulatory terminology, and judiciously harnessing new technologies, the pharmaceutical sector can progress towards better public health service delivery and ensure the safety, efficacy, and real-world impact of drug products.
Adventure Activities Final By H R Gohil Sir
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Adventure Activities Final By H R Gohil SirHow to use Init Hooks in Odoo 18 - Odoo ºÝºÝߣs
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The Dravidian Languages by Arul Aram
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APM event hosted by the South Wales and West of England Network (SWWE Network)
Speaker: Aalok Sonawala
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Tonight, Aalok planned to discuss the importance of a PMO within project-based organisations, the different types of PMO and their key elements, PMO governance and centres of excellence.
PMO¡¯s within an organisation can be centralised, hub and spoke with a central PMO with satellite PMOs globally, or embedded within projects. The appropriate structure will be determined by the specific business needs of the organisation. The PMO sits above PM delivery and the supply chain delivery teams.
For further information about the event please click here.Useful environment methods in Odoo 18 - Odoo ºÝºÝߣs
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This PPT is showing the effect of social changes in human life and it is very understandable to the students with easy language.in this contents are Itroduction, definition,Factors affecting social changes ,Main technological factors, Social change and stress , what is eustress and how social changes give impact of the human's life.Principle and Practices of Animal Breeding || Boby Basnet
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Principle and Practices of Animal Breeding Full Note
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QuickBooks Desktop to QuickBooks Online How to Make the Move
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If you use QuickBooks Desktop and are stressing about moving to QuickBooks Online, in this webinar, get your questions answered and learn tips and tricks to make the process easier for you.
Key Questions:
* When is the best time to make the shift to QuickBooks Online?
* Will my current version of QuickBooks Desktop stop working?
* I have a really old version of QuickBooks. What should I do?
* I run my payroll in QuickBooks Desktop now. How is that affected?
*Does it bring over all my historical data? Are there things that don't come over?
* What are the main differences between QuickBooks Desktop and QuickBooks Online?
* And moreStatistics Cheatsheet by MIT
- 1. ¦Ì 2 = (x ¦Ì)2 n 2 = x2 n ¦Ì2 s2 s2 = (x x)2 n 1 s2 = x2 ( x)2 n n 1 s s n s2 1 n1 + 1 n2 s1 2 n1 + s2 2 n2
- 2. ¦Ì ¦Ì ¦Ì t = x ¦Ì0 s n ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì x = 0.0508 s2 = (x x)2 n 1 = (0.051 0.0508)2 + (0.0505 0.0508)2 + etc... 6 = 9.15 10 7 s = s2 = 9.56 10 4 t = x ¦Ì0 s n = 0.0508 0.05 9.56 10 4 7 = 2.17 ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì
- 3. t = x1 x2 s2 1 n1 + 1 n2 t = x1 x2 s1 2 n1 + s2 2 n2 ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì ¦Ì t = x1 x2 s1 2 n1 + s2 2 n2 = 700 668 212 12 + 302 6 = 2.342 2 = (O E)2 E
- 4. 2 = (O E)2 E = (143 155.25)2 155.25 + (60 51.75)2 51.75 + (55 51.75)2 51.75 + (18 17.25)2 17.25 = 2.519 2 = (O E)2 E = (1 2.778)2 2.778 + 2 (2 2.778)2 2.778 + 4 (3 2.778)2 2.778 + 2 (4 2.778)2 2.778 = 2.72 #1 +#3 #1 +#2 +#3 +#4 #1 +#3 #1 +#2 +#3 +#4 #1 +#2( ) E = (#1 +#3 )(#1 +#2 ) #1 +#2 +#3 +#4 = (row total)(column total) grand total 2 = (O E)2 E
- 5. E = (row total)(column total) grand total = (495)(490) 1960 =123.75 2 = (O E)2 E = (113 123.75)2 123.75 + (113 124)2 124 + (110 124.26)2 124.26 + etc...= 91.73 P(A and B) = P(A) P(B) P(A or B) = P(A) + P(B) P(A and B) P(A | B) = P(A and B) P(B) P(A | B) = P(B | A) P(A) P(B)
- 6. 0.5 0.5 0.5 = 0.125 = P(A and B) = P(2 children = bb and 1st child bb) P(B) = P(1st child = bb) = 0.5 P(2 children = bb | 1st child bb) = P(A | B) = P(A and B) P(B) = 0.125 0.5 0.25 P(A | B) = P(B | A) P(A) P(B) P(S = Z |W = Zz) = 0.5 P(W = Zz) = 0.7 P(S = Z) = (0.7 0.5) + (0.3 1) = 0.65 P(W = Zz | S = Z) = 0.5 0.7 0.65 = 0.538 P(X = m) = n! pm (1 p)(n m) m! (n m)! P(1 boy of 5 children) = 5! 0.51 0.54 1! (4)! = 0.15625 P(X = m) = e np (n p)m m!