This document lists shortcut keys that can be used from the desktop in Windows 10. Some examples include:
- Windows key to open the Start menu
- +1, +2 etc. to switch between applications in the taskbar
- +D to toggle between minimizing all windows and returning to the previous state
- +I to open the Settings app
- +TAB to open Task View to see all open windows and switch between them
How to install windows 8 using usb pen driveparag dhok
?
Computer System Requirement to install windows 8 on your computer
1 GB of Ram
16 GB Or More Free Hard disk Space
DVD - RW
Working Keyboard & Mouse
P4 Processor
Graphics card: Microsoft DirectX 9 graphics device with WDDM driver
Step 1 :: Insert Windows 8 0r windows 8.1 DVD In DVD -RW...
Step 2 :: Go to CMOS Setup | Bios Configuration By Continuous Pressing DEL Key From Keyboard Some common Keys are F1,F2,F10,F12
This document provides a summary of shortcut keys that can be used in Windows 10 from the desktop. It lists shortcuts for launching the Start menu, switching between apps and desktops, using Cortana, taking screenshots, managing windows, and more. The shortcuts are concisely presented and categorized by function for easy reference.
The document provides steps to apply a watercolor painting effect to an image in a photo editing program:
1. Duplicate the background layer 3 times to create 4 layers total. Turn off the top 2 layers, leaving only the bottom layer visible.
2. Apply the Cutout filter to the bottom layer, then change the blend mode to Luminosity.
3. Turn on the layer above and apply the Dry Brush filter, setting the brush size to 10 and texture to 3, changing the blend mode to Screen.
4. Turn on the top layer and apply the Median filter with a radius of 12, changing the blend mode to Soft Light to complete the watercolor effect.
This quick start guide introduces the interface, controls, and basic actions in Second Life through explanations of menus, function buttons, keyboard commands, avatar interactions, communications, inventory, camera controls and more. It provides an overview of how to navigate, communicate, and engage with the virtual world.
This document provides tips and shortcuts for using Microsoft Word. It discusses customizing toolbars and menus, adding items to toolbars, cursor movement shortcuts, special characters, styles, and adding a shortcut to your directory for easy access to files. The document offers advice on using styles to make global changes and provides examples of modifying common styles like Normal, Heading 1, and Heading 2.
The document provides an outline for an Alpha Tech Program trainer kit that teaches basic computer skills. The program aims to remove fear of computers and teach basic usage skills like operating a computer, connecting to the internet, creating documents and sending emails. The outline includes 4 modules that cover computer basics, common terminology, types of computers and operating systems, and teaches how to use Windows and manage files and folders.
Jupyter Notebook allows users to create and share documents that contain code, equations, visualizations and explanatory text. It supports interactive coding in over 40 programming languages. Notebooks contain cells that can contain code, equations, visualizations or explanatory text. Kernels run the code cells and communicate results. Common kernels include IPython for Python and IRkernel for R.
This document provides keyboard shortcuts for Microsoft Excel on Windows and Mac. It includes shortcuts for navigation, selection, formatting, formulas and other tasks. It also lists video tutorials and over 300 formula examples available on the ExcelJet website. Users can click shortcut titles to access more detailed online explanations. A printed quick reference card with all shortcuts is also available.
The document provides 17 productivity tips for using Altium Designer, an electronic design automation software. It describes tips such as how to walk through nets to check a design, manage layer sets, use net name prefixes to set up classes, find unconnected nets, place drill drawings, close out test point errors, and more. The tips are meant to improve efficiency and help hide some less known features of the software. It concludes by providing additional resources for more Altium Designer tips and tutorials.
This document provides an overview and instructions for a C# programming lecture on creating a gold miner game in Unity. It discusses troubleshooting resources, writing code in Unity using MonoDevelop IDE, code structure, printing text to the console, attaching scripts to game objects, variables, conditional statements, planning the game, pseudocode, reading user input, updating location, using classes and objects, methods, and key terminology. The goal is to program a game where the player controls a gold miner to navigate a world and find a gold pit by pressing arrow keys to move while the distance to the pit is displayed, with the objective of finding it in as few turns as possible.
The quick start guide provides instructions for installing the tablet driver and connecting the tablet to a computer. It explains the system requirements, how to install the driver software by inserting the driver CD, and how to connect the tablet to the computer via USB cable or dongle. It also provides an overview of the tablet hardware components and their functions.
This document provides an introduction to Calc, the spreadsheet component of OpenOffice. It discusses how to get started with Calc, manage files, work with sheets, cells, rows and columns. It also covers entering and formatting data, using formulas and functions, sorting and filtering data. The document contains exercises for students to practice these skills.
This document provides 17 productivity tips for Altium Designer, including how to walk through nets to check a design, manage layer sets, use net name prefixes to set up classes, find unconnected nets, delete long traces quickly, and move selections by X and Y coordinates. It also recommends additional resources for learning more Altium Designer tips and tricks.
Vi editor is a popular text editor in Unix systems. It has three modes of operation - command mode, insert mode, and ex command mode. Command mode allows navigation and editing using keyboard commands while insert mode allows inserting new text. Ex command mode allows issuing commands from the command line. Vi is more advanced than earlier line editors like ed allowing screen-based editing. However, it can be difficult to learn due to its modal nature and lack of error messages.
Productivity Enhencement with Visual StudioAhasan Habib
?
This document outlines various productivity features in Visual Studio, including keyboard shortcuts, code analyzers, code snippets, debugging tools, and extensions like Web Essentials and Resharper. It discusses features for formatting, refactoring, navigating, and inspecting code. The conclusion compares Visual Studio and Resharper features and notes considerations for using extensions like hardware requirements and compatibility issues.
Fontlab Ltd.
http://www.fontlab.com
Basics of
FontLab Studio 5
Ted Harrison
OpenType / TrueType fonts exist in two flavors: Windows TrueType (.ttf) and OpenType PostScript (.otf). They can use either TrueType or Type 1 glyphs and work across Windows, Mac and Unix platforms.
The font table in FontLab Studio displays information about glyphs and allows editing characters. Cells are color coded to indicate encoding status. Context menus provide glyph editing and encoding options.
This document provides an overview of basic FontLab Studio interface and tools for editing glyphs, contours, nodes and performing transformations. Guidelines, layers and hints can be used to aid in
Я расскажу о том, как можно использовать терминал не по назначению.
Какие подводные камни мне встречались, когда я разрабатывал canvas для терминала. Какие алгоритмы я использовал, чтобы оптимизировать скорость отрисовки элементов.
И конечно же покажу все на реальных примерах и отвечу на вопросы.
The BIOS Setup Utility allows the user to configure settings that control how the computer initializes and operates. Key pages in the utility include Standard CMOS Features to set date/time and hardware, Advanced BIOS Features for boot priorities and security options, and Advanced Chipset Features for timing parameters. The utility must be used carefully to avoid incorrect settings that could cause system errors.
The document discusses VIM basics, including its three main modes: command mode, insert mode, and visual mode. It provides examples of commands for navigating and editing text in VIM. Navigation commands cover moving the cursor within a line and between lines/pages. Editing commands demonstrate how to delete, change, copy/paste text using commands optionally followed by a number or text object for precision. Visual block mode and text objects in visual mode are also summarized as ways to efficiently edit selected text across multiple lines.
The document discusses VIM basics, including its three main modes: command mode, insert mode, and visual mode. It provides examples of commands for navigating and editing text in VIM. Navigation commands cover moving the cursor within a line and between lines/pages. Editing commands demonstrate how to delete, change, copy/paste text using commands optionally followed by a number or text object for precision editing. Visual block mode and text objects in visual mode are also summarized as advanced editing techniques in VIM.
Swing is a Java GUI widget toolkit that improves upon the older AWT toolkit. It includes common GUI components like JFrame, JPanel, and JLabel. JFrame represents a window, JPanel is used to group and layout components, and JLabel displays text. These components have constructors and methods to create, configure, add, and listen to GUI elements. Layout managers automatically position components and should be used for most applications.
The document discusses texture analysis in computer vision. It begins by asking what texture is and whether objects themselves can be considered textures. It then outlines several statistical and Fourier approaches to texture analysis, citing specific papers on texture energy measures, texton theory, and using textons to model materials. Deep convolutional neural networks are also discussed as being able to recognize and describe texture through learned filter banks. The concept of texels is introduced as low-level features that make up texture at different scales from edges to shapes. The document hypothesizes that CNNs are sensitive to texture because texture repeats across images while object shapes do not, and that CNNs act as texture mappers rather than template matchers. It also questions whether primary visual cortex
This document discusses active learning techniques called Deep Badge Active Learning. It proposes using gradient embeddings to represent samples and k-means++ initialization for sample selection. Specifically, it uses the gradient embedding for feature representation, then performs k-means++ initialization to select samples by finding those with the maximum 2-norm and those farthest from existing samples, adding them to the set iteratively. This aims to select a diverse set of samples, similar to how binary search works. The technique could improve over entropy-based and core-set selection approaches for active learning with convolutional neural networks.
Neural Radiance Fields (NeRF) represent scenes as neural networks that map 5D input (3D position and 2D viewing direction) to a 4D output (RGB color and opacity). NeRF uses an MLP that is trained to predict volumetric density and color for a scene from many camera views. Key aspects of NeRF include using positional encodings as inputs to help model view-dependent effects, and training to optimize for integrated color and density values along camera rays. NeRF has enabled novel applications beyond novel view synthesis, including pose estimation, dense descriptors, and self-supervised segmentation.
The document discusses various pooling operations used in image processing and convolutional neural networks (CNNs). It provides an overview of common pooling methods like max pooling, average pooling, and spatial pyramid pooling. It also discusses more advanced and trainable pooling techniques like stochastic pooling, mixed/gated pooling, fractional pooling, local importance pooling, and global feature guided local pooling. The document analyzes the tradeoffs of different pooling methods and how they can balance preserving details versus achieving invariance to changes in position or lighting. It references several influential papers that analyzed properties of pooling operations.
This document discusses background elimination techniques which involve three main steps: object detection to select the target, segmentation to isolate the target from the background, and refinement to improve the quality of the segmented mask. It provides an overview of approaches that have been used for each step, including early methods based on SVM and more recent deep learning-based techniques like Mask R-CNN that integrate detection and segmentation. The document also notes that segmentation is challenging without object detection cues and discusses types of segmentation as well as refinement methods that use transformations, dimension reduction, and graph-based modeling.
More Related Content
Similar to Use Jupyter notebook guide in 5 minutes (15)
Jupyter Notebook allows users to create and share documents that contain code, equations, visualizations and explanatory text. It supports interactive coding in over 40 programming languages. Notebooks contain cells that can contain code, equations, visualizations or explanatory text. Kernels run the code cells and communicate results. Common kernels include IPython for Python and IRkernel for R.
This document provides keyboard shortcuts for Microsoft Excel on Windows and Mac. It includes shortcuts for navigation, selection, formatting, formulas and other tasks. It also lists video tutorials and over 300 formula examples available on the ExcelJet website. Users can click shortcut titles to access more detailed online explanations. A printed quick reference card with all shortcuts is also available.
The document provides 17 productivity tips for using Altium Designer, an electronic design automation software. It describes tips such as how to walk through nets to check a design, manage layer sets, use net name prefixes to set up classes, find unconnected nets, place drill drawings, close out test point errors, and more. The tips are meant to improve efficiency and help hide some less known features of the software. It concludes by providing additional resources for more Altium Designer tips and tutorials.
This document provides an overview and instructions for a C# programming lecture on creating a gold miner game in Unity. It discusses troubleshooting resources, writing code in Unity using MonoDevelop IDE, code structure, printing text to the console, attaching scripts to game objects, variables, conditional statements, planning the game, pseudocode, reading user input, updating location, using classes and objects, methods, and key terminology. The goal is to program a game where the player controls a gold miner to navigate a world and find a gold pit by pressing arrow keys to move while the distance to the pit is displayed, with the objective of finding it in as few turns as possible.
The quick start guide provides instructions for installing the tablet driver and connecting the tablet to a computer. It explains the system requirements, how to install the driver software by inserting the driver CD, and how to connect the tablet to the computer via USB cable or dongle. It also provides an overview of the tablet hardware components and their functions.
This document provides an introduction to Calc, the spreadsheet component of OpenOffice. It discusses how to get started with Calc, manage files, work with sheets, cells, rows and columns. It also covers entering and formatting data, using formulas and functions, sorting and filtering data. The document contains exercises for students to practice these skills.
This document provides 17 productivity tips for Altium Designer, including how to walk through nets to check a design, manage layer sets, use net name prefixes to set up classes, find unconnected nets, delete long traces quickly, and move selections by X and Y coordinates. It also recommends additional resources for learning more Altium Designer tips and tricks.
Vi editor is a popular text editor in Unix systems. It has three modes of operation - command mode, insert mode, and ex command mode. Command mode allows navigation and editing using keyboard commands while insert mode allows inserting new text. Ex command mode allows issuing commands from the command line. Vi is more advanced than earlier line editors like ed allowing screen-based editing. However, it can be difficult to learn due to its modal nature and lack of error messages.
Productivity Enhencement with Visual StudioAhasan Habib
?
This document outlines various productivity features in Visual Studio, including keyboard shortcuts, code analyzers, code snippets, debugging tools, and extensions like Web Essentials and Resharper. It discusses features for formatting, refactoring, navigating, and inspecting code. The conclusion compares Visual Studio and Resharper features and notes considerations for using extensions like hardware requirements and compatibility issues.
Fontlab Ltd.
http://www.fontlab.com
Basics of
FontLab Studio 5
Ted Harrison
OpenType / TrueType fonts exist in two flavors: Windows TrueType (.ttf) and OpenType PostScript (.otf). They can use either TrueType or Type 1 glyphs and work across Windows, Mac and Unix platforms.
The font table in FontLab Studio displays information about glyphs and allows editing characters. Cells are color coded to indicate encoding status. Context menus provide glyph editing and encoding options.
This document provides an overview of basic FontLab Studio interface and tools for editing glyphs, contours, nodes and performing transformations. Guidelines, layers and hints can be used to aid in
Я расскажу о том, как можно использовать терминал не по назначению.
Какие подводные камни мне встречались, когда я разрабатывал canvas для терминала. Какие алгоритмы я использовал, чтобы оптимизировать скорость отрисовки элементов.
И конечно же покажу все на реальных примерах и отвечу на вопросы.
The BIOS Setup Utility allows the user to configure settings that control how the computer initializes and operates. Key pages in the utility include Standard CMOS Features to set date/time and hardware, Advanced BIOS Features for boot priorities and security options, and Advanced Chipset Features for timing parameters. The utility must be used carefully to avoid incorrect settings that could cause system errors.
The document discusses VIM basics, including its three main modes: command mode, insert mode, and visual mode. It provides examples of commands for navigating and editing text in VIM. Navigation commands cover moving the cursor within a line and between lines/pages. Editing commands demonstrate how to delete, change, copy/paste text using commands optionally followed by a number or text object for precision. Visual block mode and text objects in visual mode are also summarized as ways to efficiently edit selected text across multiple lines.
The document discusses VIM basics, including its three main modes: command mode, insert mode, and visual mode. It provides examples of commands for navigating and editing text in VIM. Navigation commands cover moving the cursor within a line and between lines/pages. Editing commands demonstrate how to delete, change, copy/paste text using commands optionally followed by a number or text object for precision editing. Visual block mode and text objects in visual mode are also summarized as advanced editing techniques in VIM.
Swing is a Java GUI widget toolkit that improves upon the older AWT toolkit. It includes common GUI components like JFrame, JPanel, and JLabel. JFrame represents a window, JPanel is used to group and layout components, and JLabel displays text. These components have constructors and methods to create, configure, add, and listen to GUI elements. Layout managers automatically position components and should be used for most applications.
The document discusses texture analysis in computer vision. It begins by asking what texture is and whether objects themselves can be considered textures. It then outlines several statistical and Fourier approaches to texture analysis, citing specific papers on texture energy measures, texton theory, and using textons to model materials. Deep convolutional neural networks are also discussed as being able to recognize and describe texture through learned filter banks. The concept of texels is introduced as low-level features that make up texture at different scales from edges to shapes. The document hypothesizes that CNNs are sensitive to texture because texture repeats across images while object shapes do not, and that CNNs act as texture mappers rather than template matchers. It also questions whether primary visual cortex
This document discusses active learning techniques called Deep Badge Active Learning. It proposes using gradient embeddings to represent samples and k-means++ initialization for sample selection. Specifically, it uses the gradient embedding for feature representation, then performs k-means++ initialization to select samples by finding those with the maximum 2-norm and those farthest from existing samples, adding them to the set iteratively. This aims to select a diverse set of samples, similar to how binary search works. The technique could improve over entropy-based and core-set selection approaches for active learning with convolutional neural networks.
Neural Radiance Fields (NeRF) represent scenes as neural networks that map 5D input (3D position and 2D viewing direction) to a 4D output (RGB color and opacity). NeRF uses an MLP that is trained to predict volumetric density and color for a scene from many camera views. Key aspects of NeRF include using positional encodings as inputs to help model view-dependent effects, and training to optimize for integrated color and density values along camera rays. NeRF has enabled novel applications beyond novel view synthesis, including pose estimation, dense descriptors, and self-supervised segmentation.
The document discusses various pooling operations used in image processing and convolutional neural networks (CNNs). It provides an overview of common pooling methods like max pooling, average pooling, and spatial pyramid pooling. It also discusses more advanced and trainable pooling techniques like stochastic pooling, mixed/gated pooling, fractional pooling, local importance pooling, and global feature guided local pooling. The document analyzes the tradeoffs of different pooling methods and how they can balance preserving details versus achieving invariance to changes in position or lighting. It references several influential papers that analyzed properties of pooling operations.
This document discusses background elimination techniques which involve three main steps: object detection to select the target, segmentation to isolate the target from the background, and refinement to improve the quality of the segmented mask. It provides an overview of approaches that have been used for each step, including early methods based on SVM and more recent deep learning-based techniques like Mask R-CNN that integrate detection and segmentation. The document also notes that segmentation is challenging without object detection cues and discusses types of segmentation as well as refinement methods that use transformations, dimension reduction, and graph-based modeling.
1. TL-GAN matches feature axes in the latent space to generate images without fine-tuning the neural network.
2. It discovers correlations between the latent vector Z and image labels by applying multivariate linear regression and normalizing the coefficients.
3. The vectors are then adjusted to be orthogonal, allowing different properties to be matched while labeling unlabeled data to add descriptions.
Image matting is the process of separating the foreground and background of an image by assigning each pixel an alpha value between 0 and 1 indicating its transparency. Traditionally, matting uses a trimap to classify pixels as foreground, background, or uncertain. Early sampling-based methods calculated alpha values based on feature distances of closest foreground and background pixels. More recent approaches use deep learning, where the first deep learning matting method in 2016 took local and non-local information as input, and the 2017 Deep Image Matting method used an RGB image and trimap as input in a fully deep learning framework.
Multi object Deep reinforcement learningDong Heon Cho
?
This document discusses multi-objective reinforcement learning and introduces Deep OLS Learning, which combines multi-objective learning with deep Q-networks. It presents Deep OLS Learning with Partial Reuse and Full Reuse to handle multi-objective Markov decision processes by finding a convergence set of policies that optimize multiple conflicting objectives, such as maximizing server performance while minimizing power consumption. The approach is evaluated on multi-objective versions of mountain car and deep sea treasure problems.
Multi agent reinforcement learning for sequential social dilemmasDong Heon Cho
?
This document summarizes research on multi-agent reinforcement learning in sequential social dilemmas. It discusses how sequential social dilemmas extend traditional matrix games by adding temporal aspects like partial observability. Simulation experiments are described where agents learn cooperative or defective policies for tasks like fruit gathering and wolfpack hunting in a partially observable environment. The agents' learned policies are then used to construct an empirical payoff matrix to analyze whether cooperation or defection is rewarded more, relating the multi-agent reinforcement learning results back to classic social dilemmas.
This document discusses multi-agent systems and their applications. It provides examples of multi-agent systems for spacecraft control, manufacturing scheduling, and more. Key points:
- Multi-agent systems consist of interacting intelligent agents that can cooperate, coordinate, and negotiate to achieve goals. They offer benefits like robustness, scalability, and reusability.
- Challenges include defining global goals from local actions and incentivizing cooperation. Games like the prisoner's dilemma model social dilemmas around cooperation versus defection.
- The document outlines architectures like the blackboard model and BDI (belief-desire-intention) model. It also provides a manufacturing example using the JADE platform.
The document discusses Hybrid Reward Architecture (HRA), a reinforcement learning method that decomposes the reward function of an environment into multiple sub-reward functions. In HRA, each sub-reward function is learned by a separate agent using DQN. This allows HRA to learn complex reward functions more quickly and stably compared to using a single reward signal. An experiment is described where HRA learns to eat 5 randomly placed fruits in an environment over 300 steps more effectively than a standard DQN agent.
Deep Learning AtoC with Image PerspectiveDong Heon Cho
?
Deep learning models like CNNs, RNNs, and GANs are widely used for image classification and computer vision tasks. CNNs are commonly used for tasks like classification, detection, segmentation through learning hierarchical image features. Fully convolutional networks with encoder-decoder architectures like SegNet and Mask R-CNN can perform pixel-level semantic segmentation and instance segmentation by combining classification and bounding box detection. Deep learning has achieved state-of-the-art performance on many image applications due to its ability to learn powerful visual representations from large datasets.
The document discusses approaches for using deep learning with small datasets, including transfer learning techniques like fine-tuning pre-trained models, multi-task learning, and metric learning approaches for few-shot and zero-shot learning problems. It also covers domain adaptation techniques when labels are not available, as well as anomaly detection for skewed label distributions. Traditional models like SVM are suggested as initial approaches, with deep learning techniques applied if those are not satisfactory.
The document discusses domain adaptation and transfer learning techniques in deep learning such as feature extraction, fine tuning, and parameter sharing. It specifically describes domain-adversarial neural networks which aim to make the source and target feature distributions indistinguishable and domain separation networks which extract domain-invariant and private features to model each domain separately.
This document discusses various techniques for compressing and speeding up deep neural networks, including singular value decomposition, pruning, and SqueezeNet. Singular value decomposition can be used to compress fully connected layers by minimizing the difference between the original weight matrix and its low-rank approximation. Pruning techniques remove unimportant weights below a threshold. SqueezeNet is highlighted as designing a small CNN architecture from the start that achieves AlexNet-level accuracy with 50x fewer parameters and less than 0.5MB in size.
AI + Disability. Coded Futures: Better opportunities or biased outcomes?Christine Hemphill
?
A summary report into attitudes to and implications of AI as it relates to disability. Will AI enabled solutions create greater opportunities or amplify biases in society and datasets? Informed by primary mixed methods research conducted in the UK and globally by Open Inclusion on behalf of the Institute of People Centred AI, Uni of Surrey and Royal Holloway University. Initially presented at Google London in Jan 2025.
If you prefer an audio visual format you can access the full video recorded at Google ADC London where we presented this research in January 2025. It has captioned content and audio described visuals and is available at https://www.youtube.com/watch?v=p_1cv042U_U. There is also a short Fireside Chat about the research held at Zero Project Conference March 2025 available at https://www.youtube.com/live/oFCgIg78-mI?si=EoIaEgDw2U7DFXsN&t=11879.
If 狠狠撸 Share's format is not accessible to you in any way, please contact us at contact@openinclusion.com and we can provide you with the underlying document.
Analyzing Consumer Spending Trends and Purchasing Behavioromololaokeowo1
?
This project explores consumer spending patterns using Kaggle-sourced data to uncover key trends in purchasing behavior. The analysis involved cleaning and preparing the data, performing exploratory data analysis (EDA), and visualizing insights using ExcelI. Key focus areas included customer demographics, product performance, seasonal trends, and pricing strategies. The project provided actionable insights into consumer preferences, helping businesses optimize sales strategies and improve decision-making.
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Optimizing Common Table Expressions in Apache Hive with CalciteStamatis Zampetakis
?
In many real-world queries, certain expressions may appear multiple times, requiring repeated computations to construct the final result. These recurring computations, known as common table expressions (CTEs), can be explicitly defined in SQL queries using the WITH clause or implicitly derived through transformation rules. Identifying and leveraging CTEs is essential for reducing the cost of executing complex queries and is a critical component of modern data management systems.
Apache Hive, a SQL-based data management system, provides powerful mechanisms to detect and exploit CTEs through heuristic and cost-based optimization techniques.
This talk delves into the internals of Hive's planner, focusing on its integration with Apache Calcite for CTE optimization. We will begin with a high-level overview of Hive's planner architecture and its reliance on Calcite in various planning phases. The discussion will then shift to the CTE rewriting phase, highlighting key Calcite concepts and demonstrating how they are employed to optimize CTEs effectively.
Cost sheet. with basics and formats of sheetsupreetk82004
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The truth behind the numbers: spotting statistical misuse.pptxandyprosser3
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As a producer of official statistics, being able to define what misinformation means in relation to data and statistics is so important to us.
For our sixth webinar, we explored how we handle statistical misuse especially in the media. We were also joined by speakers from the Office for Statistics Regulation (OSR) to explain how they play an important role in investigating and challenging the misuse of statistics across government.
9. Just remember 7 THINGS!
Esc Command Mode
a Insert cell above b Insert cell below delete selected cellsd d
Shift + Enter Ctrl + Enter
Run cell, select || create below Run selected cells
m To markdown y To code
10. Just remember 3 THINGS!
Enter Edit Mode
Tab Code indent Shift + Tab tooltip Ctrl + m Command mode
Same as ESC