How much each I'm A Celebrity 2019 star is being paid - from Caitlyn Jenner to Ian Wright
Jenner is pocketing a huge £500,000 to star - and she's not the only well-paid contestant on this year's series
by James Rodger, https://www.facebook.com/jamesrodgerjournalistCaitlyn Jenner has become one of the most high-paid stars to appear in I'm A Celebrity... Get Me Out Of Here!
The celebrity is starring alongside nine others in the ITV1 reality show, which returned on Sunday evening (November 17).
Jenner is pocketing a huge £500,000 to star - and she's not the only well-paid contestant on this year's series.
Ian Wright, a former Arsenal star turned broadcaster, who regularly appears as a pundit on BBC One's Match of the Day, is earning £400,000.
Girls Aloud star Nadine Coyle is taking home £250,000, according to reports.
Good Morning Britain presenter Kate Garraway and former EastEnders actress Jacqueline Jossa are set to make £75,000 each.
At the other end of the table, £50,000 is being paid to former Midlands rugby star James Haskell.
The former Wasps and England star will be paid the same amount as BBC Radio DJ Adele Roberts.
Capital FM DJ Roman Kemp, the son of Spandau Ballet star Martin, is being paid £40,000.
Rak Su boyband member Myles Stephenson will take back £25k.
I'm A Celebrity... Get Me Out Of Here! will return to our screens on ITV1 from 9pm this evening.
Jenner is among the favourites with bookmakers to win the show, with Ian Wright, Jacqueline Jossa and Roman Kemp also among the front-runners.


At GWK Farms we are constantly working on developing new and exciting product ranges to add even more magic to your home delights.
Keep checking our facebook (will be linked) page for updates on new and exciting product range announcements.
Thank you


public class BlitzEdit extends java.lang.Object implements Initializable
| Modifier and Type | Field and Description |
|---|---|
private MenuItem |
About |
private TabPane |
CircuitsTabPane |
private MenuItem |
Close |
private MenuItem |
Copy |
static Point2D |
copyMousePosition |
private Label |
Debug_Text |
private MenuItem |
Delete |
static Element |
dragAndDropElement |
static Element[] |
elementsToCopy |
private MenuItem |
GridOnOff |
private MenuItem |
Import_Component |
private MenuItem |
Import_Library |
private static java.util.ArrayList<LibraryCanvas> |
libraries |
private Accordion |
LibrariesAccordion |
private MenuItem |
New |
private MenuItem |
Open |
private TutorialPanel |
pane |
private MenuItem |
Paste |
private MenuItem |
QuickSave |
private MenuItem |
Reload |
private MenuItem |
Save |
private MenuItem |
SaveAs |
private MenuItem |
SelectAll |
private MenuItem |
SelectNone |
private MenuItem |
ToggleTutorial |
private Accordion |
Tutorial |
private MenuItem |
ZoomIn |
private MenuItem |
ZoomOut |
| Constructor and Description |
|---|
BlitzEdit() |
| Modifier and Type | Method and Description |
|---|---|
private LibraryCanvas |
addLibrary(java.lang.String name)
Adds a new tab to the accordion that contains a
LibraryCanvas and selects the new one. |
private CircuitCanvas |
addTab(java.lang.String name)
Adds a new tab to the tabpane that contains a
CircuitCanvas and selects the new one. |
private boolean |
checkForDuplicateLibrary(java.io.File destination)
Checks the LibraryCanvas directories of libraries for any duplication with the destination.
|
private void |
createTutorialPane() |
CircuitCanvas |
getCurrentCircuitCanvas()
Uses the TabPane to check which circuit canvas is currently active.
|
LibraryCanvas |
getCurrentLibraryCanvas()
Uses the Accordion to check which library canvas is currently active.
|
TitledPane |
getCurrentLibraryTitledPane()
Uses the Accordion class to check which library is currently selected.
|
Tab |
getCurrentTab()
Uses the TabPane class to check which circuit tab is currently selected.
|
private void |
handleCloseAction(Event event) |
private void |
handleCopyAction(Event event) |
private void |
handleDeleteAction(Event event) |
private void |
handleHelpAboutAction(Event event) |
private void |
handleImportComponentAction(Event event) |
private void |
handleImportLibraryAction(Event event) |
private void |
handleNewAction(Event event) |
private void |
handleOpenAction(Event event) |
private void |
handlePasteAction(Event event) |
private void |
handleReloadAction(Event event) |
private void |
handleSaveAction(Event event) |
private void |
handleSaveAsAction(Event event) |
private void |
handleSelectAllAction(Event event) |
private void |
handleSelectNoneAction(Event event) |
private void |
handleToggleTutorialAction(Event event) |
private void |
handleViewGridOnOffAction(Event event) |
private void |
handleViewZoomInAction(Event event) |
private void |
handleViewZoomOutAction(Event event) |
void |
initialize(java.net.URL location,
java.util.ResourceBundle resources) |
private boolean |
removeCurrentLibraryTitlesPane()
Removes the current selected TitledPane from the accordion and the library from libraries array.
|
private void |
setAnchorForNode(Node node,
double value)
Sets the anchor top, bottom, left and right positons for the javafx node.
|
public static Element[] elementsToCopy
public static Point2D copyMousePosition
public static Element dragAndDropElement
private static java.util.ArrayList<LibraryCanvas> libraries
private TutorialPanel pane
private MenuItem New
private MenuItem Reload
private MenuItem Open
private MenuItem Save
private MenuItem SaveAs
private MenuItem Import_Library
private MenuItem Import_Component
private MenuItem Close
private MenuItem Copy
private MenuItem Paste
private MenuItem Delete
private MenuItem SelectAll
private MenuItem SelectNone
private MenuItem QuickSave
private MenuItem ZoomIn
private MenuItem ZoomOut
private MenuItem GridOnOff
private MenuItem About
private MenuItem ToggleTutorial
private TabPane CircuitsTabPane
private Accordion LibrariesAccordion
private Accordion Tutorial
private Label Debug_Text
public void initialize(java.net.URL location, java.util.ResourceBundle resources)
initialize
in interface Initializable
public Tab getCurrentTab()
public TitledPane getCurrentLibraryTitledPane()
public CircuitCanvas getCurrentCircuitCanvas()
public LibraryCanvas getCurrentLibraryCanvas()
private void handleNewAction(Event event)
private void handleReloadAction(Event event)
private void handleOpenAction(Event event)
private void handleSaveAction(Event event)
private void handleSaveAsAction(Event event)
private void handleImportLibraryAction(Event event)
private void handleImportComponentAction(Event event)
private void handleCloseAction(Event event)
private void handleCopyAction(Event event)
private void handlePasteAction(Event event)
private void handleDeleteAction(Event event)
private void handleSelectAllAction(Event event)
private void handleSelectNoneAction(Event event)
private void handleViewZoomInAction(Event event)
private void handleViewZoomOutAction(Event event)
private void handleViewGridOnOffAction(Event event)
private void handleHelpAboutAction(Event event)
private void handleToggleTutorialAction(Event event)
private void createTutorialPane()
private CircuitCanvas addTab(java.lang.String name)
CircuitCanvas
and selects the new one.name
- Name for the tabprivate LibraryCanvas addLibrary(java.lang.String name)
LibraryCanvas
and selects the new one.name
- Name for the library tabprivate void setAnchorForNode(Node node, double value)
node
- Node anchor changes apply tovalue
- Anchor positionsprivate boolean removeCurrentLibraryTitlesPane()
private boolean checkForDuplicateLibrary(java.io.File destination)
destination
- Directory to check


Dear LexisNexis and {{ name }}:
Attached for your records is a copy of the CAP Bulk Access Agreement signed by {{ name }} on {{ signed_date|date:"F j, Y" }} and approved by LexisNexis.
- The Caselaw Access Project
----------------------------
{{ contract_html|safe }}


|
C++ Actor Framework 0.18
|
Computes credit for an attached source. More...
#include <credit_controller.hpp>
Classes | |
| struct | calibration |
| Wraps an assignment of the controller to its source. More... | |
Public Member Functions | |
| virtual void | before_processing (downstream_msg::batch &batch)=0 |
Called before processing the batch x in order to allow the controller to keep statistics on incoming batches. | |
| virtual calibration | init ()=0 |
| Returns an initial calibration for the path. | |
| virtual calibration | calibrate ()=0 |
| Computes a credit assignment to the source after crossing the low-threshold. | |
Computes credit for an attached source.
|
pure virtual |
Called before processing the batch x
in order to allow the controller to keep statistics on incoming batches.
Implemented in caf::detail::token_based_credit_controller.
|
pure virtual |
Computes a credit assignment to the source after crossing the low-threshold.
May assign zero credit.
Implemented in caf::detail::size_based_credit_controller, and caf::detail::token_based_credit_controller.
|
pure virtual |
Returns an initial calibration for the path.
Implemented in caf::detail::size_based_credit_controller, and caf::detail::token_based_credit_controller.


--- title: Glossary layout: handbook ---
data that is summarized using statistical methods or by simply summing its component parts
Application Programming Interface: a specification allowing two pieces of software to interface with each other, without either having knowledge of the inner workings of the other
a list of tasks prepared by the project manager
refers to the entirety of a dataset, instead of the incremental amounts that may be retrieval via other means, such as an API
Civil Service Organizations: a legally constituted organization created by natural or legal persons that operates independently from any form of government in the interest of civil society. Also commonly referred to as NGOs.
Comma Separated Values: A format for transactional data that separates each column value by a comma. Can be imported and exported from most spreadsheet programs
a syntax for interacting with data in a database. Usually specific to the software being used
one who programs enthusiastically (even obsessively) or who enjoys programming rather than just theorizing about programming (like a 'coder' - but more energetic :) )
a session where hackers come together to answer a particular task
a period of a defined length during which changes to a project can be made. Usually ends in a review meeting to plan the next iteration.
Javascript Object Notation: a data format that is relatively easy for humans to read and write and very easy for machines to read and parse
creating an entire copy of a website on another server, primarily for backup
(pronounced "My Sequel" or "MY S-Q-L"): Open source relational database software
A web service which turns place names and addresses into coordinates so that they can be mapped / associated with an administrative area
(abbreviated Postgres): Open source object relational database software, a query language similar (but not identical) to that in MySQL is used
Optical Character Recognition: the electronic conversion of scanned images of handwritten, typewritten or printed text into machine-encoded text
data from outside an organisation (often from standards organisations) which is, apart from occasional revisions, static
When a project's scope slowly and steadily increases, without a formal re-examination of the original terms and requirements
a file format for geospatial vector data that is primarily used to feed data into mapping applications
Hosted and open source mapping software for creating maps
short descriptions that outline in plain language the different use cases and possibilities for different types of users for the purposes of determining software requirements
a type of image composed of connected points rather than pixels
eXtensible Markup Language: a markup language created to structure, store, and transport data by defining a set of rules for encoding documents in a format that is both human-readable and machine-readable.
Some of the above entries contain excerpts from Wikipedia.org and the Jargon File v. 4.4.8


I am a student studying at Arizona State University pursuing a Bachleor in Science Computer Science degree.
Developed a ML software for classification of documents.
Mentoring first year students
GPA: 3.94
GPA: 4.00
Apart from being a web developer, I enjoy most of my time being outdoors. In the summer, I enjoy trekking and mountain climbing.I have a keen interest in all kinds of sports.


Esri ArcGIS Tiled Map Layer
Use the Layer Switch Control on the top rigth of the map to show another Esri Tiled Map Layers.


The Artisinal Impact Fund works with goverments, companies and private investors to help harness the power of artisanal mining and turn mineral wealth into prosperity.
The fund helps artisanal miners in:
- Purchasing mining equipment
- Purchasing safety equipment
- Enabling pre-payment for miners
- Access to low interest loans
- Access to essential commodities


The information from this app is provided “as is” without any representations or warranties, express, implied or insurance. The app makes no representations or warranties in relation to the information. You must not rely on the information from this app as an alternative to medical advice from your doctor or other professional healthcare provider. If you have any specific questions about any medical matter you should consult your doctor or other professional healthcare provider. If you think you may be suffering from any medical condition you should seek immediate medical attention. You should never delay seeking medical advice, disregard medical advice, or discontinue medical treatment because of information from this app.
Simon Cichosz. (2022). BOMP (Body Composition Prediction) (v0.2-alpha).


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There are many large bots out there for the same purpose as Watchdog. But we hope
our accessability features and reliability will be enough to grow a small user base.
This page is designed for anyone new who is using Watchdog.
Some of the settings
might be confusing, or you dont know what to set them to. This page will explain everything.
Ratelimit
The ratelimit is the required amount of offences to trigger the system
Weblog Type
We store our logs with a few types. below you can see what they all mean.
Similar Words
~70%
Mentions
~5
Capitals
~70%
Emotes
8~
Joins
3~
Spam
Depends, 7 - 12
Spoilers
5~
Without revealing the secrets behind our systems most of our features are measured overtime. The measured
elements are reset at a static ratemeaning that only we can change it. This duration is between 5 - 10 seconds.
Incase
you were wondering what the 'Type' numbers in Weblogs refer too. Heres an index.
1: Guild Join
2: Guild Leave
3: Prefix Change
4: Mute & Unmute
5: Warning Added / Removed
6: All Guild
warnings erased
7: Kick
8: Ban
9: Auto Moderator
~ Means approximately.


Contact!


- Regardless of its size, each company needs stability and safety in its accounting and
finance to calmly perform its inherent activity. We encompass all accounting processes
and provide our customers with an efficient and safe service based upon transparency in
the accounting processes and stability in the tax relationships with the state.
continue »
- The VAT tax is the main source of revenue for the national budget in Bulgaria and the
rest of the EU members. We can answer the questions you might have regarding its law as
well as the way your company should act in different situations both in Bulgaria or when
doing business inside or outside the EU.
continue »
- You can stay calm and safe facing inspections and audits by the National Revenue Agency
and the National Social Security Institute, for issues concerning the Occupational
Medical Service or an inspection by the Labour Inspectorate.
continue »


This app demonstrates how simple "rules" for chemical- and contact-based communication could be used for active delivery of therapeutic compounds, while overcoming traditional biotransport limits and using hypoxia to target delivery. It is based on a published PhysiCell example in Ghaffarizadeh et al. (2018) [1]. In this model, cancer cells (green) rapidly divide while consuming oxygen, which drives the emergence of hypoxic gradients. Greater availability of oxygen drives faster proliferation, while low oxygen can trigger necrotic death, creating a necrotic core (brown cells) in the center of the tumor. After initial growth, we "inject" a multicellular synthetic therapy.
In this (for now theoretical) synthetic therapy, "worker cells" (red) search for "cargo cells" (blue), form focal adhesions, and haul their cargo towards hypoxic (oxygen-depleted) regions in the tissue. Once they reach a region of sufficiently low oxygen, they release their cargo and resume their initial search for more cargo.  (Cancer cells are shaded according to their level of therapy-induced damage.)
Click here to jump straight to the instructions.
This model and cloud-hosted demo are part of a course on computational multicellular systems biology created and taught by Dr. Paul Macklin in the Department of Intelligent Systems Engineering at Indiana University. It is also part of the education and outreach for the IU Engineered nanoBIO Node and the NCI-funded cancer systems biology grant U01CA232137. The models are built using PhysiCell: a C++ framework for multicellular systems biology [1].
All chemical signals move by chemical diffusion in the simulated environment, using BioFVM [2] to solve the reaction-diffusion equations. A key property is the diffusion length scale. Diffusion (with a diffusion coefficient D) helps spread a signal over large distances, while decay (and uptake) (with coefficient λ) eliminates the signal to slow its spread. These effects compete to determine the characteristic distance L that a chemical signal travels.
That length scale L is given by L = sqrt( D/λ ).
Choosing D and λ can help to tune the chemical communication distance in these models.
This is released by the computational boundary (a simplified model of a far-field vasculature), and consumed by tumor cells.
We use a far-field value of 38 mmHg (about 5%), which is a typical physioxic condition. See [1] and [2] for more ferences. We use a length scale of 1000 μm, and cancer cell uptake gives a length scale of 100 μm in densely packed regions.
This factor is released by undocked cargo cells to help guide chemotaxis by worker cells.
The chemoattractant is nondimensionalized so that 0 ≤ a ≤ 1, and we initially assign a length scale of 100 μm.
This factor is released by released cargo cells to kill cancer cells.
The therapeutic is nondimensionalized so that 0 ≤ c ≤ 1, and we initially assign a length scale of 80 μm.
We use agent-based models to explore multicellular systems like this one. Each cell is modeled as a software agent with an independent state, and its own rules to change its behavior based on local environmental conditions and communication. We use PhysiCell as our modeling platform [1].
Cells can proliferate (divide into two daughter cells of half size), with division rates regulated by cell rules. In our model, cells continuously work to grow towards a "target" mature volume.
Note that birth and death are stochastic events for each cell agent: if such a process occurs at rate r, then between now (t) and soon (t + Δt), the probability of the event occurring for that agent is rΔt.
Cells can secrete chemical factors, or they can remove them (i.e., consume or uptake). This can contribute to gradients in chemical factors, and it's a key method of communication between cells. Moreover, cells can "sample" the chemical state of their surrounding environment. Secreting is sending a signal. Uptaking and sampling is receiving a signal.
Motile cells perform biased migration. After traveling for Tpersistence time (the mean persistence time), they choose a new migration direction dmigrate. Based on the environmental conditions, the cell chooses a directional bias (intended direction) dbias and a random unit vector r. The motile direction dmigrate is then determined by according to:
dmigrate = b dbias + (1-b) r.
Here, b is a bias parameter (between 0 and 1) that determines how strongly biased migration is towards dbias. If b =1, then migration is deterministic towards dbias. If b = 0, then migration is completely random (Brownian).
Once the migration direction is chosen, it is normalized, and multiplied by the cell's migration speed.
Cell agents can stick to one another within a prescribed interaction distance (some multiple of their radius), and they can exert a pushing force on neighbors. PhysiCell [1] uses potential functions to implement these simple mechanics. Notably, PhysiCell is an off-lattice model, meaning that cells can have variable sizes, and can move freely without grid artifacts.
They aren't required to move some prescribed number of spatial steps. They also can divide without checking for an open neighbor "site". Instead, they can divide and push their neighbors out of the way.
PhysiCell allows custom functions for any cell agents. We use this to model focal cell-cell adhesions as spring-like forces. If two cells i and j (with positions xi and xj) have a focal adhesion, then cell i is adhered to cell j with a spring-like force that we model as:
Fij = ε (xj - xi),
where ε is the elastic coefficient. Because PhysiCell has an inertialess formatulation (see the method paper in [1]), this is added directly to the cell's velocity, and so ε has units min-1. To model the finite deformability of cells and these elastic focual adhesions, we detach the cells if their distance (||xj - xj||) exceeds a set maximum distance rmax.
Worker cells do not divide, but they can be assigned an apoptotic death rate to model normal "wear and tear." We model workers as non-adhesive except by spring-like focal adhesions; see focal adhesions. They are made more "repulsive" (by default, fivefold more repulsive) to allow them to more readily move through dense cellular regions. Worker cells consume oxygen, but at a reduced rate (by default, 10% of the rate of cancer cells).
Unattached worker cells use the following as their migration bias direction:
dbias = ∇a,
where a is the chemoattractant released by unattached cargo cells. Note that ∇ is the gradient operator that points towards higher concentrations of a chemical factor.
Unattached worker cells test for near contact with unattached cargo cells. If they detect an unattached cargo cell with active receptor R (R ≈ 1), they form a focal adhesion (see focal adhesions).
Attached worker cells change their migration bias direction to:
dbias = -∇σ,
where σ is the oxygen.
Cargo cells do not divide, but they can be assigned an apoptotic death rate to model normal "wear and tear." We model cargo as non-adhesive except by spring-like focal adhesions; see focal adhesions. They are made more "repulsive" (by default, fivefold more repulsive) to allow them to more readily move through dense cellular regions. Cargo cells consume oxygen, but at a reduced rate (by default, 10% of the rate of cancer cells). Note that all cargo cells are non-motile.
Initially, all cargo cells express a receptor R with dimensionless value 1, which is used to control their chemical secretions. If R = 1, they secrete chemoattractant a to help guide worker cells. If R = 0 and they are unattached, then they secrete the therapeutic c.
Cargo cells permanently deactivate their receptor (set R = 0) once they are attached to a worker cell. They detach from worker cells if σ < σrelease.
Cancer cells divide at a rate proportional to oxygen availability, and they experience necrotic death at a rate that increases as oxygen decreases. We use the default parameter values from [1] for this sample model; they cannot be adjusted by users in the current demo version. Note that all cancer cells are non-motile in this example.
Beyond oxygen-driven birth and hypoxia-driven death, cancer cells are have a basic damage-repair model. Exposure to the chemotherapeutic compound c causes damage (ω, which can be repaired. We simulate this as
dω ⁄ dt = rdamage c - rrepair ω,
where c is sampled at the cell's position. Cancer cells increase their apoptotic death rate by their therapeutic death rate dtherapy, which we model as
dtherapy = ω ⁄ ωmax dmax ,
where dmax is the maximum death rate that is reach when damage (ω) reaches ωmax.
In this model, we set ωmax by solving the damage model to find the steady state damage at a maximum value of c (which is 1):
ωmax = rdamage ⁄ rrepair .
This model and cloud-hosted demo are part of a course on computational multicellular systems biology created and taught by Dr. Paul Macklin in the Department of Intelligent Systems Engineering at Indiana University. It is also part of the education and outreach for the IU Engineered nanoBIO Node and the NCI-funded cancer systems biology grant U01CA232137. The models are built using PhysiCell: a C++ framework for multicellular systems biology [1].
Modify parameters in the "Config Basics", "Microenvironment", or "User Params" tabs. Click the "Run" button once you are ready.
To view the output results, click the "Out: Plots" tab, and move the slider bar to advance through simulation frames. Note that as the simulation runs, the "# cell frames" field will increase, so you can view more simulation frames.
If there are multiple substrates defined in the Microenvironment, you can select a different one from the drop-down widget in the Plots tab. You can also fix the colormap range of values.
Note that you can download full simulation data for further exploration in your tools of choice.


git-merge-index - Run a merge for files needing merging
git merge-index [-o] [-q] <merge-program> (-a | [--] <file>*)
This looks up the <file>(s) in the index and, if there are any merge entries, passes the SHA-1 hash for those files as arguments 1, 2, 3 (empty argument if no file), and <file> as argument 4. File modes for the three files are passed as arguments 5, 6 and 7.
--
Do not interpret any more arguments as options.
-a
Run merge against all files in the index that need merging.
-o
Instead of stopping at the first failed merge, do all of them in one shot - continue with merging even when previous merges returned errors, and only return the error code after all the merges.
-q
Do not complain about a failed merge program (a merge program failure usually indicates conflicts during the merge). This is for porcelains which might want to emit custom messages.
If git merge-index is called with multiple <file>s (or -a) then it processes them in turn only stopping if merge returns a non-zero exit code.
Typically this is run with a script calling Git(cqs imitation of the merge command from the RCS package.
A sample script called git merge-one-file is included in the distribution.
ALERT ALERT ALERT! The Git "merge object order" is different from the RCS merge program merge object order. In the above ordering, the original is first. But the argument order to the 3-way merge program merge is to have the original in the middle. Don(cqt ask me why.
Examples:
torvalds@ppc970:~/merge-test> git merge-index cat MM This is MM from the original tree. # original This is modified MM in the branch A. # merge1 This is modified MM in the branch B. # merge2 This is modified MM in the branch B. # current contents
or
torvalds@ppc970:~/merge-test> git merge-index cat AA MM cat: : No such file or directory This is added AA in the branch A. This is added AA in the branch B. This is added AA in the branch B. fatal: merge program failed
where the latter example shows how git merge-index will stop trying to merge once anything has returned an error (i.e., cat returned an error for the AA file, because it didn(cqt exist in the original, and thus git merge-index didn(cqt even try to merge the MM thing).
Part of the git(1) suite


Number
Between
Two Given Numbers
|
Let Us Learn
Where is the girl putting her pencil?
The girl is putting her pencil between the ruler and the eraser.
A number can be between two numbers. Look at the
numbers below. What number is between 65 and 67?
65 66 67
66 is between 65 and 67.
Can you tell what number is between the two given numbers?
|
58 ____ 60
|
|
74 ____ 76
|
|
93 ____ 95
|
|
82 ____ 84
|
|
26 ____ 28
|
|
34 ____ 36
|
Let Us Try
A.
Write in the blank the number between the two given
numbers. The first one has been done for you.
B.
Write in the blank the number between the two given
numbers. The first one has been done for you.
Let Us Practice
A.
Answer each question. Circle (
) the letter of the correct answer.
B.
Write in the blanks the numbers that come before, between,
and after the given numbers.
.


Rafael here. It's been quite some time since our last update. But this blog post is intended to show what we did and what we have in mind for the near future.
So, now there'll be some jams in a few days (GBJam, Ludum Dare 30), and we can sum up everything so far to FAIL (or maybe trial and error).
Since our last game on the Ludum Dare, we really wanted to break free from Box2D, since it is just too much for fake physics platformers. With that, came an idea that we could create something new and better, but it's just not that simple. So I tried to implement the programming part of a full fledged physics for a platformer game . I developed the map editor and created something quite good that relies on the size of the character sprite (at will) to develop the collision box, which is quite good for movement and fighting games. But to get that right took some time. I re-programmed everything three times and I am about to do it again for the fourth time because of the latest inclusion of an entity system on libGDX 1.2 (I don't know if it's good to use it, but I have to try it anyway).
So, basically what we were trying to do was to develop a game similar to the ones that we did on LD and GGJ, but we couldn't. Not only because programming and creating art is hard, but because the themes behind Beneath Lights and Silver Lining are hard to implement in a way that the game makes sense and it's fun to play. It's not like we can't make a simple platformer with a corky sense of humor. It's just that is going to be a project where we might dedicate months of our lives to create it, so we can't just make something that we don't feel proud of.
Now let's talk about the future, and the future is our debut at GBJam and the new Ludum Dare.
GBJam is an event held by GameJolt which consists of developing a game using the game boy audio and video limitations. I believe this compo is really important for us, because the limitations are so big that we don't even need to care about other graphical stuff, like lights and such. I'm intending to work on the project solo, making the art assets and the music as well. João Vitor might work on the project as soon as he got time (he's really busy with college and internship), but he'll try come up with other game. So, at best, we might see two new SCG games in 13 days.
The game boy is quite an impressive device to tackle on, it has only four colors (three for drawing and one for the background). Its sound chip has the same architecture of the NES, except that it doesn't have a triangle wave form. I'm intending to use Famitracker to compose, and to achieve that sound, we have to create a new FDS channel and make it sound similar to the original thing. Good thing we have lots of infos on the web.
For the game, I am intending to make a strategy game. So I'm assembling a small engine to achieve that. I'm also drawing the skeleton character for the other characters, since the perspective is different from a platformer. I'm going with a Shadowrun perspective on the game, but since the GB screen is so tiny (160x144) we'll have a really small character (16x16). I'm still working on the code to make the pallete swap and as always I'm intending to make a full game (even though it might be small like the previous games). It'll be a great learning experience for me and I hope everybody likes the final product.
Next, we have Ludum Dare 30. But what can we say about this one? This time, João Vitor will participate on the 72h jam compo and I'm going to tackle on the 48h solo compo. I know it's a huge leap, but I'm confident of the power of the engine that I'm assembling so far, so does João Vitor. Even though all the code should be rewritten for the 48h compo, I'm confident that I'll be able to remake everything, it's good practice. Expect a new platform game coming from us.
So, be happy! SheetColdGames might bring four new games this month (two or three in the worst case). We hope you guys have lots of fun while playing them and I'll see you on the GBJam update!


Tom
Tom is our little friendly Gecko. At just 1.5 years old, he is full of energy, (At lease when he's had an hour under a heat lamp.)
Seam
Seam is our newest addition to the shelter. She has a very unusual fur pattern on her face. She is a very curious cat, but very playful and friendly.
Sarah
Sarah is a sweet heart. She comes from a Bad home, and is very shy and afraid of new people. Once she gets to know you, she is one of the nicest puppo's you will ever meet.
Noodle
Noodle is a calm and cute python. He is a great snake to introduce to children, and has become a icon for it. (But he is looking for a place he can call home.)


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2017 1987 PhilCon
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The 1987 Constitution
The Constitution of the Republic of the Philippines


| java.lang.Object | |
| ↳ | com.google.android.gms.fitness.FitnessActivities |
Constants representing different user activities, such as walking, running, and cycling.
Activities are used in Sessions
,
DataTypes
and in
read queries.
Session
associates an activity with all data that
was recorded during a time interval, such as
heart rate samples
taken while the user was doing aerobics
, or
wheel RPM
measured
while biking
.
Activities be stored and read using the
activity sample
and
activity segment
data
types. When samples are stored, these are automatically converted into segments by the
platform's default data source.
When reading data, the
activity segment
and activity type
bucketing
strategies can be used to aggregate data by the activities happening at the time data was
collected. This would allow, for instance, aggregating step counts taken during running and
those taken during walking separately, or to query the average heart rate during each activity.
Each activity is represented by its name, which is a string constant. These constants are also
used in intents for tracking and visualizing activity data. Internally, activities are stored as
integers inside of DataPoint
for efficiency. You can
convert between the String
and int
representations for storage using
setActivity(String)
and
asActivity()
.
A subset of the activities can be detected by
Activity Recognition
and are
listed in DetectedActivity
.
| Constants | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| String | AEROBICS | The user is performing aerobic exercises. | |||||||||
| String | BADMINTON | The user is playing badminton. | |||||||||
| String | BASEBALL | The user is playing baseball. | |||||||||
| String | BASKETBALL | The user is playing basketball. | |||||||||
| String | BIATHLON | The user is practicing biathlon (combination of cross-country skiing and rifle shooting). | |||||||||
| String | BIKING | The user is on a bicycle. | |||||||||
| String | BIKING_HAND | The user is handcycling (or handbiking). | |||||||||
| String | BIKING_MOUNTAIN | The user is mountain biking. | |||||||||
| String | BIKING_ROAD | The user is cycling on a road or other smooth surface. | |||||||||
| String | BIKING_SPINNING | The user is indoor cycling (spinning) on a specialized stationary bike. | |||||||||
| String | BIKING_STATIONARY | The user is cycling on a stationary bike. | |||||||||
| String | BIKING_UTILITY | The user is cycling as a means of transport. | |||||||||
| String | BOXING | The user is boxing. | |||||||||
| String | CALISTHENICS | The user is performing calisthenics exercises. | |||||||||
| String | CIRCUIT_TRAINING | The user is performing circuit training exercises. | |||||||||
| String | CRICKET | The user is playing cricket. | |||||||||
| String | CURLING | The user is practicing curling. | |||||||||
| String | DANCING | The user is dancing. | |||||||||
| String | DIVING | The user is diving into water, from a platform or spring board. | |||||||||
| String | ELLIPTICAL | The user is using an elliptical trainer or cross-trainer machine. | |||||||||
| String | ERGOMETER | The user is using an ergometer machine. | |||||||||
| String | EXTRA_STATUS | Name for the String extra containing the status of an activity. | |||||||||
| String | FENCING | The user is fencing. | |||||||||
| String | FOOTBALL_AMERICAN | The user is playing American football (known as football in the United States). | |||||||||
| String | FOOTBALL_AUSTRALIAN | The user is playing Australian-rules football. | |||||||||
| String | FOOTBALL_SOCCER | The user is playing association football (known as soccer in the United States). | |||||||||
| String | FRISBEE_DISC | The user is playing with a Frisbee disc. | |||||||||
| String | GARDENING | The user is gardening. | |||||||||
| String | GOLF | The user is playing golf. | |||||||||
| String | GYMNASTICS | The user is practicing gymnastics. | |||||||||
| String | HANDBALL | The user is playing handball. | |||||||||
| String | HIKING | The user is hiking. | |||||||||
| String | HOCKEY | The user is playing hockey. | |||||||||
| String | HORSEBACK_RIDING | The user is horseback riding. | |||||||||
| String | HOUSEWORK | The user is doing house work. | |||||||||
| String | ICE_SKATING | The user is ice-skating. | |||||||||
| String | IN_VEHICLE | The user is in a vehicle, such as a car. | |||||||||
| String | JUMP_ROPE | The user is jumping rope. | |||||||||
| String | KAYAKING | The user is kayaking. | |||||||||
| String | KETTLEBELL_TRAINING | The user is training with a kettlebell. | |||||||||
| String | KICKBOXING | The user is kickboxing. | |||||||||
| String | KICK_SCOOTER | The user is riding a kick scooter. | |||||||||
| String | KITESURFING | The user is kite-surfing. | |||||||||
| String | MARTIAL_ARTS | The user is practicing martial arts. | |||||||||
| String | MEDITATION | The user is meditating. | |||||||||
| String | MIME_TYPE_PREFIX | The common prefix for activity MIME types. | |||||||||
| String | MIXED_MARTIAL_ARTS | The user is practicing mixed martial arts (MMA). | |||||||||
| String | ON_FOOT | The user is on foot, walking or running. | |||||||||
| String | OTHER | The user is performing a generic fitness activity, which isn't classified. | |||||||||
| String | P90X | The user is performing P90X exercises. | |||||||||
| String | PARAGLIDING | The user is paragliding. | |||||||||
| String | PILATES | The user is performing pilates exercises. | |||||||||
| String | POLO | The user is playing polo (team sport on horseback). | |||||||||
| String | RACQUETBALL | The user is playing racquetball. | |||||||||
| String | ROCK_CLIMBING | The user is rock climbing. | |||||||||
| String | ROWING | The user is rowing. | |||||||||
| String | ROWING_MACHINE | The user is using a rowing machine. | |||||||||
| String | RUGBY | The user is playing rugby. | |||||||||
| String | RUNNING | The user is running. | |||||||||
| String | RUNNING_JOGGING | The user is jogging. | |||||||||
| String | RUNNING_SAND | The user is running on sand. | |||||||||
| String | RUNNING_TREADMILL | The user is running on a treadmill. | |||||||||
| String | SAILING | The user is sailing. | |||||||||
| String | SCUBA_DIVING | The user is scuba diving. | |||||||||
| String | SKATEBOARDING | The user is skateboarding. | |||||||||
| String | SKATING | The user is skating. | |||||||||
| String | SKATING_CROSS | The user is cross skating. | |||||||||
| String | SKATING_INDOOR | The user is skating in-doors. | |||||||||
| String | SKATING_INLINE | The user is inline skating (roller-blading). | |||||||||
| String | SKIING | The user is skiing. | |||||||||
| String | SKIING_BACK_COUNTRY | The user is back-country skiing. | |||||||||
| String | SKIING_CROSS_COUNTRY | The user is cross-country skiing. | |||||||||
| String | SKIING_DOWNHILL | The user is downhill skiing. | |||||||||
| String | SKIING_KITE | The user is kite skiing. | |||||||||
| String | SKIING_ROLLER | The user is roller skiing (non-snow). | |||||||||
| String | SLEDDING | The user is sledding. | |||||||||
| String | SLEEP | The user is sleeping. | |||||||||
| String | SLEEP_AWAKE | The user is in an awake period in the middle of sleep. | |||||||||
| String | SLEEP_DEEP | The user is in a deep sleep cycle. | |||||||||
| String | SLEEP_LIGHT | The user is in a light sleep cycle. | |||||||||
| String | SLEEP_REM | The user is in a REM sleep cycle. | |||||||||
| String | SNOWBOARDING | The user is snowboarding. | |||||||||
| String | SNOWMOBILE | The user is on a snow mobile. | |||||||||
| String | SNOWSHOEING | The user is snow-shoeing. | |||||||||
| String | SQUASH | The user is playing Squash. | |||||||||
| String | STAIR_CLIMBING | The user is climbing stairs. | |||||||||
| String | STAIR_CLIMBING_MACHINE | The user is using a stair-climbing machine. | |||||||||
| String | STANDUP_PADDLEBOARDING | The user is on a stand-up paddle board. | |||||||||
| String | STATUS_ACTIVE | Status indicating the activity has started. | |||||||||
| String | STATUS_COMPLETED | Status indicating the activity has ended. | |||||||||
| String | STILL | The user is still (not moving). | |||||||||
| String | STRENGTH_TRAINING | The user is strength training. | |||||||||
| String | SURFING | The user is surfing. | |||||||||
| String | SWIMMING | The user is swimming. | |||||||||
| String | SWIMMING_OPEN_WATER | The user is swimming in open waters. | |||||||||
| String | SWIMMING_POOL | The user is swimming in a swimming pool. | |||||||||
| String | TABLE_TENNIS | The user is playing table tennis (or ping-pong). | |||||||||
| String | TEAM_SPORTS | The user is playing a team sport. | |||||||||
| String | TENNIS | The user is playing tennis. | |||||||||
| String | TILTING |
This is a synthetic activity used by the
Activity Recognition API to
indicate that the device angle relative to gravity changed significantly between the sample
immediately before and immediately after the "tilting" sample.
|
|||||||||
| String | TREADMILL | The user is on a treadmill (either walking or running). | |||||||||
| String | UNKNOWN | The current activity is not known. | |||||||||
| String | VOLLEYBALL | The user is playing volleyball. | |||||||||
| String | VOLLEYBALL_BEACH | The user is playing beach volleyball. | |||||||||
| String | VOLLEYBALL_INDOOR | The user is playing indoor volleyball. | |||||||||
| String | WAKEBOARDING | The user is wake boarding. | |||||||||
| String | WALKING | The user is walking. | |||||||||
| String | WALKING_FITNESS | The user is walking at a moderate to high pace, for fitness. | |||||||||
| String | WALKING_NORDIC | The user is performing Nordic walking (with poles). | |||||||||
| String | WALKING_TREADMILL | The user is walking on a treadmill | |||||||||
| String | WATER_POLO | The user is playing water polo. | |||||||||
| String | WEIGHTLIFTING | The user is weight lifting. | |||||||||
| String | WHEELCHAIR | The user is on a wheel chair. | |||||||||
| String | WINDSURFING | The user is wind surfing. | |||||||||
| String | YOGA | The user is performing Yoga poses. | |||||||||
| String | ZUMBA | The users is performing Zumba exercises. |
| Public Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Returns the MIME type for a particular activity.
|
|
[Expand]
Inherited Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
From class
java.lang.Object
|
The user is performing aerobic exercises.
The user is practicing biathlon (combination of cross-country skiing and rifle shooting).
The user is handcycling (or handbiking).
The user is mountain biking.
The user is cycling on a road or other smooth surface.
The user is indoor cycling (spinning) on a specialized stationary bike.
The user is cycling on a stationary bike.
The user is cycling as a means of transport.
The user is performing calisthenics exercises.
The user is performing circuit training exercises.
The user is diving into water, from a platform or spring board.
The user is using an elliptical trainer or cross-trainer machine.
The user is using an ergometer machine.
Name for the String extra containing the status of an activity. This is a mandatory
extra for ACTION_TRACK
intents, and holds one of the following values:
STATUS_ACTIVE
- the activity has started
STATUS_COMPLETED
- the activity has ended
The user is playing American football (known as football in the United States).
The user is playing Australian-rules football.
The user is playing association football (known as soccer in the United States).

The user is practicing gymnastics.
The user is horseback riding.
The user is in a vehicle, such as a car.
The user is training with a kettlebell.
The user is riding a kick scooter.
The user is practicing martial arts.
The common prefix for activity MIME types. The MIME type for a particular activity type will be this prefix followed by the activity name. Examples:
vnd.google.fitness.activity/walking vnd.google.fitness.activity/biking.mountain vnd.google.fitness.activity/jump_ropeThe names for all activities are described by the constants in this class. The full MIME type can be computed using
getMimeType(String)
The user is practicing mixed martial arts (MMA).
The user is on foot, walking or running. It's preferred to use the more specific activity when known.
The user is performing a generic fitness activity, which isn't classified.
Unlikeunknown
, which gives no indication as to what the user is doing,
"other" indicates that the user is performing a fitness-related activity.
The "other" activity is most useful to collect user input, for instance when an app wants to
give the user the ability to choose an activity that's not part of the regular activity set
for the app.
Note that an activity classified as "other" could represent any of the other activities in
this activity list, as well as an activity not in this list.
The user is performing pilates exercises.
The user is playing polo (team sport on horseback).
The user is playing racquetball.
The user is using a rowing machine.
The user is running on a treadmill.
The user is skating in-doors.
The user is inline skating (roller-blading).
The user is back-country skiing.
The user is cross-country skiing.
The user is downhill skiing.
The user is roller skiing (non-snow).
The user is in an awake period in the middle of sleep.
The user is in a deep sleep cycle.
The user is in a light sleep cycle.
The user is climbing stairs.
The user is using a stair-climbing machine.
The user is on a stand-up paddle board.
Status indicating the activity has started.
Status indicating the activity has ended.
The user is strength training.
The user is swimming in open waters.
The user is swimming in a swimming pool.
The user is playing table tennis (or ping-pong).
The user is playing a team sport.
This is a synthetic activity used by the
Activity Recognition API
to
indicate that the device angle relative to gravity changed significantly between the sample
immediately before and immediately after the "tilting" sample.
The user is on a treadmill (either walking or running).
The current activity is not known. In this case, the activity could be any of the activities described in this class, or a completely different one.
The user is playing beach volleyball.
The user is playing indoor volleyball.
The user is walking at a moderate to high pace, for fitness.
The user is performing Nordic walking (with poles).
The user is walking on a treadmill
The user is weight lifting.
Returns the MIME type for a particular activity. The MIME type is used in intents for viewing a session and tracking an activity.
| activity | the desired activity. |
|---|


{% extends "base.html" %} {% block title %}URL Preview{% endblock %} {% block content %} The following URL:
consists of {{ url.target_url_len }} characters, and resulted in a URL which is {{ url.short_url_len }} characters long: {% if preview_image_small %} preview image (click for larger view): {% endif %} {% endblock %}


--- layout: default --- {% include header.html %}
Proof of concept of a bookmarklet script that interacts with Google Docs to store data.
This is the Bookmarklet link.
Data are stored on this spreadsheet.


Don’t you see the grief of those who have lost loved ones since 1975? Hasina asks
by bdnews24.comSheikh Hasina has questioned the criticisms of her government for “not allowing jailed BNP chief Khaleda Zia to go abroad for treatment”.
Citing the spate of deadly violence during the BNP protests, she asked the critics if they saw the grief of those who had lost their loved ones since 1975, including the Aug 15 massacre of Bangabandhu Sheikh Mujibur Rahman’s family and the Nov 3 killings of four national leaders inside Dhaka jail that year.
“Many are shedding fake tears for Khaleda Zia who is in jail after conviction,” the prime minister told a Jail Killing Day event organised by the ruling Awami League at the Krishibid Institution in the capital on Sunday.
“Many were burnt alive, hundreds of thousands of our leaders and activists were tortured after 2001 and some of them were crippled. Some are living with grenade splinters in their bodies. Many have died while many others lived on helplessly.
 Don’t you think their pain is real?” she asked.
On Nov 3, 1975, four leaders of the wartime national government—acting president Syed Nazrul Islam, prime minister Tajuddin Ahmad and cabinet ministers M Mansur Ali and AHM Qamaruzzaman—were killed inside Dhaka Central Jail.
Months earlier, on Aug 15 that year, Bangabandhu and most members of his family were killed at his home. Bangabandhu’s daughters Hasina and Sheikh Rehana survived as they were abroad.
Hasina said BNP founder Ziaur Rahman, who became the first military ruler of the country after the assassination of Bangabandhu, had appointed as prime minister Shah Azizur Rahman, who had been sent to the UN to object to Bangladesh’s independence.
“Razakars like Abdul Alim and many of those who carried out genocide became their ministers. It’s the most unfortunate chapter in Bangladesh’s history,” she said.
The prime minister said Khaleda had not allowed former army chief and injured freedom fighter M Mustafizur Rahman to receive treatment at the Combined Military Hospital and insulted him when the BNP chief had been in power.
“They did not only encourage the killers, but also insulted an injured freedom fighter, for whose sacrifice we achieved independence. I wonder who would cry for them,” she said.
Hasina also criticised the BNP for making Khaleda’s son Tarique Rahman party chief in exile despite his conviction in many cases.
“I doubt whether those in the BNP have any backbone, self-esteem,” she said.
MASTERMINDS OF KILLINGS WILL BE TRIED
The prime minister said the war criminals and killers of Bangabandhu and the four national leaders have been tried, but the Awami League government has noy been able to try those behind the killings.
“Our efforts will continue. But the day will come when these conspirators will be caught as history cannot be wiped off. Someone will surely do it.
“Many had thought the name of Bangabandhu would not return after it was erased from history. But that did not happen,” she said.
Hasina also said the government was looking for the killers who had fled abroad, and trying to bring them back.


This page contains the documentation for the classes and methods in the mcautograder package.
mcautograder
::DOCUMENTATION::


Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally.
| Version: | 1.3-1 |
| Depends: | R (≥ 3.3.0) |
| Imports: | graphics, gstat, magrittr, methods, mvtnorm, purrr, raster, whisker |
| Suggests: | dplyr, GGally, gridExtra, knitr, png, readr, sp, testthat |
| Published: | 2018-07-03 |
| Author: | Kasia Sawicka [aut, cre], Gerard Heuvelink [aut], Dennis Walvoort [aut], Stefan van Dam [ctb], Damiano Luzzi [ctb] |
| Maintainer: | Kasia Sawicka <katwic55 at ceh.ac.uk> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | spup results |
| Reference manual: | spup.pdf |
| Vignettes: |
Case study with cross-correlated variables Case study with spatially variable standard deviation - slope calculations Case study with calling external model Case study with categorical data - calculating tax depending on a building function |
| Package source: | spup_1.3-1.tar.gz |
| Windows binaries: | r-devel: spup_1.3-1.zip, r-release: spup_1.3-1.zip, r-oldrel: spup_1.3-1.zip |
| OS X binaries: | r-release: spup_1.3-1.tgz, r-oldrel: spup_1.3-1.tgz |
| Old sources: | spup archive |
Please use the canonical form https://CRAN.R-project.org/package=spup to link to this page.


WITNESS LIST S/C on Base Realignment and Closure Committee April 13, 2005 - 1:30P SB 1105 FOR: Barrera, Enrique Councilman (City of San Antonio), San Antonio, TX Kunau, Camila Assistant City Attorney (City of San Antonio), San Antonio, TX Oliver, Harold Government Relations Consultant (City of San Antonio), San Antonio, TX


Chemical equations
- In all chemical reactions, the atoms in the reactants rearrange to make new products. The products are new combinations of the atoms; they have different physical and chemical properties from the reactants.
- Total mass before and after a reaction is the same (conservation of mass), no atoms are made or destroyed. Product mass is difficult to measure if one of the products is a gas; it may escape into the air.
- Word equations describe the chemicals involved in a reaction. Their usefulness is limited: they do not say how much of each reactant/product is present, and they are written only in one language.
- Symbol equations show exactly how much of the reactants and products are involved in a chemical reaction. These symbols are internationally recognised and understood.
- Symbol equations must always balance, with the same number of each atom on each side of the equation. They should also show the state of each product: solid (s), liquid (l), aqueous solution (aq), or gas (g).
Example balancing of an equation
When tin oxide is heated with hydrogen, these form tin and water vapour. The unbalanced equation shows 2 oxygen atoms on the left, 1 on the right:
SnO2 + H2 ⇒ Sn + H2O
We balance any unbalanced parts first; we double the oxygen atoms by doubling the water molecules:
SnO2 + H2 ⇒ Sn + 2H2O
But this gives an unbalanced left side: the right side has 4 hydrogens, the left has 2. We double the hydrogen atoms on the left:
SnO2(s) + 2H2(g) ⇒ Sn(s) + 2H2O(g)
We must also add the physical states.
Tests for CO2, H2, O2 (Edexcel)
- Test for carbon dioxide (also AQA): Limewater (calcium hydroxide) becomes cloudy/milky in the presence of carbon dioxide. The milky substance is white insoluble calcium carbonate:
Ca(OH)2(aq) + CO2(g) ⇒
CaCO3(s) + H2O(l)
- Test for hydrogen: A lighted splint will ‘pop’ with a small squeaky explosion, if held in a test tube that contains hydrogen mixed with air.
- Test for oxygen: A glowing splint (i.e. lit but then blown out) will relight vigorously if put in a test tube that contains oxygen.
Reactions of calcium compounds (Edexcel/AQA)
- Calcium oxide combines with water in a vigorous reaction, initially making a white solid; this dissolves to make limewater as more water is added:
CaO(s) + H2O(aq) ⇒ Ca(OH)2(s)
- Neutralisation reactions: calcium oxide (CaO), calcium hydroxide (Ca(OH)2) and calcium carbonate (CaCO3) are alkalis, and therefore neutralise acids. They have practical uses in farming to treat acidic soils.
- Coal in power stations burns to release sulfur dioxide, an acidic gas that contributes to acid rain:
S(s) + O2(g) ⇒ SO2(g)
Calcium carbonate is used in power stations to prevent this; it reacts with and neutralises the acidic gases.


Stanford CoCoLab
You will watch {{}} short video clips and be asked some questions about them. The HIT should take approximately {{}} minutes. Please pay attention, thanks!
Legal information: By answering the following questions, you are participating in a study being performed by cognitive scientists in the Stanford Department of Psychology. If you have questions about this research, please contact Erin Bennett at<EMAIL_ADDRESS>or Noah Goodman, at<EMAIL_ADDRESS>You must be at least 18 years old to participate. Your participation in this research is voluntary. You may decline to answer any or all of the following questions. You may decline further participation, at any time, without adverse consequences. Your anonymity is assured; the researchers who have requested your participation will not receive any personal information about you.
Note however that we have recently been made aware that your public Amazon.com profile can be accessed via your worker ID if you do not choose to opt out. If you would like to opt out of this feature, you may follow instructions available here.
On each trial, a short video clip will play. When the video clip is finished, a question will appear.
Please fill in the blank as best you can.
Please watch the video and answer the question that appears.
{{}} because .
(Note: you can replay the video if that helps.)
Please fill in the blank before continuing.
Answering these questions is optional, but will help us understand your answers.
Did you read the instructions and do you think you did the HIT correctly?
Were there any problems or bugs in the experiment?
What do you think is a fair price for the work you did?
Did you enjoy the hit?
Gender:
Age:
Level Of Education:
Native Language:
Any additional comments about this experiment:
Click here for links to the full videos.
Thanks for your Time!
The video clips you saw were from:


flex
one item
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Eventetia is a event management company based in Durgapur WB. We are composed of creative designers and talented engineers.
We guarantee remarkable services for events like marraige, birthday party, Family Function, Anniversary Party, Farewell Party, College Events with affordable pricing and exotic locations.
Our leadership in event management coorporate world is quite sustainable. We serve our customers with honour and pride. Our motto is to provide overall satisfaction and a lifetime experience.
Our promise is very adequate and stiff. Join and work with us to experience what we can provide to you for herthrobing exporsures.
Our country is the evidence of some of the spectacular and delicious dishes around the globe. We do try to sustain the fame to the next level.
Our packages involves equipents like DJ, Stage, Mike and High Definition Speakers for your extraordinary enterntainment.
“Productivity is never an accident. It is always the result of a commitment to excellence, intelligent planning, and focused effort.” We assure you a lifetime experience and we wont let you down.
There is no more lovely, friendly and charming relationship, communion or company than a good marriage. Proceed
I believe that every single event in life happens in an opportunity to choose love over fear.Proceed
It's the perfect time to give a good start to another year. It's the time to feel good about everything. May you get everything, you wish with every passing year. Happy Birthday! Proceed
“The two hardest things to say in life are hello for the first time and goodbye for the last.” “Great is the art of beginning, but greater is the art of ending.”Proceed
Even amidst difficult times, tough situations and harsh circumstances, you have proved that a happy marriage can sail you through any storm of life.Proceed
The most important thing in the world is family and love. The bond that links your true family is not one of blood, but of respect and joy in each other's life. Proceed
We seek your kind response, suggestions and relationship.
Bengal College of Engineering
Durgapur WB
713212 IND
<EMAIL_ADDRESS>Phone: (+91) 555 1212
Mobile: (+91) 555 0100
Fax: (+91) 555 0101


 At least there are turtles and
taco pizza there.
Mars
Mars is not just red, it is also brown and kinda greyish!
There is a large amount of flat places to run, and sharp
rocks to clamber up! Safe and fun, Mars is great for families.

Mercury
Mercury is a firey gem! It's really firey though. Don't touch anything. Enjoy the barren charred scenery!
Saturn
Saturn has rings. Everybody likes the rings. Whooooo Rings.
It also has cool moons with ice and dirt and lava and stuff.
Tragically optimistic scientists think there could be life
on a couple od them.
Jupiter
Jupiter is the big one. If you go outside right now and look, you will either be able to see it, unable to see it, or hurt your
eyes staring at the sun. The sun is not Jupiter. Stop it.
Neptune
Neptune is big, but not that big. It looks kinda green-blue. there
really isn't anything special about it. It's the B-/C+ student of
planetary gas giants.
Uranus
Nobody really knows much about Uranus because they are really
immature and get caught up in making fun of it's name instead of
studying it. Too bad, because it's a pretty color and might be
awesome.
Pluto
This is a spot for the real super adventure person type. It's
not even a planet really! Staying there is like going camping
at the bottom of the ocean.


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It has been said that life is a school of probability. A major effect of probability theory on everyday life is in risk assessment. Let's suppose you have an exam and you are not so well prepared. There are 20 possible subjects, but you only had time to prepare for 15. If two subjects are given, what chances do you have to be familiar with both? This is an example of a simple question inspired by the world in which we live today. Life is a very complex chain of events and almost everything can be imagined in terms of probabilities.
Rolling a die once
We define an event as any collection of outcomes of an experiment. Thus, an event is a subset of the sample space S. If we denote an event by E, we could say that E⊆S. If an event consists of a single outcome in the sample space, it is called a simple event. Events which consist of more than one outcome are called compound events.
Sample space S = {1, 2, 3, 4, 5, 6} Tossing two coins Sample space S = {(Heads, Heads), (Heads, Tails), (Tails, Heads), (Tails, Tails)} What we are actually interested in is the probability of a certain event to occur, or P(E). By definition, P(E) is a real number between 0 and 1, where 0 denotes the impossible event and 1 denotes the certain event (or the whole sample space). As stated earlier, each possible outcome is represented by exactly one point in the sample space. This leads us to the following formula: That is, the probability of an event to occur is calculated by dividing the number of favorable outcomes (according to the event E) by the total number of outcomes (according to the sample space S). In order to represent the relationships among events, you can apply the known principles of set theory.  As we have seen previously, the sample space is S = {1, 2, 3, 4, 5, 6}. Let's now consider the following events:
Event A = 'score > 3' = {4, 5, 6}
Event B = 'score is odd' = {1, 3, 5} Event C = 'score is 7' = ∅ A∪B ='the score is > 3 or odd or both' = {1, 3, 4, 5, 6} A∩B ='the score is > 3 and odd' = {5} A' = 'event A does not occur' = {1, 2, 3} We have:
P(A∪B) = 5/6
P(A∩B) = 1/6 P(A') = 1 - P(A) = 1 - 1/2 = 1/2 P(C) = 0 The first step when trying to solve a probability problem is to be able to recognize the sample space. After that, you basically have to determine the number of favorable outcomes. This is the classical approach, but the way we implement it may vary from problem to problem. Let's take a look at QuizShow (SRM 223, Div 1 - Easy). The key to solving this problem is to take into account all the possibilities, which are not too many. After a short analysis, we determine the sample space to be the following:
S = { (wager 1 is wrong, wager 2 is wrong, you are wrong),
(wager 1 is wrong, wager 2 is wrong, you are right), (wager 1 is wrong, wager 2 is right, you are wrong), (wager 1 is wrong, wager 2 is right, you are right), (wager 1 is right, wager 2 is wrong, you are wrong), (wager 1 is right, wager 2 is wrong, you are right), (wager 1 is right, wager 2 is right, you are wrong), (wager 1 is right, wager 2 is right, you are right) } The problem asks you to find a wager that maximizes the number of favorable outcomes. In order to compute the number of favorable outcomes for a certain wager, we need to determine how many points the three players end with for each of the 8 possible outcomes. The idea is illustrated in the following program: int wager (vector Let's now consider a series of n independent events: E1, E2, ... , En. Two surprisingly common questions that may appear (and many of you have already encountered) are the following:
The best way to answer the second question is to first determine the probability that no event will occur and then, take the complement. We have: These formulae are very useful and you should try to understand them well before you move. BirthdayOdds A good example to illustrate the probability concepts discussed earlier is the classical "Birthday Paradox". It has been shown that if there are at least 23 people in a room, there is a more than 50% chance that at least two of them will share the same birthday. While this is not a paradox in the real sense of the word, it is a mathematical truth that contradicts common intuition. The TopCoder problem asks you to find the minimum number of people in order to be more than minOdds% sure that at least two of them have the same birthday. One of the first things to notice about this problem is that it is much easier to solve the complementary problem: "What is the probability that N randomly selected people have all different birthdays?". The strategy is to start with an empty room and put people in the room one by one, comparing their birthdays with those of them already in the room: int minPeople (int minOdds, int days) { int nr; double target, p; target = 1 - (double) minOdds / 100; nr = 1; p = 1; while (p > target) { p = p * ( (double) 1 - (double) nr / days); nr ++; } return nr; } Sometimes a probability problem can be quite tricky. As we have seen before, the 'Birthday Paradox' tends to contradict our common sense. But the formulas prove to us that the answer is indeed correct. Formulas can help, but to become a master of probabilities you need one more ingredient: "number sense" . This is partly innate ability and partly learned ability acquired through practice. Take this quiz to assess your number sense and to also become familiar with some of the common probability misconceptions. Step by Step Probability Computation In this chapter we will discuss some real TopCoder problems in which the occurrence of an event is influenced by occurrences of previous events. We can think of it as a graph in which the nodes are events and the edges are dependencies between them. This is a somewhat forced analogy, but the way we compute the probabilities for different events is similar to the way we traverse the nodes of a graph. We start from the root, which is the initial state and has a probability of 1. Then, as we consider different scenarios, the probability is distributed accordingly. NestedRandomness This problem looked daunting to some people, but for those who figured it out, it was just a matter of a few lines. For the first step, it is clear what do we have to do: the function random(N) is called and it returns a random integer uniformly distributed in the range 0 to N-1. Thus, every integer in this interval has a probability of 1/N to occur. If we consider all these outcomes as input for the next step, we can determine all the outcomes of the random(random(N)) call. To understand this better, let's work out the case when N = 4.
The source code for this problem is given below: double probability (int N, int nestings, int target) { int I, J, K; double A[1001], B[2001]; // A[I] represents the probability of number I to appear for (I = 0; I < N ; I++) A[I] = (double) 1 / N; for (K = 2; K ≤ nestings; K++) { for (I = 0; I < N; I++) B[I] = 0; // for each I between 0 and N-1 we call the function "random(I)" // as described in the problem statement for (I = 0; I < N; I++) for (J = 0; J < I; J++) B[J] += (double) A[I] / I; for (I = 0; I < N; I++) A[I] = B[I]; } return A[target]; } ChessKnight - assign each square a probability and for every move check the squares one by one to compute the probabilities for the next move. DiceThrows - determine the probability of each possible outcome for both players and then compare the results. RockSkipping - the same approach, just make sure you got the lake pattern correctly. PointSystem - represent the event space as a matrix of possible scores (x, y). VolleyBall - similar to PointSystem, but the scores may go up pretty high. Let's now take a look at another TopCoder problem, GeneticCrossover, which deals with conditional probability. Here, you are asked to predict the quality of an animal, based on the genes it inherits from its parents. Considering the problem description, there are two situations that may occur: a gene does not depend on another gene, or a gene is dependent. For the first case, consider p the probability that the gene is to be expressed dominantly. There are only 4 cases to consider:
int n, d[200]; double power[200]; // here we determine the characteristic for each gene (in power[I] // we keep the probability of gene I to be expressed dominantly) double detchr (string p1a, string p1b, string p2a, string p2b, int nr) { double p, p1, p2; p = p1 = p2 = 1.0; if (p1a[nr] ≤ 'Z') p1 = p1 - 0.5; // is a dominant gene if (p1b[nr] ≤ 'Z') p1 = p1 - 0.5; if (p2a[nr] ≤ 'Z') p2 = p2 - 0.5; if (p2b[nr] ≤ 'Z') p2 = p2 - 0.5; p = 1 - p1 * p2; if (d[nr] != 1) power[nr] = p * detchr (p1a, p1b, p2a, p2b, d[nr]); // gene 'nr' is dependent on gene d[nr] else power[nr] = p; return power[nr]; } double cross (string p1a, string p1b, string p2a, string p2b, vector Randomized Algorithms We call randomized algorithms those algorithms that use random numbers to make decisions during their execution. Unlike deterministic algorithms that for a fixed input always give the same output and the same running-time, a randomized algorithm behaves differently from execution to execution. Basically, we distinguish two kind of randomized algorithms:
The main goal of randomized algorithms is to build faster, and perhaps simpler solutions. Being able to tackle "harder" problems is also a benefit of randomized algorithms. As a result, these algorithms have become a research topic of major interest and have already been utilized to more easily solve many different problems. An interesting question is whether such an algorithm may become useful in TopCoder competitions. Some problems have many possible solutions, where a number of which are also optimal. The classical approach is to check them one by one, in an established order. But it cannot be guaranteed that the optima are uniformly distributed in the solution domain. Thus, a deterministic algorithm may not find you an optimum quickly enough. The advantage of a randomized algorithm is that there are actually no rules to set about the order in which the solutions are checked and for the cases when the optima are clustered together, it usually performs much better. See QueenInterference for a TopCoder example. Randomized algorithms are particularly useful when faced with malicious attackers who deliberately try to feed a bad input to the algorithm. Such algorithms are widely used in cryptography, but it sometimes makes sense to also use them in TopCoder competitions. It may happen that you have an efficient algorithm, but there are a few degenerate cases for which its running time is significantly slower. Assuming the algorithm is correct, it has to run fast enough for all inputs. Otherwise, all the points you earned for submitting that particular problem are lost. This is why here, on TopCoder, we are interested in worst case execution time. To challenge or not to challenge? Another fierce coding contest is now over and you have 15 minutes to look for other coders' bugs. The random call in a competitor's submission is likely to draw your attention. This will most likely fall into one of two scenarios:
Will "random" really work? In most optimizing problems, the ratio between the number of optimal solutions and the total number of solutions is not so obvious. An easy, but not so clever solution, is to simply try generating different samples and see how the algorithm behaves. Running such a simulation is usually pretty quick and may also give you some extra clues in how to actually solve the problem. Max = 1000000; attempt = 0; while (attempt < Max) { answer = solve_random (...); if (better (answer, optimum)) // we found a better solution { optimum = answer; cout << "Solution " << answer << " found on step " << attempt << "\n"; } attempt ++; } Level 1 PipeCuts - SRM 233 BirthdayOdds - SRM 174 BenfordsLaw - SRM 155 QuizShow - SRM 223 Level 2 Collision - SRM 153 ChessKnight - TCCC05 Round 1 ChipRace - SRM 199 DiceThrows - SRM 242 TopFive - SRM 243 ProbabilityTree - SRM 174 OneArmedBandit - SRM 226 RangeGame - SRM 174 YahtzeeRoll - SRM 222 BagOfDevouring - SRM 184 VolleyBall - TCO04 Round 3 RandomFA - SRM 178 PackageShipping - TCCC05 Round 3 QueenInterference - SRM 208 BaseballLineup - TCO '03 Finals Level 3 GeneticCrossover - TCO04 Qual 3 NestedRandomness - TCCC05 Qual 5 RockSkipping - TCCC '04 Round 1 PointSystem - SRM 174 AntiMatter - SRM 179 TestScores - SRM 226 Hangman42 - SRM 229 KingOfTheCourt - SRM 222 WinningProbability - SRM 218 Disaster - TCCC05 Semi 1
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class Tilt::Mapping
Tilt::Mapping associates file extensions with template implementations.
mapping = Tilt::Mapping.new mapping.register(Tilt::RDocTemplate, 'rdoc') mapping['index.rdoc'] # => Tilt::RDocTemplate mapping.new('index.rdoc').render
You can use {#register} to register a template class by file extension, {#registered?} to see if a file extension is mapped, {#[]} to lookup template classes, and {#new} to instantiate template objects.
Mapping also supports lazy template implementations. Note that regularly registered template implementations always have preference over lazily registered template implementations. You should use {#register} if you depend on a specific template implementation and {#register_lazy} if there are multiple alternatives.
mapping = Tilt::Mapping.new mapping.register_lazy('RDiscount::Template', 'rdiscount/template', 'md') mapping['index.md'] # => RDiscount::Template
{#register_lazy} takes a class name, a filename, and a list of file extensions. When you try to lookup a template name that matches the file extension, Tilt will automatically try to require the filename and constantize the class name.
Unlike {#register}, there can be multiple template implementations registered lazily to the same file extension. Tilt will attempt to load the template implementations in order (registered last would be tried first), returning the first which doesn’t raise LoadError.
If all of the registered template implementations fails, Tilt will raise the exception of the first, since that was the most preferred one.
mapping = Tilt::Mapping.new mapping.register_lazy('Bluecloth::Template', 'bluecloth/template', 'md') mapping.register_lazy('RDiscount::Template', 'rdiscount/template', 'md') mapping['index.md'] # => RDiscount::Template
In the previous example we say that RDiscount has a *higher priority* than BlueCloth. Tilt will first try to `require “rdiscount/template”`, falling back to `require “bluecloth/template”`. If none of these are successful, the first error will be raised.
Attributes
@private
@private
Public Class Methods
# File lib/tilt/mapping.rb, line 52 def initialize @template_map = Hash.new @lazy_map = Hash.new { |h, k| h[k] = [] } end
Public Instance Methods
Looks up a template class based on file name and/or extension.
@example
mapping['views/hello.erb'] # => Tilt::ERBTemplate mapping['hello.erb'] # => Tilt::ERBTemplate mapping['erb'] # => Tilt::ERBTemplate
@return [template class]
# File lib/tilt/mapping.rb, line 150 def [](file) _, ext = split(file) ext && lookup(ext) end
Finds the extensions the template class has been registered under. @param [template class] template_class
# File lib/tilt/mapping.rb, line 181 def extensions_for(template_class) res = [] template_map.each do |ext, klass| res << ext if template_class == klass end lazy_map.each do |ext, choices| res << ext if choices.any? { |klass, file| template_class.to_s == klass } end res end
@private
# File lib/tilt/mapping.rb, line 58 def initialize_copy(other) @template_map = other.template_map.dup @lazy_map = other.lazy_map.dup end
Instantiates a new template class based on the file.
@raise [RuntimeError] if there is no template class registered for the
file name.
@example
mapping.new('index.mt') # => instance of MyEngine::Template
@see Tilt::Template.new
# File lib/tilt/mapping.rb, line 134 def new(file, line=nil, options={}, &block) if template_class = self[file] template_class.new(file, line, options, &block) else fail "No template engine registered for #{File.basename(file)}" end end
Registers a template implementation by file extension. There can only be one template implementation per file extension, and this method will override any existing mapping.
@param template_class @param extensions [Array<String>] List of extensions. @return [void]
@example
mapping.register MyEngine::Template, 'mt' mapping['index.mt'] # => MyEngine::Template
# File lib/tilt/mapping.rb, line 102 def register(template_class, *extensions) if template_class.respond_to?(:to_str) # Support register(ext, template_class) too extensions, template_class = [template_class], extensions[0] end extensions.each do |ext| @template_map[ext.to_s] = template_class end end
Registrers a lazy template implementation by file extension. You can have multiple lazy template implementations defined on the same file extension, in which case the template implementation defined last will be attempted loaded first.
@param class_name [String] Class name of a template class. @param file [String] Filename where the template class is defined. @param extensions [Array<String>] List of extensions. @return [void]
@example
mapping.register_lazy 'MyEngine::Template', 'my_engine/template', 'mt' defined?(MyEngine::Template) # => false mapping['index.mt'] # => MyEngine::Template defined?(MyEngine::Template) # => true
# File lib/tilt/mapping.rb, line 79 def register_lazy(class_name, file, *extensions) # Internal API if class_name.is_a?(Symbol) Tilt.autoload class_name, file class_name = "Tilt::#{class_name}" end extensions.each do |ext| @lazy_map[ext].unshift([class_name, file]) end end
Checks if a file extension is registered (either eagerly or lazily) in this mapping.
@param ext [String] File extension.
@example
mapping.registered?('erb') # => true mapping.registered?('nope') # => false
# File lib/tilt/mapping.rb, line 121 def registered?(ext) @template_map.has_key?(ext.downcase) or lazy?(ext) end
Looks up a list of template classes based on file name. If the file name has multiple extensions, it will return all template classes matching the extensions from the end.
@example
mapping.templates_for('views/index.haml.erb') # => [Tilt::ERBTemplate, Tilt::HamlTemplate]
@return [Array<template class>]
# File lib/tilt/mapping.rb, line 166 def templates_for(file) templates = [] while true prefix, ext = split(file) break unless ext templates << lookup(ext) file = prefix end templates end
Private Instance Methods
# File lib/tilt/mapping.rb, line 257 def constant_defined?(name) name.split('::').inject(Object) do |scope, n| return false if scope.autoload?(n) # skip autload return false unless scope.const_defined?(n) scope.const_get(n) end end
# File lib/tilt/mapping.rb, line 194 def lazy?(ext) ext = ext.downcase @lazy_map.has_key?(ext) &&<EMAIL_ADDRESS>end
# File lib/tilt/mapping.rb, line 217 def lazy_load(pattern) return unless @lazy_map.has_key?(pattern) choices = @lazy_map[pattern] # Check if a template class is already present choices.each do |class_name, file| template_class = constant_defined?(class_name) if template_class register(template_class, pattern) return template_class end end first_failure = nil # Load in order choices.each do |class_name, file| begin require file if Thread.list.size > 1 warn "WARN: tilt autoloading '#{file}' in a non thread-safe way; " + "explicit require '#{file}' suggested." end # It's safe to eval() here because constant_defined? will # raise NameError on invalid constant names template_class = eval(class_name) rescue LoadError => ex first_failure ||= ex else register(template_class, pattern) return template_class end end raise first_failure if first_failure end
# File lib/tilt/mapping.rb, line 213 def lookup(ext) @template_map[ext] || lazy_load(ext) end
# File lib/tilt/mapping.rb, line 199 def split(file) pattern = file.to_s.downcase full_pattern = pattern.dup until registered?(pattern) return if pattern.empty? pattern = File.basename(pattern) pattern.sub!(/^[^.]*\.?/, '') end prefix_size = full_pattern.size - pattern.size [full_pattern[0,prefix_size-1], pattern] end


AccountsClient |
class AccountsClient |
ContributionsClient |
class ContributionsClient |
MessagesClient |
class MessagesClient |
MultisClient |
class MultisClient |
RedditorsClient |
class RedditorsClient |
SearchClient |
class SearchClient |
SubredditsClient |
class SubredditsClient |
WikisClient |
class WikisClient |


Research Methods for Global Studies
Class 4: Working With Data
John Palmer
From the Theoretical to the Empirical
Constructs
abstract concepts chosen to explain a phenomenon of interest
Examples of Constructs
- Age
- Education
- Academic aptitude
- Prejudice
- Level of Rights
Variables
- Measurable representations of constructs
Examples of Variables
- Age in years
- Highest degree completed
- Test scores
- Prejudice index
- Rights index
Types of variables
- quantitative vs. categorical
- nominal vs. ordinal vs. interval
- discrete vs. continuous
Class Survey Example
- What experience do you have with academic research? (Please select all that apply.)
- What areas of math and statistics do you feel you understand? (Please select all that apply.)
- What programming languages and computer applications are you familiar with? (Select all that apply.)
- Approximately how many interviews have you conducted for research purposes during the course of your studies?
Class Survey Example
- Which operating system do you prefer to use?
- How long does it normally take you to get to UPF from home? (Please give answer in minutes.)
- How bad is the air pollution in your neighborhood?
- Who is your favorite author?
- How many Twitter followers does your favorite author have (if any)?
- How many Tweets has your favorite author sent (if any)?
Class Survey Example
- Is there any material you would especially like to cover in this course that you do not see on the syllabus? If so, please describe it.
Describing Quantitative Variables
(for population data)
$$\textrm{Mean: } \mu = \frac{1}{N}\sum_{i=1}^{N} x_i$$
$$\textrm{Variance: }\sigma^2 = \frac{1}{N}\sum_{i=1}^{N} (x_i-\mu)^2$$
$$\textrm{Standard Deviation: }\sigma = \sqrt{\frac{1}{N}\sum_{i=1}^{N} (x_i-\mu)^2}$$
Traditional Data
- Censuses
- Surveys
- Tests
- Diaries
How do you estimate population mean \(\mu\) based on a random sample?
We need to start by understanding probability and distributions
Frequentist Definition
probability of a particular outcome from a random phenomenon is the proportion of times that outcome would occur in a very long sequence of observations
Bayesian Definition
probability as degree of belief that outcome will occur
Basic Rules
$$P(\textrm{not}~A) = 1 - P(A)$$
$$P(A ~\textrm{or}~B) = P(A) + P(B)$$
$$P(A ~\textrm{and}~B) = P(A) * P(B|A)$$
If A and B are independent, then:
$$P(A ~\textrm{and}~B) = P(A) * P(B)$$
Probability Distributions
describe probabilities of all possible outcomes
Example
What is the probability of rolling each value on a six-sided die?
Example
What is the probability that a randomly-selected person is exactly 1.8 meters tall?
Example
What is the probability that a randomly-selected person is between 1.7 and 1.9 meters tall?
The Normal Distribution
Sampling Distribution
the probability distribution of a particular sampling statistic
Point estimate
$$\hat{\mu} = \bar{y}$$
- In other words, the estimator, \(\hat{\mu}\), of the population mean, \(\mu\), is the sample mean \(\bar{y}\)
Confidence Interval
$$\textrm{CI}_{a\%} = \bar{y} \pm z\frac{s}{\sqrt{n}}$$
- where \(\textrm{CI}_{a\%}\) is the \(a\%\) confidence interval, \(z\) is the number of standard deviations from the mean of a normal distribution corresponding to this interval, \(s\) is the sample standard deviation, and \(n\) is the sample size.
- For example, a 95% confidence interval is calculated as:
$$\bar{y} \pm 1.96\frac{s}{\sqrt{n}}$$
- We are assuming here that sample size, \(n\), is large enough for the Central Limit Theorem to hold or that the population distribution is normal (in which case Central Limit Theorem is not needed).
- A more robust approach for small sample sizes and non-normal populations is to use the t-distribution.
- For the 95% confidence interval, we often use \(z=2\) instead of \(z=1.96\) in order to simplify things.
Step 1
- Central Limit Theorem: For random sampling with a large sample size \(n\), the sampling distribution of the sample mean \(\hat{y}\) is approximately a normal distribution.
Step 2
$$\sigma_{\bar{y}} = \frac{\sigma}{\sqrt{n}}$$
- In other words, the standard error of the sample mean (i.e. the standard deviation of the sampling distribution for the mean), \(\sigma_{\bar{y}}\), is equal to the population standard deviation, \(\sigma\), divided by the square root of the sample size, \(n\).
Step 3
$$\hat{\sigma} = s = \sqrt{\frac{1}{n-1}\sum\left(y-\bar{y}\right)^2}$$
- As an estimator of the population standard deviation, \(\hat{\sigma}\), we use the sample standard deviation, \(s\).
Student's t distribution
Student's t distribution
Student's t distribution
- Bell-shaped and symmetric about a mean of 0
- Standard deviation and shape depend on degrees of freedom (\(df\))
- For inference about a population mean, \(df = n - 1 \)
- As \(df\) increases, t becomes more like normal; almost identical at \(df=30\)