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im sweet sexy sassy , a good friend and listener, im creative ,
love to laugh , love friendly flirty banter back and forth , im
honest and trustworthy and looking for the same , just looking for
one good man of my very own
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im a hairstylist/chef by trade both jobs i love , but needed a
pension so i took a job at the university here , now i do hair on
the side , and make jewelry out of old sea glass that i collect by
combing the local shorelines, love live music , flea markets, hikes
to anywhere, boating swim daily weather permitting , anything to do
with exploring nature, photography.... and so much more
I'm really good at
Go on, brag a little (or a lot). We won't judge.
im artistic , creative, love the ocean exploring, im a great cook ,
shoot a mean game of pool, im a good listener , a loyal friend and
above all a great mom, im a very good cook , everything from
scratch , love to cook create and entertain friends and family ,
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Help your potential matches find common interests.
love to read , live music from southern rock, country , pop
alternative,, like action adventure and comedies as far as movies ,
food im a great cook and very creative in the kitchen
The six things I could never do without
Think outside the box. Sometimes the little things can say a lot.
my friends , my family , my sense of humor, my common sense, my
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StartUpHire is the place to find Product Manager - Mobile Solutions jobs. Review the list of Product Manager - Mobile Solutions career opportunities to the left. These Product Manager - Mobile Solutions jobs represent some of the most rewarding careers available.
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BIA is seeking a Product Manager to direct the expansion of its unique product suite in additional markets. The Product Manager is a future-oriented thinker, focused on creating service-based solutions.
2/9/2015 Boston, MA Save This JobLocation: New York, NYDepartment: Product DevelopmetType: Full TimeMin. Experience: Experienced Summary: The Associate Product Manager is a member of our Pharma Product team and will use market research, user feedback, and emerging business trends to build new products and
12/28/2014 New York, NY Save This JobLocation: Columbia, MDDepartment: Product ManagementType: Full TimeMin. Experience: Mid Level Lotame provides comprehensive data management capabilities helping digital Publishers, Marketers and Advertisers collect, organize and activate their audience data. Lotame?s Product and
2/2/2015 Columbia, MD Save This JobNomis is searching for a kick-ass Associate Product Manager to join our growing Product Team. You will be responsible for defining the strategy and guiding the execution of core features that power the Nomis Pricing Platform. Who We Are & What We Build Our top-notch people,
1/10/2015 San Bruno, CA Save This JobPosition Summary RSi is seeking a Technical Product Manager for its fast growing Digital Targeting and Measurement Services. As a Technical Product Manager you will play a unique role to drive product vision, define roadmaps, and work closely with all stakeholders. You will be
1/21/2015 Mountain View, CA Save This JobProduct Management - Providence, RI - Internship Primary Responsibilities Become familiar with the retailer on-boarding process Work with key stakeholders to understand retailer data and align with Retail Solutions data definitions and data model Manage, prioritize and document
1/21/2015 Providence, RI Save This JobAs a Product Manager focused on Growth, you will be responsible for driving product initiatives that enhance customer acquisition and engagement. You can break down complex problems into steps that drive product development at lightning speed.
1/3/2015 New York, NY Save This JobThe technical product manager will be responsible for defining and for assuring the implementation of the technical strategy of Carbon Black by being exceedingly customer centric, gathering, defining and prioritizing product and customer requirements, sup
1/4/2015 Waltham, MA Save This JobSoftware product manager for the leading endpoint security solution focused on preventing advanced cyber threats. This role will be responsible for definition and delivery of the product and ensuring successful market launch. You would be working in a fas
2/19/2015 Waltham, MA Save This JobThe product management team works closely with our engineers to define and deliver products from conception to launch. As part of the product group you bridge the creative, technical and business worlds as you design services that our customers love.
1/3/2015 New York, NY Save This JobTeam description:Â The Twixt team is a tightly-focused group building an important new product line at AppNexus. Our mission is to automate the process of buying digital media for advertising agencies. We are building a scaled, flexible, and highly usable web application that
1/30/2015 New York, NY Save This JobTracking Code 1801 Job Description Come join the Software Product Management team at Silver Spring and help define the next generation network software for the Smart Grid. We are looking for creative individuals with a passion for building software platforms and working on
12/27/2014 Redwood City, CA Save This JobAlert Logic sits at the nexus of two big trends in IT: the adoption of cloud technologies, and an escalation in security and compliance requirements driven by an increasingly connected world. More than half of the largest managed hosting cloud service providers currently use
1/10/2015 Seattle, WA Save This JobAlert Logic is experiencing explosive growth. The security market is growing twice as fast as the overall IT market and Alert Logic?s entirely unique security-as-a-service approach positions the company as the clear up and coming leader in this $5b market. Our products are easier
1/10/2015 Seattle, WA Save This JobSprout Social provides social media engagement, publishing and analytic tools for leading brands including GrubHub, Spotify, Urban Outfitters and Zipcar.
1/24/2015 Chicago, IL Save This JobTracking Code 1848 Job Description Come join us and change the world?s relationship with energy, forever. Be a part of a passionate, collaborative and innovative culture at Silver Spring. We create technology platforms that empower networks to perform brilliantly. Connecting
12/27/2014 Redwood City, CA Save This JobThe Product Manager is responsible for defining and implementing the solution strategy for the solution area. This extends from increasing the profitability of existing products to developing new products for the company. You will build products from existing ideas, and help to
1/6/2015 King of Prussia, PA Save This JobAre you seeking a position that offers you the opportunity to apply your technical and product management expertise? ENA may have the right position for your unique skills and abilities! ENA, the nation?s leading education-focused provider of data, voice, and video solutions,
1/27/2015 Nashville, TN Save This JobThe Product Manager will take a customer and market perspective to coordinate CradlePoint products and services into holistic solution sets for target markets with a focus on how we extend our products into the M2M/IOT space. Through market research, competitive analysis,
1/27/2015 Boise, ID Save This JobWe like to think we?re at the head of the class when it comes to helping businesses accelerate revenue growth and gain a competitive edge. With solutions to improve conversion and win rates at every stage of the revenue cycle, Lattice is the leader in predictive marketing and
1/23/2015 San Mateo, CA Save This Job

subset: commoncrawl


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Looking for another chapter beyond 'Pride & Prejudice'? Sandy Lerner, writing under the penname 'Ava Farmer', will present just that with her book 'Second Impressions'. Join The Jane Austen Society for a reading and signing this upcoming Thursday, June 21st, at 6:30pm.
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Le garden looks old it has that gloomy feeling when you walk in but I was relived when I entered my bedroom , bed was comfortable , bath room was clean and staff on front desk was fast and friendly had great experience , breakfast was just ok , compared to other hotel it looks little expensive we stayed only... More
- Reservation Options:
- TripAdvisor is proud to partner with Ctrip TA, Expedia, Despegar.com and TripOnline SA so you can book your Le Garden Hotel reservations with confidence. We help millions of travelers each month to find the perfect hotel for both vacation and business trips, always with the best discounts and special offers.
- Also Known As:
- Le Garden Xi An
- Hotel Le Garden
- Le Garden Hotel China/Shaanxi


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Summer/Fall Enrollment will begin on March 23rd! Clear your holds before your Enrollment Access Date (www.ucmo.edu/registrar/dates/enroll.cfm).
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Come celebrate Unity Week 2015 with several collaborative events throughout the week of Feb 23!
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Mark your calendars to attend the first meeting of UCM's Gender Violence Prevention coalition on Tuesday, March 3rd. You can make a difference!
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Thursday, February 26th 7-9PM at Fitters! Appetizers provided! Email email@example.com if you cannot attend to join
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Nick, Dale, and Kurt are back to teach their new bosses a lesson.
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Joined: 16 October 2010
Posts: 23
Dadisa asks Jagiya to come to temple with her and Anandi.First he doesn't agree and then Gehna asks him to go and he agrees.
At the temple they see his friends and Dadisa asks him to go and talk to them.He goes towards them and starts his "friendly talk".They get irritated and starts shouting at him.They ask why is he saying so many lies and Lal singh told them about his new affair with Gauri.They ask how they can forget all the things which Anandi.He shouts at them and asks them to mind their business and not to interfear in his personal life.They badmouth about Gauri and Jagiya indulges in a fight with them.Suddenly Anandi comes and they stop.After he goes they discuss how Jagiya cannot be mended.Phooli overhears the entire conversation.
Jagiya and Gauri start their romantic conversation on laptop.He is on the verge of telling ILU to Gauri when Anandi enters and he shouts at her asking how she can enter without his permission.He further warns her from now onwards she should knock on the door before entering.She feels bad and leaves.Gauri calls him but he doesn't lift the phone.
A song sequence follows and Anandi recollects her moments with Jagiya and his behaviour towards her.
Jagiya is talking with Gauri when he hears a knock on the door.Anandi enters and takes her pillow and leaves the room.When he asks him about it she says that she doesn't want to disturb him anymore.
Gehna sees her and asks where she is going.She says Jagiya has some work and doesn't want to disturb him.
Precap:Phooli tells Jagiya that whatever he is now is because of Anandi also.He can make new friends but should not forget his closer ones.
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Balika Vadhu undertakes a huge step as the new chapter (Ek Naya ...
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This article describes two experiments at Towson University's Cook Library, which took library service outside the library directly to places where students gather.
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Kym Reinstadler, SCN Feature Writer's curator insight,
January 17, 12:38 AM
Make your web pages pretty, but don't steal the art. This article recaps ethical practices for using images you find on the Web. It also lists several Websites (including Open Source sites!) for finding great images.
Anita Vance's curator insight,
July 25, 2014 8:32 AM
A great aid in targeting the best starting point for specific searches!


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70s -Veneto- Mens navy blue, longsleeve, white zippered front acrylic track jacket with red and white piping down both sleeves, The initials KWM embroidered in white on the left chest, rib knit cuffs and waistband and two front side inset pockets. In stock.
42Chest (Mens Smaller Large) 32Sleeve
Very Good Condition
Approx Shipping Weight 1.25 lbs.
Item No. 249614
Questions? View our Help Page or contact Jen at email@example.com


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MSC Opera Cruise Review by Rosalie: Opera Highlight
Member Rating
Compare Prices on MSC Opera Baltic Sea Cruises
Opera Highlight
I had an excellent holiday aboard the MSC Opera. The service was first class. All staff were friendly and attentive. Cleanliness was good.
I greatly enjoyed the food in the restaurant and in the buffet. The pasta and the cakes were especially good. There was always an excellent choice
There were times that the buffet area was extremely crowded but it was always possible to get a table when the restaurant was open. I feel that the wine in the restaurant sold by carafe was very reasonable at 7 Euro (plus 15% service) People have complained in other reviews about having to pay for water but water dispensers were available for use in the buffet areas. Free water was poured in the restaurant. It was only bottled water that was charged for
I especially enjoyed the classical concerts held some afternoons.There was a wonderful violinist and pianist and a fabulous tenor and soprano. The entertainment shows in the evening were high quality too.
Met some More
lovely people on the cruise. Would certainly be happy to cruise with MSC again. Less
Published 06/19/11
Read more MSC Opera cruise reviews >>
Read Cruise Critic's MSC Opera Review >>
Compare Prices on MSC Opera Baltic Sea Cruises
Cabin review: MSC Opera Inside Stateroom Turandot Deck 10226
Good cabin with plenty of cupboards and hanging space. Bed comfortable although I would have preferred to have duvet rather than sheets,blanket and bedspread. My only negative is that the shower area was small.
FIRST and LAST NCL CRUISE
Unexpectedly nice ship!
Great time and Northern Lights...
Service to improve
Stylish Opera missing some act...
MSC Opera upped the ante
Value for money cruise ship wi...
Ship and Crew were outstanding...
Great time but wished we had k...
Sponsored Links


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Hotel Villa Pandora is a family run hotel situated on the magnificent Amalfi Coast. All the rooms have a breathless sea view. They offer all modern comforts such as air conditioning, Jacuzzi shower and bathtub, Tv, safe and minibar.Thanks to our position, it's easy to move around and reach all local most important places such as Positano, Praiano, Amalfi and Ravello. Our hotel is also ideal for a relaxing vacation.
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whySubmitted by SurfCityTom on Sat, 09/12/2009 - 4:07pm.
is this even an issue? Ban cell phones from the school grounds. What are cell phones doing with students on school grounds during the school day?
Are they present for networking or education?
Gracious, school resource officers -- a nice term for deputies on school grounds to maintain order.
Cell phones on school grounds.
Focus on education. Ban cell phones and require students to follow school protocol.
Reply |


text:
New Children's Nonfiction Books
New arrivals added to our Children's Nonfiction Collection in the last 7 days
Title | Date added | |
Inspiring stories of sportsmanship by Herzog, Brad. |
" Teaches kids about positive character and sportsmanship through real-life examples." |
Feb 23, 2015 |
What a wonderful world by Thiele, Bob. |
Illustrations and lyrics introduce a song, first recorded in 1967 by Louis Armstrong, which celebrates the wonders of nature, loving friends, and the promise of the future in a baby's cry. |
Feb 23, 2015 |
Thirteen-lined ground squirrels by Clark Sawyer, J. author. |
"In this book, young readers will learn about thirteen-line ground squirrels and their winter habits."-- |
Feb 23, 2015 |
Start sweating! - a kids' guide to being active by Kreisman, Rachelle, author. |
Discusses the health benefits of exercising and suggests various forms of physical activity, including jumping rope, playing a sport, and riding a bike. |
Feb 23, 2015 |
Monster goose nursery rhymes by Herz, Henry. |
Feb 23, 2015 |
Kangaroo to the Rescue! - And More True Stories of Amazing Animal Heroes by Donohue, Moira Rose |
Feb 23, 2015 |
The Founding Fathers! - those horse-ridin', fiddle-playin', book-readin', gun-totin' gentlemen who started America by Winter, Jonah, 1962- |
An introduction to the Founding Fathers describes the heated exchanges that shaped the drafting of the U.S. Constitution, sharing fact-filled profiles of such historic icons as George Washington, Thomas Jefferson, and Benjamin Franklin. |
Feb 23, 2015 |
Growing new plants by Colby, Jennifer, 1971- |
Discusses what a plant needs to grow and how they reproduce. |
Feb 23, 2015 |
Gods and goddesses of ancient Greece by Smith-Llera, Danielle, 1971- |
Introduces the gods and goddesses of ancient Greece, providing profiles on Zeus, Artemis, and Hera. |
Feb 23, 2015 |
Guess who's in the trees by De la BeÌdoyeÌre, Camilla, author. |
Describes the unique characteristics of eight animals that find shelter and food in trees and encourages the reader to guess which animal is hiding under the flap. |
Feb 23, 2015 |
Firefly encyclopedia of animals by de la Bedoyere, Camilla (EDT)/ Whitfield, Philip, Dr. (EDT) |
Presents information about animals ranging from vampire bats and tarsiers to oarfish and bumblebees. |
Feb 23, 2015 |
Cougar by Tomljanovic, Tatiana. |
Introduces cougars, describing their physical features, behavior, life cycle, habitat, and their status as an endangered species. |
Feb 23, 2015 |
Calico cats by Dash, Meredith, author. |
Describes the unique physical characteristics of calico cats, and relates interesting facts associated with these cats, including how they got their name and the most common gender. |
Feb 23, 2015 |
Anglerfish by Murphy, Julie, 1965- author. |
"Discover facts about anglerfish, including physical features, habitat, life cycle, food, and threats to these ocean creatures. Photos, captions, and keywords supplement the narrative of this informational text"-- |
Feb 23, 2015 |
Air Force One by Mattern, Joanne, 1963- |
Introduces the presidential airplane known as Air Force One, recounting the history of presidential aircraft and describing its physical features and the reasons why it is a symbol of the United States. |
Feb 23, 2015 |
Lions by Marsh, Laura, author. |
An itroduction to lions covers where they live, what they eat, and how they communicate, and follows the animal's development from cub to adult.5050 Whar am I? -- Big cats -- Lion around --Super hunters -- Food fight -- 7 cool facts about lions --Cubs -- Lion talk -- What in the world? |
Feb 25, 2015 |
I am Jackie Robinson by Meltzer, Brad. |
"Jackie Robinson always loved sports, especially baseball. He could run, leap, and throw better than any other kid around. But he lived at a time when the rules weren't fair to African Americans: Even though Jackie was a great athlete, he wasn't allowed on the best teams just because of the color of his skin. Jackie knew that sports were best when everyone, of every color, played together. He became the first black baseball player on a major-league team, and his bravery helped lead the way to eq |
Feb 25, 2015 |
Planet Earth - continents, oceans, climate, geology - with 19 easy-to-do experiments and 250 exciting pictures by Farndon, John, author. |
Feb 25, 2015 |
Welcome to the family by Hoffman, Mary, 1945- author. |
Introduces different types of households and discusses families with children, adoption, foster parents, same-sex parents, and fertility treatments. |
Feb 25, 2015 |
Rocks & minerals by Symes, R. F. |
Examines the creation, importance, erosion, mining, and uses of rocks and minerals. |
Feb 25, 2015 |
New Arrivals
Movies & Music
Audiobooks and eBooks
Adult Books
- New Adult Fiction Books
- New Adult Nonfiction Books
- New Large Print Books
- New Biographies
- New Cook Books
- New Ellettsville Inspirational Fiction Books
- New Graphic Novels
- New Indiana Room Materials
- New Job Search Materials
- New Mystery & Detective Books
- New Science Books
- New Science Fiction & Fantasy Books
- New Travel Books
- New VITAL Materials
Young Adult Books
- Foreign Language Materials
New Children's Materials
Movies & Music
Children's Books


text:
LitwareHR is our sample application demonstrating how to use Microsoft technologies to build a SaaS solution. Originally the plan was to have two clients - a web client and a smart client, both communicating with the server hosted services. However the original release of Litware included only the Web client.
I am pleased to see the Litware team have been busy and have now released a smart client for LitwareHR using.... WPF and the approaches outlined in the Composite UI Application Block and the Smart Client Software Factory.
The smart client is available for download from Codeplex and Eugenio has a very nice series of blog posts in July covering the architecure of the smart client.
The following features are demonstrated by the new client:


text:
What it does:
What it does not do:
After ages of searching for some code to do this, I came up with nothing. The closest I had was CGZip by Jonathan de Halleux. (CGZip, a C++ wrapper for gzip methods) The problem was that this only did one file at a time. I wanted to write an installer program to do loads. It can't be too hard to write some code to lump them all together I thought, and I was right.
Ok, now embarking on rArchiving for dummies. First get all the files you need. Go to (CGZip, a C++ wrapper for gzip methods) and get the demo project. Now go to the top of this article and get rArchive.zip. On my computer I have a folder where I keep all my programs. I have a subfolder in it called _common. This is where I put all the common stuff. Unzip CGZip into a temp folder then move the zlib folder into whatever is your equivalent of _common. You do not need any more files from CGZip. Next unzip rArchive.zip into a tmp folder. Move rArchive.h and rArchive.cpp into the _common folder. Now your _common folder should have at least these entries:
Now move the zip_test directory from the rArchive zip into the folder where you keep your Visual Studio projects. (It should now be along side the _common folder) Now go into the zip_test folder and load zip_test.dsw. It will probably lose the zlib project. Now browse to common \zlib\zlib.dsp. Once you have re-setup the link to the project, select the ziptest project and compile. You might get the following two errors: fatal error C1083: Cannot open source file: 'D:\My Documents\Programming Projects\Visual Studio\_common\rArchive.cpp': No such file or directory You might also get lots of link errors. To fix the above error go to file view, and in the zip_test project, remove the entries rArchive.h and rArchive.cpp, then right click on zip_test files and then use Add files to project option, browse to the _common folder, where you put the rArchive files and add rArchive.h and rArchive.cpp to the project. D:\My Documents\Programming Projects\Visual Studio\zip_test\zip_testDlg.cpp(9) : fatal error C1083: Cannot open include file: '../_common/rArchive.h': No such file or directory To solve this one, double click on it and alter the line to point to the correct file.
#include "../_common/rArchive.h"
Now the program should compile. It doesn't have much checking, so make sure values in the text boxes are ok before using it. You can test each of the three main features of the archive. Note: It will always overwrite files, so be careful.
Once you have done the last bit, you can now use the code. I'll go through the process of creating an app similar to the demo one. Create an MFC dialog based app in the usual way. Add 3 controls onto it, named compress, extract and extract from resource. Now select Project -> Insert into workspace. Add the zlib project (_common\zlib\zlib.dsp). Click on Project -> Dependencies, select the new project in the top box and check the checkbox below. (It should say zlib) Go to Project -> Set Active Project and select your new dialog app. Now go to File view and add _common\rArchive.h and rArchive.cpp to the project. Go back to the dialog and double click on the compress button. Create it's handler and don't put anything in it. Instead scroll to the top of the file and near the top, add the include line below:
static char THIS_FILE[] = __FILE__; #endif #include "../_common/rArchive.h" //ADD THIS LINE //////////////////////////////////////////////////// // CZip_test_twoDlg dialog
Now, go back to your compress handler. I am not showing the code to scan a directory for files here, but if you want it, nick it from the zip_test app. Instead, here's a handler that will create an archive, then add some common files to it:
void CZip_test_twoDlg::OnComp() { // TODO: Add your control notification handler code here rArchive arc; if (arc.Open(_T("c:\\windows\\desktop\\new_arch.r"),true)==FALSE) FatalAppExit(0,_T("couldn't create archive"));; arc.SetRoot(_T("c:\\")); if (arc.AddToArchive(_T("autoexec.bat"))==FALSE) FatalAppExit(0,_T("Couldn't add file")); if (arc.AddToArchive(_T("config.sys"))==FALSE) FatalAppExit(0,_T("Couldn't add file")); arc.Close(); MessageBox(_T("Done")); }
Some notes on the code above: The second parameter of Open tells it whether or not to create a new blank archive. You can do two things, to create an archive or have an existing one written over, set it to true. If you are reading from an archive, or simply want to append some files to the archive, set it to false. You must always call SetRoot. You need a relative location to extract the files to. Also it's nice to have a message box at the end, as otherwise I always have to wait 5 minutes and then realize it's already done! (Needless to say, this won't work if you don't have autoexec.bat or config.sys, if not change to some files you have got!) Next is the code for the Extract button:
void CZip_test_twoDlg::OnExtract() { // TODO: Add your control notification handler code here rArchive arc; if (arc.Open(_T("c:\\windows\\desktop\\new_arch.r"),false)==FALSE) FatalAppExit(0,_T("couldn't create archive"));; arc.SetRoot(_T("c:\\windows\\desktop\\test_dir")); while (arc.WriteNextFile() == TRUE) { //Good place to put code to incrment progress bar! }; arc.Close; MessageBox(_T("Done")); }
Note the parameter is now false in the Open statement. Otherwise the archive would be deleted before we got anything out of it. Now the fun bit. Use either your or my program to create an archive of what ever you want (Mine will search folders automatically and save you work). Now, lets say the file you use is new_arch.r. Click Insert -> Resource, then click the Import button. Select the new_arch.r file you just created. Change the filter to *.* to see it. Now click Import. Enter RFILE as the resource type, then click ok. Note down the ID the resource has been given. (IDR_RFILE1 in my case) Now add the handler for the "Extract from" resource button.
void CZip_test_twoDlg::OnExtres() { // TODO: Add your control notification handler code here rArchive arc; if (arc.Open(IDR_RFILE1)==FALSE) FatalAppExit(0,_T("couldn't create archive")); arc.SetRoot(_T("c:\\windows\\desktop\\test_dir_from_res")); while (arc.WriteNextFile() == TRUE) { //Good place to put code to increment progress bar! }; arc.Close; MessageBox(_T("Done")); }
Now it should work, ain't that good? Note, the only difference is we have used the second version of the Open member function, passing it only one parameter, that is the resource id. I won't explain how I did it, but if you look at rArchive.h and rArchive.cpp you should see.
Ok, now that I have shown you how to use the code, I am going to explain it's limitations. It's a best fix and there are some things I don't know how to do, maybe someone can help.
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The First game was played February 20, 1892, in Atlanta's Piedmont Park. It was the brainchild of Auburn's George Petrie and Charles Herty of Georgia. Piedmont Park hosted several Auburn-Georgia games, and it all started in 1892 when Auburn beat Georgia 10-0. According to legend, that first game is also the origin of Auburn's "War Eagle!" cheer.
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Meeting New Challenges at the Aldine Independent School District (A)
Stacey Childress, Allen Grossman, Caroline King
Revision Date:Jun 13, 2011Publication Date:Oct 14, 2005Discipline:Organizational BehaviorSource:Public Education Leadership Project |
Product number:PEL030-PDF-ENG Length:25p |
English PDF
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This is a PELP case study.
Examines the Aldine Independent School District's (AISD) efforts in 2005 to sustain and accelerate significant increases in student performance achieved during the preceding decade. Highlights the school district's managerial challenges posed by higher state standards, performance gaps within AISD, and the district's historically decentralized approach toward the implementation of reforms at the school level. Looks at organizational alignment or coherence, leadership, and managing performance gaps across various field units.
subjects covered:
Leadership; Performance management; Strategic alignment; Strategy
- Industry: Public schools
- Event Year Begin: 2005
- Event Year End: 2005


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Center for Enrichment & Life Long Learning
The Center for Enrichment is a place where older adults (age 55 years and better) can gather to stay active, healthy and continue to learn. The center provides opportunities for education, wellness and spiritual growth while encouraging social interaction.
We invite you to take a brief look at how lives are enriched through Life Long Learning at Clement Manor. Click here to view video.
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Keynote: Journey of Love and Faith: The Leslie Lemke Story - March 26 from 1:30 to 3pm. Author Shirlee Monty shares the true story of a Pewaukee, WI family taking in a blind, cerebral-palsied and severely brain-damaged boy named Leslie. Leslie became one of less than 100 prodigious savants in the world and of that group, one of the most musically gifted. Members $3 / Guests $5.
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Clement Manor Center for Enrichment and Life Long Learning Receives the Wisconsin Health Care Association 2008 Community Harmony Award
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all categories Compressors, Gate... Effects and Signa... all manufacturers API Audio ART Avalon Behringer Chandler Limited Crane Song DBX Drawmer Elysia Empirical Labs FMR Audio Fredenstein JoeMeek Manley SPL SSL TC Electronic Tegeler Audio Man... Tube-Tech the t.racks
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Behringer XR4400 Multigate Pro - 4-channel expander/gate with link-function (e.g. stereo gate), hold and release controls, IRC circuitry, parametric side-chain filter, gain reduction/threshold VU meters, balanced XLR/jack I/O, 21.7cm deep. Weight: 3.5kg
I purchased the Behringer XR4400 Multigate Pro after having used an automatic microphone gate at a large convention a number of years back. After checking the prices of the automatic gates which I thought were too expensive for my personal kit I looked for a cost effective alternative, the Behringer XR4400 looked as if it would fit the bill although it would need to be set up manually.Why did I want a multigate:I had purchased a set of Behringer XM8500 Microphones (see separate review) and was trying to overcome the handling noise when used for speech, one possibility was trying to gate the microphones so that the continual low frequency rustling-scraping noise could be eliminated or at least reduced.Did it work:Unfortunately not for the handling noise but it is still a great piece of equipment. What did it do:I found a great use for the XR4400 in helping with feedback by reducing the number of mics that are open at any one time without having to keep fiddling with mixer sliders. In a multi participant situation it comes into its own.Ease of use:The XR440 takes a bit of getting used and setting up is a bit of trial and error. There are not a lot of tutorials out there for setting up gates. But once set the features of daisy chaining etc. can really come into its own.Would I recommend it to others:Yes I would. It is another great Behringer piece of kit but you need to know that you have a specific job for it. Unless like me you enjoy fiddling with anything to do with sound. P.S. Did I eliminate the microphone handling noise, unfortunately no. I also tried an EQ to find the offending frequency still no good so in the end still being cost conscious I purchased a set of t.bone EM 800 mics as I had one in my kit that was pretty well noise free (see review)
These were bought for mic-ing up a drum set in a live environment. The functionality for the money spent is superb. The build quality reflects the price but is good enough for the level of use we give it and it lives in a flight case for solid protection. I always check that all of the switches and functions work with Behringer gear on arrival - but this one was fine. Great control of the gating function and easy to configure.
This is a good device. I dont know why someone has strong negative thoughts of this brand. I was purchased some Behringer equipment and so far been very pleased with the quality. Specially considering the very low price. This gate modules are in my oppinion good and we use them in backing vocal mics. (I dont want those mics to bleed all noise to the mix as my backing vocalists sing only some short parts in the songs) We also use another module for the drums (Tom mics, so that they dont bleed to the mix then the toms are not played) on the drums this gives me also the possibility to add reverb and create that gated reverb sound that we want for our 80's metal cover band. -Sami-
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"The harder you work, the luckier you get." by Gary Player
Below is the poem entitled Sweet Believer which was written by poet Anna J Carroll. Please feel free to comment on this poem. However, please remember, PoetrySoup is a place of encouragement and growth.
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Sweet Honest Loving & caring Intelligent Modest Intuitive & daring Generous Outgoing Will help you in need Shy Quiet But I will succeed Talented Anxious Fallen dreamer Creative Determined I am a believer


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Carol Priest Is Recognized By Continental Who's Who As A Pinnacle Professional In The Field Of Finance.
SWEDESBORO, N.J., Sept. 12, 2013 /PRNewswire/ -- Carol Priest is recognized by Continental Who's Who as a Pinnacle Professional in the field of Finance as a result of her role as Vice President of Franklin Bank.
Established in 1861, Franklin Bank has been serving the banking needs of its community for over 150 years. As of 2011, Franklin Bank has been recognized as a 4-Star Excellent Bank by Bauer Financial, Inc., the nation's most elite bank rating firm. In order to attain a 4-Star rating, the bank is required to file a detailed financial report with federal regulators four times a year. Bauer Financial then subjects this information to a thorough analysis and compares it with historical data for consistency. Upon completion of the analysis, a star-rating is assigned. Bauer Financial's 4-Star rating is one of the most preeminent ratings a bank can receive, confirming Franklin Bank's place in the top-tier of the banking industry.
As Vice President of Franklin Bank, Carol brings over thirty-one years of experience to her current position. Working in the banking industry since 1970 and with Franklin Bank for more than three decades, Carol has amassed exceptional knowledge in her field. In this role, Carol is responsible for Finance; Deposit Operations; Individual Retirement; Checking; Savings; Electronic Transfer Cards; as well as Customer Service.
In addition, Carol expertly works with all areas of the banking organization as a computer liaison with primary and secondary vendors. In this capacity, Carol ensures that the bank is able to supply the most effective and efficient services to its valued customers. Her most recent accomplishment in the field of Banking was in a leadership position where she was asked to lead a talented and dedicated team successfully through a full core computer conversion.
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At CES 2015, Sony shows its video expertise in the form of two new Action Cams ready to do everything from capture high-bit-rate video to live-streaming to blazing-fast burst shooting.
Pricing not available
This action camera is a cheap and cheerful alternative to the more expensive GoPro models, ideal for those who want an all-in-one package with a glut of mounting options.
Typical price: $159.00
A leaner, lighter version of its top POV camera, the Sony Action Cam Mini HDR-AZ1 puts big performance in a little package.
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Forget placing your phone perfectly on a pad. We demo wireless charging that frees you from cords -- and constraints.
This week, it's politics in action as Shorten and Abbott exchange shots over proposed data retention laws, all the fun and games from the Dallas Buyers Club court case and which mobile telcos are short-changing you on 1800 calls.
The iconic fantasy video game franchise could be turned into a live-action series on the streaming video service, with help from game maker Nintendo, according to The Wall Street Journal.
Netflix and Marvel have just shown off a full-length teaser trailer for their upcoming series "Daredevil," and it looks to be dark, smart and full of action.
With this rerelease, three of the five classic PC games we wished for back in 2009 are now available.
This browser plug-in can add the bells and whistles that YouTube is missing.
Play as your favorite characters from "The Lego Movie" in a game that has you trying to help save their world.


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More Fantasy
The Latest
Player Updates
Russell Westbrook (OKC-PG)
Russell Westbrook said he is good to go Friday
Westbrook had 39 points, 14 rebounds, 11 assists and three steals for his third triple-double in February. He struggled from the field, going 12-of-38 and 1-of-10 from beyond the arc, and appeared to favor his ankle briefly at the end of the game. There is no reason to doubt him when he says he will be fine, but this situation is worth keeping an eye on. -- Fantasy Alarm
Steven Jackson (Atl-RB)
Steven Jackson does not intend to retire follow
Jackson is 31 years old and averaged 3.7 yards per carry last season, so he may have a difficult time finding a time interested in his services. The Falcons offensive line was pretty dreadful in both of Jackson's seasons there, so it is possible he could be a productive player on a better team. -- Fantasy Alarm
Max Scherzer (Was-SP)
Scherzer is adding cutter to his existing four-pitch repertoire
Pitchers will often toy with different pitches during spring training to see what does and does not work. An effective cutter has proven to a dominating pitch and if Scherzer who is already one of the games best is capable of throwing it with effectiveness he should be in for another incredible season. -- Fantasy Alarm
Timofey Mozgov (Cle-C)
Mozgov recorded a double-double in Thursday win
Mozgov's double-double on Thursday night was exactly what the doctor ordered. He controlled the paint by holding his counterpart Andrew Bogut to a mere six points and five rebounds and he also set a pretty good tone by recording two blocks. It should be noted that he had flashbacks of Blake Griffin and 2010 on Thursday night, as Draymond Green put him on a poster late in the third quarter. -- Fantasy Alarm


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A book for intermediate Python programmers is available at http://inventwithpython.org/pygame. It's free under a Creative Commons license, and aimed at beginners who know a little programming (but doesn't assume you know Python). It has the source code for 11 games (clones like Tetris, Nibbles, Simon, Connect Four, etc.) and explains how the source code for these games work.
You don't need to have read "Invent with Python" first to read this book. This book just assumes you aren't a complete beginner programmer.
(Pygame is an Python wrapper for SDL, and can be downloaded for free from pygame.org)
Free book: "Making Games with Python & Pygame" (sequel to "Invent Your Own Computer Games with Python")
Started by AlSweigart, Feb 11 2012 02:48 PM
2 replies to this topic


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3.1 Introduction
3.2 Tables and graphs
3.3 Bar and pie charts
3.4 Classification by variable
3.5 Quantification of disease events in populations
3.6 Methods of summarising numerical data
Evidence of the presence, nature and severity of a disease will usually be contained in statistical data of some kind. These may take the form of counts of the numbers of diseased animals, physical measurements of a sample of animals, the measurement of one or more biological variables that are likely to be affected by the presence of the disease, and so on. Any report on the disease will have to include at least a descriptive presentation of the statistical evidence.
There are several basic methods and measures which are commonly used to display and summarise sets of data. The choice of technique used depends mainly on the kind of data involved. Data come in two main categories - categorical (discrete) and continuous (numerical) data. Categorical data are data that can be allocated to distinct categories, and normally take the form of counts. Categorical data found in epidemiology may take the form of dichotomous data i.e. data that can have only two values (e.g. diseased or non-diseased, infected or non-infected). Continuous data consist primarily of measurements, which, although they can be classified into defined categories, have the theoretical possibility of being infinitely subdividable. For example, the weight of a chicken could be 1.45 kg, 1.453 kg, 1.45327856 kg etc.
In this chapter we will be looking at some of the more common and useful methods for summarising both categorical and continuous data.
Table 1 consists of the liveweights of 150 chickens selected randomly in a large market during a day on which approximately 4000 chickens were sold.
Table 1. Weights (kg) of a sample of 150 chickens sold in a market.
1.40 |
1.09 |
1.74 |
1.48 |
1.82 |
1.09 |
1.52 |
1.41 |
1.83 |
1.22 |
1.34 |
1.68 |
1.25 |
1.65 |
1.14 |
1.33 |
1.06 |
1.71 |
1.17 |
1.51 |
1.36 |
1.34 |
1.03 |
1.24 |
1.06 |
1.12 |
1.15 |
1.57 |
1.38 |
1.40 |
1.39 |
1.31 |
1.50 |
1.10 |
1.45 |
1.34 |
1.38 |
1.35 |
1.49 |
1.58 |
1.25 |
1.42 |
1.64 |
1.57 |
1.53 |
1.18 |
1.39 |
1.34 |
1.13 |
1.23 |
1.17 |
1.88 |
1.30 |
1.27 |
1.01 |
1.63 |
1.47 |
1.23 |
1.48 |
1.48 |
1.37 |
1.42 |
1.22 |
1.47 |
1.31 |
1.05 |
1.61 |
1.41 |
1.17 |
1.45 |
1.43 |
1.22 |
1.40 |
1.14 |
1.53 |
1.25 |
1.02 |
1.30 |
1.35 |
1.37 |
1.69 |
1.37 |
1.11 |
1.30 |
1.05 |
1.19 |
1.36 |
1.63 |
1.44 |
1.29 |
1.35 |
1.59 |
1.94 |
1.51 |
1.78 |
1.37 |
1.11 |
1.38 |
1.53 |
1.44 |
1.47 |
1.39 |
1.55 |
1.76 |
1.43 |
1.37 |
1.67 |
1.36 |
1.31 |
1.41 |
1.36 |
1.26 |
1.17 |
1.15 |
1.79 |
1.46 |
1.35 |
1.29 |
1.50 |
1.26 |
1.36 |
1.41 |
1.36 |
1.32 |
1.08 |
1.28 |
1.33 |
1.29 |
1.42 |
1.50 |
1.32 |
1.39 |
1.20 |
1.68 |
1.20 |
1.35 |
1.56 |
1.57 |
1.37 |
1.27 |
1.25 |
1.38 |
1.56 |
1.60 |
1.74 |
1.40 |
1.11 |
1.60 |
1.21 |
1.44 |
It is not easy to make sense of these figures displayed in this form. What can we do to make them more intelligible? Perhaps the first thing which will occur to most of us is to calculate the mean (i.e. sample average) by adding all these values and dividing by 150. Doing this, we find that the mean weight of chickens in the sample is 1.3824 kg. How useful is this number? By itself, not very useful. For example, it does not allow us to draw the conclusion that "most of the chickens weighed about 1.38 kg".
Adding the information that the lightest chicken weighed 1.01 kg and the heaviest 1.94 kg, we might say that the range of the sample was 0.93 kg (1.94 - 1.01), with a mean weight of 1.3824 kg. However, this does not rule out the possibility that the weights were evenly spread throughout the range, or indeed that about half were at the low end and the remainder at the upper end of the range. In other words, we would like to know precisely how the values were distributed throughout the range. The simplest way to do this is to draw up a frequency table (see Table 2).
Table 2. Frequency table of the individual weights of 150 chickens.
Grouped interval of chicken weights (kg) |
Frequencya |
Relative frequency (%) |
Cumulative frequencyb |
Relative cumulative frequency (%) |
1.00-1.09 |
10 |
(6.7) |
10 |
(6.7) |
1.10- 1.19 |
16 |
(10.7) |
26 |
(17.3) |
1.20- 1.29 |
21 |
(14 0) |
47 |
(31.3) |
1.30- 1.39 |
39 |
(26.0) |
86 |
(57.3) |
1.40-1.49 |
26 |
(17.3) |
112 |
(74 7) |
1.50- 1.59 |
17 |
(11.3) |
129 |
(86.0) |
1.60 1.69 |
11 |
(7.3) |
140 |
(93.3) |
1.70-1.79 |
6 |
(4 0) |
146 |
(97 3) |
1.80-1.89 |
3 |
(2.0) |
149 |
(99 3) |
1.90-1.99 |
1 |
(0 7) |
150 |
(100.0) |
a Number of values in each interval.
b Cumulative number of values up to the end of a particular interval.
The relative frequencies (column 3) were obtained by dividing the number of values in each interval by the total number of chickens in the sample and converting the result to a percentage. For example, the relative frequency of the first interval is:
(10/150) x 100 = 6.7%
Looking down the column of relative frequencies we see that 17.3% of the sampled chickens weighed between 1.40 and 1.49 kg, and over half (57.3%) weighed between 1.20 and 1.49 kg. The cumulative and relative cumulative frequencies also given in the table are useful in answering questions about the extremes or tails of the distribution. For example, 17.3% of chickens in the sample weighed less than 1.20 kg and 14% (100 - 86) weighed at least 1.60 kg.
The information in Table 2 can also be presented as a graph (Figure 2). Frequency tables are often presented as special types of graphs called histograms.
Figure 2. Histogram of the frequency distribution of chicken weights from Table 1.
The area of each block in the histogram should be proportional to the relative frequency of the corresponding interval. Only when the class intervals are all of equal size, as in this case, will the height of each block be proportional to the frequency.
Measured to the nearest hundredth of a kilogram, the chicken weights ranged from 1.01 to 1.94 kg i.e. there were 94 possible values in the range. If we had measured the weights to the nearest gram, there would have been 940 possible values in the range. In order to draw up a frequency table like Table 2, it is necessary to collapse the data into classes defined by intervals on the scale of measurement. Sometimes data can take only a limited range of values, and then it may be neither necessary nor desirable to group different values into the same classes. An example is Table 3 which gives the frequency of different parturitions in a herd of 153 cows.
Table 3. Frequency of different parturitions in a herd of 153 cows.
Parturition number |
0 |
1 |
2 |
3 |
4 |
Number of cows |
26 |
38 |
47 |
24 |
18 |
Relative frequency (%) |
0.17 |
0.25 |
0.31 |
0.16 |
0.12 |
Cumulative relative frequency(%) |
0.17 |
0.42 |
0.73 |
0.88 |
1.00 |
It does not make sense to try to draw a histogram of this data set. Other possible methods of graphical presentation will be suggested below, though, in this case, the table is by itself a clear method of presenting the data.
We could use the data to calculate the mean number of parturitions -
[(26x0) + (38x1) + (47x2) + (24x3) + (18x4)]/153= 1.80
- but this is unlikely to be a useful piece of information unless we wanted to compare two different herds. Even then, it would be better to give the complete sets of parturition data for both herds.
Categorical data that take only two possible values are often referred to as dichotomous, and we will be interested mainly in the proportions belonging to each category. Note that the use of numerical labels for categorical variables may sometimes be confusing, but it does not deprive the latter of their categorical status. The important question is whether the numerical labels still behave as numbers in the usual sense.
This may be demonstrated on the following example. Three common causes of death in chickens are salmonellosis, coccidiosis and Newcastle disease, and their frequencies in a sample of 59 dead birds are shown in Table 4. For convenience of data storage, the variables were given code numbers 1, 2, 3 and 4, as shown in the table. However, these are not numbers in the usual sense. For example, we cannot say that 2 (coccidiosis) is greater than 1 (salmonellosis), and so on. They are just simpler versions of the original labels. It would therefore be silly to try to work out the mean of these coded data; the most we can do is to give tables of frequencies or percentages.
Table 4. Frequencies of causes of death in a sample of 59 chickens.
Cause |
Code |
No. of deaths |
Relative frequency (%) |
Salmonellosis |
(1) |
12 |
0.20 |
Coccidiosis |
(2) |
7 |
0.12 |
Newcastle disease |
(3) |
30 |
0.51 |
Other |
(4) |
10 |
0.17 |
As was pointed out a histogram would not be a suitable means of presenting the data in Table 3, and this applies also for Table 4. The data in these tables can be presented graphically either in a bar chart or a pie chart. Figure 3 is a bar chart showing the relative frequencies of the different parturition values given in Table 3.
Notice the differences between a bar chart and a histogram: there should be a gap between adjacent bars in the bar chart to emphasise that the data can take only the discrete values actually marked on the horizontal axis, and each bar should have exactly the same width, with the height proportional to the relative frequency of the value over which it is centred.
Figure. 3 Bar chart of parturition data from Table 3.
The data on chicken pathology (Table 4) can also be displayed in a bar chart (Figure 4). However, unlike in Figure 3 where the different parity values have the usual, natural ordering, in Figure 4 the order of the different "values", i.e. diseases, on the horizontal axis is arbitrary. Remember, when there is a natural order, it must be adhered to; when the data are categorical, any ordering may be chosen.
Figure 4. Bar chart of data on causes of death in chickens from Table 4
Frequently, it may be helpful to present categorical data in a decreasing order of frequency, as was done in Figure 5.
Figure 5. Alternative bar chart of data from Table 4.
For purely categorical data, the pie chart is a common alternative to the bar chart. The pie chart is a circle divided into as many sectors as there are categories. The area of each sector is made proportional to the relative frequency of the corresponding category by calculating the angle which the sector makes at the centre of the circle. As the total of all the angles is 360°, we need only to divide the 360° in the correct proportions among the various categories to obtain the corresponding areas.
From Table 4 we know, for example, that the relative frequency of salmonellosis is 0.20. The corresponding angle is 360 x 0.20 = 72°. Similarly, the angles corresponding to coccidiosis and Newcastle disease are 43° and 184°, respectively, rounded to the nearest degree. The resulting pie chart is shown in Figure 6.
Note that in histograms, pie charts and bar charts the sample size should always be quoted.
All the examples discussed so far have involved observations of a single variable in a single population of animals. However, we may wish to subdivide a population into several subgroups in order to investigate possible differences between them. For example, cattle may be classified by sex, breed, geographic location, disease status etc. In epidemiological investigations, the classificatory variables will usually be categorical and will frequently be referred to as factors or determinants.
True numerical variables can also be used as classifying factors, either in the form of the values of the variable, if it takes only a small number of values, or class intervals.
Figure 6. Pie chart of relative frequencies of causes of death in 59 chickens based on Table 4.
For example, each animal that provided data for Table 3 could be classified by its number of parturitions, thus dividing the sample into five groups, while the chickens whose weights are given in Table 1 could be divided into 10 distinct weight groups, using the class intervals of Table 2 to define the different levels of the factor "liveweight".
The choice of factors and the number of levels of each factor will depend on the degree of prior knowledge of the population to be studied, the expected scientific significance of the factors, and the measures available to the investigator. Table 5 is a contrived table displaying counts of ascaris infections in pigs according to three factors: the management system (two levels; raised indoors or outdoors), the occurrence of ascaris eggs in a sample of faeces from each pig (two levels; present or absent), and the degree of whitespot observed in the liver of each pig after slaughter (three levels; absent, slight or severe).
Table 5. Contrived table based on evidence of ascaris infection in pigs: An example of a three-factor table with marginal totals.
Whitespot |
Ascaris eggs |
Management system |
Any system |
Indoors |
Outdoors |
Absent |
Absent |
503* |
112* |
615 |
Present |
141* |
38* |
179 |
Total |
644 |
150 |
794 |
Slight |
Absent |
231* |
75* |
306 |
Present |
87* |
30* |
117 |
Total |
318 |
105 |
423 |
Severe |
Absent |
79* |
32* |
111 |
Present |
71* |
17* |
88 |
Total |
150 |
49 |
199 |
Absent |
813 |
219 |
1032 |
Any whitespot condition |
Present |
299 |
85 |
384 |
Total |
1112 |
304 |
1416 |
* Recorded data.
In any table, it is often useful to give the marginal totals i.e. to sum the counts over all the levels of the different factors. This makes it easier to extract any subtables that may be of interest, and the marginal tables are needed anyway for the analysis of the data (see Chapter 5). On the other hand, marginal totals can greatly increase the size of a table. In Table 5, for instance, only the values marked with an asterisk are strictly necessary, while the remaining entries (24 out of 36) give supplementary information. The use of marginal totals is a matter of personal judgement: in general, if it is thought that the complete table might confuse rather than clarify the issues, then the totals are better left out.
Table 6 shows one of the two-factor tables that can be derived from Table 5.
Table 6. Two-factor table derived from Table 5.
Whitespot |
Ascaris eggs |
Total |
Absent |
Present |
Absent |
615 (59)a |
179 (47) |
794 (56) |
Slight |
306 (30) |
117 (30) |
423 (30) |
Severe |
111 (11) |
88 (23) |
199 (14) |
Total |
1032 (100) |
384 (100) |
1416 (100) |
a Figures in parentheses give the relative frequencies (%) of whitespot conditions.
With multi-factor tables there are always several options for presenting relative frequencies. In Table 6, for example, the relative frequency of the different whitespot conditions is given for each level of the ascaris egg factor. Alternatively, the frequency of each level of ascaris eggs could be given relative to the totals within each level of whitespot severity, or the frequency of each of the six possible whitespot-ascaris egg combinations could be calculated relative to the total number of pigs in the sample. The option chosen will depend on the point that one wants to make, but the table should make it clear which relative frequencies are given. In interpreting tables presented by other investigators care should be taken to clarify which relative frequencies are being presented or discussed.
Data used to quantify disease events in populations are often dichotomous in nature i.e. an animal can either be infected with a disease agent or not infected. Such data are frequently presented in the form of an epidemiological rate.
In epidemiology, a rate can be defined as the number of individuals having or acquiring a particular characteristic (normally an infection, a disease or a characteristic associated with a disease) during a period of observation, divided by the total number of individuals at risk of having or acquiring that characteristic during the observation period. The expression is then multiplied by a factor, normally a multiple of 10, to relate it to a specified unit of population.
Rates are commonly expressed as decimals, percentages, or events per standard units of population e.g. per 1000, 10000 animals etc. This produces a standardised measure of disease occurrence and therefore allows comparisons of disease frequencies over time to be made between or within populations. Note that in a rate, the numerator is always included in the denominator, while in a ratio it is not included. In an epidemiological rate, the period of observation should always be defined.
It is difficult to make valid comparisons of disease events between or within populations unless a denominator can be calculated. The use of "dangling numerators" to make comparisons is one of the biggest "crimes" that the epidemiologist can commit, and it should be avoided whenever possible.
For example, suppose we were interested in comparing the numbers of cases of infection with a particular disease agent over a particular time period in two herds of cattle of the same breed but under different management systems. We are told that in herd A the number of animals infected with the disease agent in question in the month of June 1983 was 25, while in herd B the number of animals infected with the same disease agent in the same month was 50. We might therefore conclude, erroneously, that the disease was a greater problem in herd B than in herd A. Note that we did not know the denominator i.e. the population of animals at risk of being infected with the disease agent in each herd. Suppose we investigated further and found that the population at risk in herd A during the month of June was 100 while in herd Bit was 500. Then, calculating a rate for each herd, we find that the rate of infection in herd A was 25/100 or 0.25 or 25% or 250 in 1000, while in herd B it was 50/500 or 0.10 or 10% or 100 in 1000. The true position, therefore, is that the disease was a greater problem in herd A!
The two main types of rates used in veterinary epidemiology are:
· Morbidity rates, which are used to measure the proportion of affected individuals in a population or the risk of an individual in a population of becoming affected.· Mortality rates, which measure the proportion of animals dying in a population.
Morbidity rates
Morbidity rates include incidence, attack, prevalence and proportional morbidity rates.
Incidence rate is the number of new cases of a disease occurring in a specified population during a specified time period, divided by the average number of individuals in that population during the specified time period.
For example, suppose that out of an average population of 4000 cattle in a quarantine camp, 600 animals developed symptoms of rinderpest during the month of June. The incidence of rinderpest in that quarantine camp for the month of June was 600/4000 = 0.15 or 15% or 150 new cases per 1000 animals.
The incidence rate is a way of measuring the risk that a susceptible individual in a population has of contracting a disease during a specified time period. Therefore, if a susceptible animal had been introduced into the quarantine camp on I June, it would have had a 15% chance of contracting rinderpest by the end of the month.
When calculating incidence rates, problems frequently arise in estimating the denominator. Because of births, deaths, sales, movements etc. livestock populations rarely remain stable over periods of time, and such fluctuations in the denominator will obviously affect the calculation of the incidence rate. There are various ways of estimating the denominator in incidence rate calculations. These normally involve measuring the population at various intervals during the study period and averaging the results.
For instance, suppose that in our previous example there were 4000 animals present at the beginning of June but that 100 animals died of the disease by the end of the second week and a further 300 by the end of the month. Assuming that no new animals were introduced or born, the animal population in the quarantine camp at the start of the observation period was therefore 4000, at the mid-period 3900 and at the end 3600. We might decide to calculate the denominator by taking the populations present at the beginning and end of the observation period and averaging them:
(4000 + 3600)/2 = 3800
The corresponding incidence rate would be 600/3800 = 0.158 or 15.8%.
Alternatively, we might take the populations present at the beginning, middle and end of the observation period and average them -
(4000 + 3900 + 3600)/3 = 3833
- and the incidence rate in this case would be 600/3833 = 0.156 or 15.6%.
Note that the different methods of calculating the denominator have resulted in slightly differing estimates of incidence. Because of this, the method used in calculating the denominator should always be specified when comparisons of incidence are being made, and the same method should be used throughout. Due to difficulties in the calculation of the denominator in incidence rates, another form of morbidity rate, the attack rate, is sometimes used.
The attack rate is the total number of cases of a disease occurring in a specified population during a specified time period, divided by the total number of individuals in that population at the start of the specified time period. The denominator, therefore, remains constant throughout the period of observation. Thus, in our previous example, the attack rate would be 600/4000 = 15%.
Strictly speaking, the definition of the attack rate requires that all cases of disease, not just new cases, are included in the numerator. Attack rates are normally used, however, to quantify the progress of a disease during an outbreak. In most instances there would have been no cases of the disease in question prior to the onset of the outbreak, so that all the cases are, in fact, new cases, and the attack rate becomes a modified form of incidence rate, sometimes referred to as a cumulative incidence rate.
Prevalence rate is the total number of cases of a disease occurring in a specified population at a particular point in time, divided by the total number of individuals in that population present at that point in time.
For example, suppose that in a population of 4000 cattle held at a quarantine camp there were 60 cases of rinderpest when the population was examined on June 18. The prevalence of rinderpest at that camp on 18 June would then be 60/4000 = 0.015 or 1.5% or 15 cases per 1000 animals.
Note that prevalence is a cross-sectional measure referring to the amount of disease present in a population at a particular point in time, hence the term point prevalence. However, when dealing with large populations, point prevalence becomes almost impossible to obtain, since it is not possible to examine all the individuals in that population at a particular point in time. In general, therefore, measurements of prevalence have to take place over a period of time, and this is known as period prevalence. Provided that the time taken to measure the prevalence remains reasonably short, this parameter retains a fair degree of precision. If, however, the time interval becomes too long, a significant number of new cases of the disease will have occurred since the start of the measurement period. The parameter then becomes a mixture of point prevalence and incidence and, as such, loses precision.
The terms incidence and prevalence are frequently confused and misused. Confusion normally arises due to a failure to define accurately the denominator i.e. the actual population being considered. This can result in the population at risk being either ignored or not considered in its entirety.
Examples of this can be found in reports from veterinary offices laboratories, in which the term "incidence" is often used to express the number of diagnoses or isolations of a particular disease agent as a percentage of the total number of diagnoses or isolations performed. In this case the denominator is not the population of individuals at risk from the disease, and the rate calculated resembles a form of a proportional morbidity rate.
A proportional morbidity rate is the number of cases of a specific disease in a specified population during a specified time period, divided by the total number of cases of all diseases in that population during that time period.
For example, suppose that an outbreak of contagious bovine pleuropneumonia (CBPP) occurs in a herd of cattle. During a 6-month period there are 45 cases of different diseases, including 18 cases of contagious bovine pleuropneumonia. The proportional morbidity rate for contagious pleuropneumonia in that herd for the 6 months would then be 18/45 = 0.4 or 40% or 400 cases of CBPP in 1000 cases of all diseases.
Mortality rates
The most commonly used mortality rates are crude death rate and cause-specific death rate.
Crude death rate is the total number of deaths occurring in a specified population during a specified time period, divided by the average number of individuals in that population during the specified time period.
The denominator for this rate can be estimated in the same ways as that for an incidence rate. Note, the method of calculating the denominator should always be defined and the same method used throughout to enable meaningful comparisons to be made.
Example: Suppose that in a herd of cattle there were 40 deaths in a year. The number of animals in the herd at the start of the year was 400, at mid-year 420, and at the end of the year 390. The average herd size could therefore be either
(400 + 390)/2 = 395
(400 + 420 + 390)/3 = 403
Depending on which method we used to calculate the denominator, the crude death rate would be either 40/395 = 0.101 (10.1%) or 40/403 = 0.099 (9.9%).
Cause-specific death rate is a useful mortality rate and can be defined as the total number of deaths occurring from a specified cause in a specified population during a specified time period, divided by the average number of individuals in that population during that time period. The denominator is calculated in the same way as for an incidence or crude death rate, and the same caveats apply in its calculation.
Example: Suppose that there were 20 deaths from babesiosis in the herd mentioned above, then the death rate due to babesiosis in that herd would be either 20/395- = 0.051 (5.1 %) or 20/403 = 0.050 (5.0 %).
Other useful mortality rates
Proportional mortality rate is the total number of deaths occurring from a specified disease in a specified population during a specified time period, divided by the total number of deaths in that population during that time period.
Example: Suppose that out of 40 deaths in a herd 20 were from babesiosis, then the proportional mortality rate due to that disease would be 20/40 = 0.5 or 50%.
Case fatality rate is the number of deaths from a specified disease in a specified population during a specified period, divided by the number of cases of that disease in that population during that time period.
Example: Supppose there were 50 cases of babesiosis in the herd, then the case fatality rate due to babesiosis would be 20/50 = 0.4 or 40%.
The rates described above are those that are most likely to be used in epidemiological studies in Africa. Details of other rates, how to calculate them, and their potential uses can be found in Schwabe et al (1977).
The use of specific rates
In epidemiology, we are nearly always involved in studying the effects of determinants on the frequency of occurrence of disease. This often involves the comparison of some of the rates mentioned previously, either in the same population over time - normally before and after a determinant is added or removed - or between populations - either with or without an added determinant, or with different frequencies of occurrence of the determinant, either at the same point in time or over a period of time.
For such comparisons to be valid, the comparison groups should differ from one another only in the presence, absence, or frequency of occurrence of the particular determinant being studied. Since epidemiology usually involves the study of determinants under uncontrolled field conditions, these criteria are extremely difficult to fulfil. Nevertheless, if rates are expressed in such a form as to ignore the different characteristics which may be present within the disease agents or host populations being compared, there is a danger that such rates may give an oversimplified and even false impression of the actual situation.
Rates can be made more specific, and the comparisons between them more valid, by taking into account various different characteristics. Differences in subspecies and strains of disease agents can be accounted for by clearly defining the subspecies or strain being studied and by making sure that only those individuals affected by that particular subspecies or strain are included in the numerator. Differences in the characteristics of host populations due to age, breed and sex can be expressed by calculating rates which take these specific characteristics into consideration.
Thus, for example, one could calculate an age-specific incidence rate which is defined as the number of new cases of a disease occurring among individuals of a specified age group in a specified population during a specified time period, divided by the average number of individuals in that specified age group in that population during that time period. Alternatively, one could calculate a breed-specific incidence rate which is defined as the total number of new cases of a disease occurring among individuals of a specific breed in a specified population during a specified time period, divided by the average number of individuals of that breed in that population during that time period. One could go even further and calculate an age-breed specific incidence rate which is defined as the total number of new cases of a disease occurring among individuals in a specified age group of a specified breed in a specified population, divided by the average number of individuals of that specific age and breed in that population during that time period.
The same procedures can be applied to other morbidity and mortality rates. A large variety of specific rates can thus be calculated by using appropriate definitions of the numerator and the denominator. As a general principle, rates should be made as specific as the data allow, but not so specific as to make the numbers involved too small for statistical analysis. For analytical purposes there is little or no advantage in calculating and comparing age- or breed-specific rates if an age-breed specific rate can be calculated.
The following is an example illustrating the advantages of using specific rates in making comparisons. Suppose we wished to assess the efficiency of a tick control programme in two East Coast fever (ECF) endemic areas, where the level of disease challenge, the environmental conditions and the systems of management were approximately the same. In area A there was an average population of 10 000 head of cattle present during a 1-month study period, and 500 animals from that population developed symptoms of ECF during that period. In area B there was an average population of 15 000 head of which 1500 developed symptoms of the disease during the study period. The crude incidence rate of the disease in area A was 500/10 000 = 5 % and in area B 1500/15 000 = 10%. We might conclude, therefore, that the tick control programme in area A was more efficient than in area B.
Suppose we also found that the cattle population in area A was made up of 400 crossbred Holsteins and 9600 East African Shorthorned Zebus, while that in area B con xxxsisted of 4500 crossbred Holsteins and 10 500 East African Shorthorned Zebus. We are now able to calculate breed-specific incidence rates as indicated in Table 7.
Table 7. Breed-specific incidence rates of East Coast fever in two cattle populations.
Area |
Breed |
Number of cattle |
Number of new cases of ECE |
Incidence (%) |
Crossbred Holstein |
40.0 |
97 |
24.3 |
East African Shorthorned Zebu |
9 600 |
403 |
4.2 |
Total |
10 000 |
500 |
5.0 |
Crossbred Holstein |
4 500 |
1 059 |
23.5 |
East African Shorthorned Zebu |
10 500 |
441 |
4.2 |
Total |
15 000 |
1 500 |
10.0 |
Note that whereas the crude incidence rates remain 5% and 10% respectively, there is no difference in the breed-specific incidence rates for East African Shorthorned Zebus between the two areas and the rate for crossbred Holsteins is, if anything, less in area B than it is in area A. The difference in the crude incidence rates between the two areas is due to the fact that the much more susceptible crossbreds make up only 4% of the cattle population in area A whereas in area B they represent 30% of the cattle population.
We have already discussed the (arithmetic) mean and noted that, by itself, the mean gives no indication of how the data are dispersed about the mean value. We resolved this problem by drawing a histogram, but graphical presentation may not be always convenient and we might like to be able to reduce a data set to a few meaningful values.
At this stage, it is necessary to introduce some simple algebraic notations to express a set of data values. For example, we could refer to the data in Table 1 as X1, X2,....X150, where X1 = 1.40 and X150 = 1.44. If we wanted to refer to a more general data set without fixing the total number of values it contains, we could write X1, X2.....Xn, and say that the data contain n different values or observations. We will not always use the letter X; when we want to refer to different data sets in the same context, we will use a different letter for each set. The arithmetic mean for a given data set will be expressed by the appropriate letter with a bar over it. For example:
(X1 + X2 +.... Xn)/n
In statistics it is common to add sets of numbers together, and we shall use a special symbol to denote that operation, namely:
which means the sum of all X's from i = 1 to i = n i.e.: n
or often we just write S X or S Xi.
For example, we can write 1/n S X.
We now return to our problem of looking for a way to describe the "scatter" of values about the mean value X. It turns out, for a variety of reasons, that a convenient value is the standard deviation (S), calculated as follows:
This formula says: "Find the distance of each individual value X from the mean, square that distance, and then find the average squared distance; finish by taking the square root of the average". Many different formulae can be found in elementary books on statistics for calculating the standard deviation. The best solution is probably to buy a cheap calculator with this calculation built in. Alternatively, the following formula can be used:
This formula gives the same answer as the previous one but is easier to manipulate on a calculator. Using this formula, the standard deviation for the data in Table I was calculated as 0.1931.
There is a point to be made here about suitable levels of accuracy. A calculator may give S = 0.1930736, but this number has too many decimal places to be intelligible. About four significant figures is the maximum that will be absorbed by most readers of a paper or report, and many will notice only the first two.
How to make use of the pair of numbers and S to grasp the main features of a data set will be explained later. One problem with the mean as an indicator of the "centre" of the data is that its value can be markedly affected by the presence of a few extreme values. Suppose, to take an exaggerated case, there are 20 farmers living in a village of whom 19 earn US$ 1000 per annum and the twentieth earns US$ 1 000 000 per annum. The average (i.e. per caput) earnings of the 20 individuals is almost US$ 51 000 per annum, which is very misleading. Data with a few very large or very small values as compared to the remainder of the set, are said to be skewed.
An indicator of the "centre" of data which is not affected in this way and which is therefore more likely to give a value typical of the whole data set is the median (m). This is a number so chosen that at least half the data have a value not smaller than m, and, simultaneously, at least half the data have a value not greater than m. The median value of the data in Table I is 1.37 kg, while for Table 3 the median parturition is 2. Of course, to discover the "middle" value in a set of data one has to write all the values in the correct order, and this can be time consuming unless it is done automatically by using a (micro) computer. In most practical contexts it will make little difference which of the indicators is used, and the mean is the most frequently chosen.


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We camped here Oct 2014. Only $10. Well worth it. South side is primitive. No water and no electricity. Each site has its own picnic table and fire pit. Each site has plenty of room, however there is no brush or barrier for privacy between sites. No reservations. Just show up and pick your site. Bathrooms by the entrance were... More
- Also Known As:
- Hurricane Lake Campground Hotel Milton


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Condition:Good "few scratches from regular use but nothing to gnarly"
Color:Dust Black
Upgrades: Stock except CCM feedneck
Known Problems: None
Asking Price: $325
Shipping Options: USPS
Trades of interest: Bob Long VICE, Bob Long G6R, Bob Long Victory
Location of item: South Dakota "where the weather is cold & the women are ugly"
What is Included: Gun, barrel, stock parts kit, box
The Vice, G6R & Victory are really the only guns I'm looking for...Yes I'm willing to add for a G6R and a Victory within reason....
Empire AXE, looking for VICE's & G6R's
Started by Deftone, Feb 14 2013 12:05 PM
7 replies to this topic
Posted 14 February 2013 - 12:05 PM
Posted 14 February 2013 - 03:18 PM
250 pluss shipping
Posted 14 February 2013 - 04:15 PM
Little low man, that price isn't set in stone but I can't drop down to $250
Posted 14 February 2013 - 09:46 PM
Deal pending over on PBN...
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Posted 15 February 2013 - 04:44 PM
last offer 300 shippet!
Posted 16 February 2013 - 10:08 AM
Sorry guys the deal went through on PBN so it's gone now!!
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These adorable butterfly earrings from Lily Nily make a wonderful gift for any budding fashionista. Made from brass with an 18k gold overlay, they feature a high-polish lavender finish. Butterfly backs keep these post earrings securely in place.
Finish: Polished
Material: Brass
Decorative Accents: Butterfly Accent
Closure Style: Butterfly
Care and Cleaning: Wipe Clean
Includes: Gift Box
Overall Dimensions: 9.0 mm. L x 8.8 mm. W
Weight: 0.8 g.
other Info.
- Online Item #: 14525835
- Store Item Number (DPCI): 215-04-0131
- Imported