Principles of Accounting Notes. The Accounting Cycle. Data processing. The source documents. What are source documents: an introduction? The subsidiary books. The Cash Book. The ledgers. Trial balance. Introduction to the trial balance Preparing a Trial Balance Determining whether an account has a debit or credit balance Trial Balance and Errors Errors that are not revealed by a Trial Balance Correcting Errors Correcting errors example The Suspense Account Correcting errors using the suspense account An example of how to correct errors in a suspense account The effects of errors on profit.
Accounting Concepts. An introduction to Accounting concepts: Money measurement concept and Historical cost concept The Going concern concept and consistency concept The Accruals concept and the Materiality concept The Prudence concept and the Business entity concept.
Year End Adjustments. Capital and Revenue Expenditure. Capital and Revenue Expenditure Accounting for Capital and Revenue Expenditure Effects of improper classification of capital and revenue expenditure.
Control Accounts. Bank Reconciliation and Accounting Ratios. Single entry and Incomplete records. Departmental Accounts. Manufacturing Accounts. Accounting for Partnerships. Company Accounts. Business Ethics. Introduction to ethics and basic terms.This is a home page for a course of 16 lectures to second year Cambridge mathematics students over 8 weeks.
Current students please note that the course schedule has changed since these notes were written in What is here is still relevant, but the new schedule covers the general linear model, and Gauss-Markov theorem, which is not covered in these notes. Each lecture is relatively self-contained and has course notes of four A4 pages. Here are the schedules. There are three examples sheets. Students should receive three supervisions on the examples sheet. There are recommended books. If you enjoy this course then you should consider related courses in Part II and other items of interest.
This material is provided for students, supervisors and others to freely use in connection with this course. Copyright remains with the author. You may like to look at comments which a supervisor wrote about the attempts that his students made on the examples sheets. He notes things that they did wrong and where they had difficultiess.
You could usefully use these comments as hints and try to do better than these students. Hints for sheet 1hints for sheet 2hints for sheet 3. Here is a nice diagram showing relationships between distributions. This file has exam questions from Questions since can be found here. The questions appear in the same order as topics are covered in lectures and you will find a recommendation on the sheet concerning the work you should do for your supervisions.
Viewing and printing is identical as for the notes above. Anchoring and bias. Lecture 14 Discriminant analysis, principal components, bootstrap. Lecture 15 These examples and other material are included in this file of overhead projector slides. This is mostly larger scale displays of information that is in the notes.
However, there are scatter plots and regression lines with confidence bands for which there was not enough space to reproduce in notes. Berry and B. Casella and J. Mendenhall, R. Scheaffer and D. Snedecor, W. Other items of interest Chance News reviews current issues in the news that use probability or statistical concepts.
Rate Your Risk Quiz. What is the risk of your being wiped out with nearly everybody else next year by a catastrophic comet, meteor, or asteroid impact? One in ,? One in 20,? One in 15,? An interesting item about speed cameras and whether or not they actually reduce the rate of accidents.Below are the notes I took during lectures in Cambridge, as well as the example sheets.
None of this is official. Included as well are stripped-down versions eg. The source code has to be compiled with header. Note that the lecture notes are not reliable indicators for what was lectured in my year, or what will be lectured in your year, as I tend to change, add and remove contents from the notes after the lectures occur.
Please email any comments to dexter math. Feel free to point out errors or unclear explanations, as well as general typographic suggestions. Even better, send a GitHub pull request. Here I'd like to thank the lecturers who delivered the usually amazing lectures, and all of those who helpfully pointed out my mistakes and typos. Note that the notes have been continuously modified since the lectures have taken place, and do not necessarily accurately reflect what the lecturer said or thought.
In particular, all errors are almost certainly mine. I live TeX the words and equations. Simple diagrams are drawn in classes as well, but more complicated ones are usually done after lectures. There is usually some significant post-processing after lectures.
Principles of Accounting Notes
I assume you already have the appropriate compiler and packages installed see question 1. The list of all packages needed can be found in header. The recommended way to compile the source file is to download the source labeled "src" from the notes page together with header.
Put them in the same folderand then compile the source file with your compiler. For the notes with images, you have to download the images from the GitHub repository and place them in a folder named image. Then you can just navigate to the appropriate folder and compile.
Note that the header. You will have to manually replace the header.Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support.
Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective.
We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases. To develop an understanding of the strengths and limitations of popular data mining techniques and to be able to identify promising business applications of data mining. Students will be able to actively manage and participate in data mining projects executed by consultants or specialists in data mining.
A useful takeaway from the course will be the ability to perform powerful data analysis in Excel. Lecture notes and homework assignments will be available at the class website in SloanSpace.
You will be responsible for downloading them to prepare for class as well as to submit home works. The following books are available as supplementary materials. Occasionally, readings from these books will be suggested to augment the lecture notes. Hand, Mannila, and Smyth. Principles of Data Mining. ISBN: X. Delmater and Hancock. Data Mining Explained. ISBN: We will be using XLMiner, an Excel add-in, for homework assignments.
The free version is limited. SAS Enterprise Miner will be available for projects that require handling large amounts of data. Instructions on using the software will be provided in recitations. Your course grade will be based on case write-ups, homework, a team project and a mid-term exam.
The weights given to these components is:. Class participation will be subjectively evaluated and will be used in borderline cases to determine the final grade.Oxford Mathematics 1st Year Student Lecture: An Introduction to Complex Numbers - Vicky Neale
Don't show me this again. This is one of over 2, courses on OCW. Find materials for this course in the pages linked along the left. No enrollment or registration. Freely browse and use OCW materials at your own pace. There's no signup, and no start or end dates.The syllabus covers basic principles of accounting, developing both a knowledge of the subject and encouraging understanding, analysis and evaluation.
The Best Accounts O Level Notes
Learners cover topics such as double-entry bookkeeping, the cash book, general journal and ledger, and how to make a trial balance. Learners also consider issues such as capital and revenue expenditure, adjustments to ledger accounts, the correction of errors, control accounts, and the purchase of a business.
The syllabus is wide-ranging and also covers topics such as balance sheets, the operation of partnerships, selected ratios and the preparation of final accounts. I hope you find them useful.
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Learn how your comment data is processed. The current website has been facing problems with Dropbox. Close Menu O Level. Pakistan Studies. A Level. Computer Science. English Literature. O Level. Pls could i have o level notes of accounting of edexcel igcse. It is Accounting not Accounts. Leave a Reply Cancel reply Your email address will not be published. Close this module. I have made a new website: StudentBase. Please support this website by adding it to your whitelist in your ad blocker.
Ads are what keep running this site possible. Thank you!Cambridge is a wonderful place to study mathematics at both undergraduate and research level. Such material as is available for specific DPMMS courses example sheets, lecture notes and so on has been gathered here. Some courses have more material, others less. To post example sheets on these pages please email pdf file to examplesheets dpmms. Search site. International students Continuing education Executive and professional education Courses in education.
Research at Cambridge. Part III General Information Cambridge is a wonderful place to study mathematics at both undergraduate and research level. Schedules - on the Mathematics Faculty web site.
Department of Pure Mathematics and Mathematical Statistics
Past Exam Papers - on the Mathematics Faculty web site. The Archimedeans ' students resources and lecture notes. Example Sheets Such material as is available for specific DPMMS courses example sheets, lecture notes and so on has been gathered here.
Study at Cambridge Undergraduate Graduate International students Continuing education Executive and professional education Courses in education.
About research at Cambridge.Resources The current version of the lecture notes is available here. Note that starred sections are non-examinable.
The statistical programming language R can be downloaded from here. Rstudio is a very useful editor for R. It can be downloaded from here. Dr Pat Altham's webpage has some excellent notes on generalised linear models, as well as solutions to some past exam questions and links to further resources on R.
I have collected together some of the past exam questions here far more than you need to do! Please note that the expression for the deviance in Paper 1 5K is incorrect, and the "t value" in the R output of Paper 1 13K should be "z value". The following topics have not been lectured and are not examinable: understanding the output of anova applied to a single model.
Also, we have not discussed exponential dispersion families as being generated by a fixed density function, though we have for exponential families. Please also don't forget to look at the example sheets during your revision.
Example Sheet 1. Example Sheet 2. Example Sheet 3. Example Sheet 4. Practical Sheet 1 and solutions. Practical Sheet 2 and solutions. Practical Sheet 3. Practical Sheet 4 and solutions. Practical Sheet 5. Practical Sheet 6. Practical Sheet 7. Practical Sheet 8. Revision sheet.