Presentation – Complete video for teachers and learners on how to use master document and source data to create mail merge IGCSE Practice Revision Exercise which covers all the related skills for documentation - Mail Merge Paper 2, ICT 0417 Specimen Paper 2020 Complete Solution; Task 4 Learner will be able to identify and differentiate between the master document [Letter / rtf] and the source data [Database / csv file]. Also, learners should be able to complete any given IGCSE ICT Task Mail Merge A short rationale explaining the improvements on Mail Merge Based on the above listed improvement on the lesson plan, the students will be able to: explore the purpose of mail merge much more better compare to the first lesson plan understand the mail merge process much more better compare to the first lesson plan complete a mail merge much more better compare to the first lesson plan

Below you could see some problems based on binary operations. Solved Examples Question 1: The binary operation * defined on Z by x * y = 1-2xy. Show that * is cumulative and associative. Solution: Given x * y = 1-2xy Binary operation is cumulative, since x * y = 1-2xy = 1-2yx = y * x => x * y = y * x * is cumulative. Now, check * is associative x * (y * z) = x * (1-2yz) = 1-2x(1-2yz) = 1-2x + 4xyz and (x * y) * z = (1-2xy) * z = 1-2(1-2xy)z = 1-2z + 4xyz => x * (y * z) ≠≠ (x * y) * z Thus, we can find that * is not associative on Z. Question 2: The binary operation * defined on Z by x * y = 1 + x + y. Show that * is cumulative and associative. Solution: Given x * y = 1 + x + y Binary operation is cumulative, since x * y = 1 + x + y = 1 + y + x = y * x => x * y = y * x Therefore, * is cumulative. Now, check * is associative x * (y * z) = x * (1 + y + z) = 1 + x + 1 + y + z = 2 + x + y + z (x * y) * z = (1 + x + y) * z = 1 + 1 + x + y + z = 2 + x + y + z x * (y * z) = (x * y) * z Thus, * is also satisfies associative property. A binary operation on a set is a calculation involving two elements of the set to produce another

At both the junior and senior secondary school levels in Nigeria, student performance in mathematics examinations has been poor. Within the context of large classes, with inadequate facilities, and teaching and learning in a second language, algebra and algebra word problems are introduced to students during their first year of junior secondary school. The transition from primary school arithmetic to the use of the algebraic letter is challenging to students and it is important that teachers should know the likely difficulties and misconceptions students may have as they begin algebra…

Centralized data management. One crucial aspect of payroll software is the ability to consolidate employees’ data in one place. Your employees’ personal records: This Payroll Management System is prepared by Aluko Sayo Enoch, as an opportunity to share my love for learning with an entire generation of thinkers and leaders; providing entrepreneurial platform via ICT based on the contemporary Technology Procedure: You can input new name and the entry level for a newly employed personal, consequetly the basic salary, allowance and equivalent payment information will be automated by the program. For more info or comment, log on: www.alukosayoenoch.wix.com/selfcoding Tel: +2348025358881, +2348033599440 www.unilag.academia.edu/SayoAluko/Papers

Creating and Simulating a Binomial Distribution Density Function and Distribution Graph Model using Excel Package Teaching and learning and many opportunities for its application have yet to be explored. Spreadsheet software is one of the most-used technologies for collecting, computing, and displaying data. Spreadsheets contain a rectangular array of cells in rows and columns that can hold data. Users can create business models, graphs and charts, and reports for financial, statistical, or other data. Most spreadsheet software allows a user to access real-time data from Web sites and to collaborate across teams and workgroups. Suppose an experiment has the following characteristics:  the experiment consists of n independent trials, each with two mutually exclusive outcomes (success and failure)  for each trial the probability of success is p (and so the probability of failure is 1 – p) Each such trial is called a Bernoulli trial. Let x be the discrete random variable whose value is the number of successes in n trials. Then the probability distribution function for x is called the binomial distribution, B(n, p), and is defined as follows: Where, C(n, x) = and n! = n(n–1)(n–2)⋯3∙2∙1 as described in Combinatorial Functions. This Binomial Distribution Simulation Model is prepared by Aluko Sayo Enoch, as an opportunity to share my love for learning with an entire generation of thinkers and leaders; providing entrepreneurial platform via ICT based on the contemporary Technology Procedure: You can vary the value of p to generate new q, probability density function and equivalent distribution graph. For more info or comment, log on: https://alukosayoenoch.wixsite.com/selfcoding/blog www.unilag.academia.edu/SayoAluko/Papers https://www.tes.com/teaching-resources/shop/alukosayoenoch https://www.youtube.com/feed/my_videos

This research work deals with the procedures for computing the presence of outliers using various distance measures and general detection performance for unsupervised machine learning, such as the K-Mean Clustering Analysis and Principal Component Analysis. A comprehensive evaluation of Data Mining Technique, Machine Learning and Predictive modeling for Unsupervised Anomaly Detection Algorithms on Electronic banking transaction dataset record for over a period of six (6) months, April to September, 2015 consisting of 9 variable data fields and 8,641 observations was used to carry out the survey on fraud detection. On completion of the underlying system, I can conclude that integrated techniques systems provide better performance efficiency than a singular system. Besides, in near real-time settings, if a faster computation is required for larger data sets, just like the unlabeled data set used for this research work, clustering based method is preferred to classification model.

The concept of a limit is fundamental to Calculus. In fact, Calculus without limits is like Romeo without Juliet. It is at the heart of so many Calculus concepts like the derivative, the integral, etc. So what is a limit? Maybe the best example to illustrate limits is through average and instantaneous speeds: Let us assume you are traveling from point A to point B while passing through point C. Then we know how to compute the average speed from A to B: it is simply the ratio between the distance from A to B and the time it takes to travel from A to B. Though we know how to compute the average speed this has no real physical meaning.