Communication Assignment Sample For Singapore Students
Posted on: 10th Oct 2022

# BUS107 Quantitative Methods SUSS Assignment Sample Singapore

BUS107 Quantitative Methods is a rigorous introduction to basic mathematical concepts and methods used in business and economics. The course covers topics such as algebra, geometry, probability, and statistics. It is designed for students who want to develop quantitative skills for critical thinking and decision-making in the business world.

If you’re looking to gain a strong foundation in mathematics, this is an excellent course to take. The material is taught in a way that’s easy to understand, and you’ll be able to apply what you learn in real-world scenarios. By the end of the semester, you’ll be able to solve complex problems and make sound business decisions using quantitative methods.

If you’re looking to download high-quality assignment answers for the BUS107 Quantitative Methods course at SUSS in Singapore, look no further than myassignmenthelp.sg. We offer comprehensive assistance for BUS107, including resources such as the BUS107 Pre Course Quiz (PCOQ), BUS107 questions and answers, BUS107 PDF materials, BUS107 TMA (Tutor-Marked Assignment), BUS107 GBA (Group-Based Assignment), BUS107 Participation Questions, BUS107 Written Exam, and BUS107 Pre Class Quiz (PCQ).

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In this section, we are providing some assignment briefs. These are:

### BUS107 Assignment Brief 1: Apply linear programming models for optimization problems.

Linear programming models can be used for a variety of optimization problems. In general, a linear programming model consists of a set of variables, called decision variables, and a set of constraints. The objective is to find the values of the decision variables that minimize or maximize an objective function, subject to the constraints.

Linear programming models are widely used in business and economics. They can be used to solve problems such as how to allocate resources among competing objectives, how much product to produce at what price to maximize profit, and how many workers to assign to each task to minimize production costs.

There are several different types of linear programming models, including transportation models, network flow models, integer programming models, and sensor models. each with its own specific formulation.

### Assignment Brief 2: Use historical data to build forecasts for future business developments.

There are a variety of ways to use historical data to build forecasts for future business developments. One approach is to use regression analysis to identify relationships between historical data and future outcomes. This can help you predict future sales or customer behavior based on past trends.

Another approach is to use data mining techniques to identify patterns in historical data. This can help you anticipate upcoming changes in the market or consumer behavior. By understanding how past events have influenced the present and future trends, you can make more informed decisions about your business strategy.

Whatever method you choose, it’s important to have accurate, timely data so that your forecasts are as accurate as possible. With the right data and the right tools, you can make sound predictions about the future of your business.

### Assignment Brief 3: Identify a recommended decision alternative in uncertain settings.

One recommended decision alternative in uncertain settings is to make a decision based on the expected value. The expected value is calculated by taking the product of each possible outcome and its probability and then summing these products. This approach takes into account both the magnitude and likelihood of each outcome.

For example, imagine you are considering whether or not to go for a run. There are two potential outcomes: you either feel great afterward or you feel terrible. The expected value would take into account both of these outcomes, as well as the likelihood of each one happening. So if you think there’s a 50% chance that you’ll feel great after running and a 50% chance that you’ll feel terrible, then the expected value would be 0 (50% x feeling great + 50% x feeling terrible).

This approach can be used in a variety of uncertain settings, from personal decisions to business decisions. It is a helpful tool for making decisions when there is uncertainty about the future.

### BUS107 Assignment Brief 4: Interpret software output for business insights.

Interpreting software output for business insights can be a challenging task. There are a few things you can do to make sure you are getting the most out of your data.

First, you need to understand the basics of data interpretation. This means understanding what the different types of data mean and how they can be used to provide insights. For example, numerical data can be used to track trends over time, while categorical data can be used to identify customer segments.

Once you have a good understanding of the different types of data, you need to start looking at your software output in a more critical way. Try to identify patterns and trends that could be helpful for your business. For example, if you notice that certain customer segments are more likely to purchase certain products, you can start targeting your marketing efforts toward those segments.

Finally, don’t be afraid to ask for help. If you’re not sure how to interpret your data, there are people who specialize in data interpretation and can help you make sense of it all. With the right help, you can unlock the insights contained in your software output and use them to improve your business.

### Assignment Brief 5: Demonstrate the use of a simulation model to estimate measures of performance of operational situations.

Simulation models can be used to estimate measures of performance in a variety of operational situations. For example, a simulation model could be used to predict the average wait time for customers at a restaurant during peak hours.

Simulation models can be helpful in anticipating how different changes (e.g., adding new employees, altering the menu, etc.) might affect measures of performance. They can also help identify areas where bottlenecks are likely to occur and suggest strategies for improving performance.

To create a simulation model, you will need to first collect data on the operational situation you are interested in. This data can be collected through observation, surveys, or other means. Once you have this data, you will need to input it into a software program that can generate a simulation model.

There are many different software programs that can be used to create simulation models. Some of these programs are designed for specific industries (e.g., manufacturing, healthcare, etc.), while others are more general purpose.

Once you have created your simulation model, you can use it to estimate measures of performance under different scenarios. For example, you might want to compare the average wait time for customers under different staffing levels. Or you might want to compare the average wait time for customers under different menu options.

### BUS107 Assignment Brief 6: Implement iterative algorithms to find the solutions for network flow problems.

There are many different ways to solve network flow problems. One common approach is to use an iterative algorithm, which is a type of algorithm that takes a sequence of steps, each of which produces a better solution than the last.

One popular iterative algorithm for solving network flow problems is the transportation algorithm. This algorithm starts by setting up a matrix (called the transportation table) that contains all the information needed to solve the problem. The transportation table has one row and one column for every node in the network. The table entries are set to 0 or -1, depending on whether or not there is an available route between two nodes.

The transportation algorithm works by computing a tentative solution to the problem and then checking to see if it can be improved. To compute a tentative solution, the algorithm starts by finding the shortest path between every pair of nodes. Once all the shortest paths have been found, the algorithm computes the flow through each of these paths. The total flow through the network is then computed and used as the tentative solution.

The transportation algorithm then checks to see if there is a way to improve the tentative solution. If there is, the algorithm makes the necessary changes and then recomputes the flow through the network. This process is repeated until an optimal solution is found.

The transportation algorithm is just one of many iterative algorithms that can be used to solve network flow problems. Other popular algorithms include the simplex algorithm, the interior point algorithm, and the Hungarian method.

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