# Devry BIAM300 2022 March Discussions Latest (Full)

Question # 00822597 Posted By: Ferreor Updated on: 04/18/2022 01:40 AM Due on: 04/18/2022
Subject Education Topic General Education Tutorials:
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BIAM300 Managerial Applications of Business Analytics

Week 1 Discussion

Information Strategy

Review the Week 1 topics located in 1.1, 1.2, 1.3, and 1.4 and answer the following questions.

1. Why are data important to a business manager?

2. What role does visualization play in the reporting process within a business?

3. Does using RStudio help create visualizations from data? Why is RStudio an important tool?

4. Why is it important to understand data formats?

5. How can we use the information we learned this week to help a manager make a decision?

BIAM300 Managerial Applications of Business Analytics

Week 2 Discussion

Data Management and Visualization Options

Review this week's content Sections 2.1 to 2.8, and answer the following questions.

1.What is data cleansing, and why is it important?

2. Why is it important to understand how to manage missing values? What role does this play in data preparation?

3. Select three chart types from this week's lesson, and discuss examples of how you would use these chart types in a report to the CEO.

BIAM300 Managerial Applications of Business Analytics

Week 3 Discussion

Probability Distribution and Statistics

There are many important topics to discuss this week. Review Sections 3.1 to 3.10 and respond to the questions below.

1. What is the purpose of using a Venn diagram?

2. Why is Bayes' Theorem important to understand? How is it used in business analytics?

3. Define normal distribution, and explain why it is important to a CEO?

4. This week, we have examples of R and how it can chart distributions. Why are charts important in the business environment?

BIAM300 Managerial Applications of Business Analytics

Week 4 Discussion

Probability Distribution

After reviewing Sections 4.1 to 4.7, your CEO has asked you for some clarification on probability. Please respond to the questions below.

1. How would you explain simple linear regression (SLR) to a CEO?

2. How is multiple regression different from single regression? What are the differences? Please provide an example of multiple regression.

3. Why is regression, multiple or simple, an important concept to understand, especially for a CEO? What are some examples of practical applications of regression?

BIAM300 Managerial Applications of Business Analytics

Week 5 Discussion

Data Mining

Review Sections 5.1 to 5.5 and answer the questions below.

2. What are the biggest challenges a company faces when trying to implement a data warehouse and use data mining?

3. Search the web for some publicly available data sources that a company might want to use for their data mining efforts.

4. Please share some examples of successful companies that utilize data mining as part of their success.

BIAM300 Managerial Applications of Business Analytics

Week 6 Discussion

Decision Trees

Review Sections 6.1 to 6.6 and respond to the questions below.

1. What is the difference between a classification tree and a decision tree?

2. Tree-based models rely on recursive partitioning and pruning. Please define what partitioning and pruning are and how they relate to the decision-making process.

3. Please share some examples of why a regression tree and a decision tree are useful.

BIAM300 Managerial Applications of Business Analytics

Week 7 Discussion

Time Series

Review Sections 7.1 to 7.4 and respond to the questions on time series below.

1. Why is using time series as a form of analysis important to a business? How is it used?

2. Why is a time series plot beneficial?

3. What is exponential smoothing, and how does it work?

4. How can regression be used to forecast an event? Why would a manager want to better understand this?

BIAM300 Managerial Applications of Business Analytics

Week 8 Discussion

Monte Carlo Analysis

Considering the Monte Carlo analysis as possibilities for looking at optimization and also the probabilities, please explain how the Monte Carlo model works. Also discuss the following analysis options by explaining the pros and cons for each.

What-If Analysis

Optimization

Forecasting

Building simulations

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