Information Strategy

Managerial Applications of Business Analytics

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 is 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?