sampling methods

PART 1
Sampling refers to the collection of data. The five primary sampling methods are discussed in section 1.2 of the textbook for this module and include:
Simple random sampling
Stratified sampling
Cluster sampling
Systematic sampling
Convenience sampling
Please explain ONE of these types of sampling methods and give an example of how it could be used. For example, you could explain simple random sampling and use an example of how it could be conducted by using a random number table to select employees to interview in your workplace about employee morale.
Please remember to be specific and to put the discussion into your own words. Do not just copy and paste technical information from the textbook or another source. Really strive to understand how the sampling method could be used.
Week 1: Provide your initial discussion post to the question. Be sure to include references to any resources you used. You should use at least one resource to help you with your initial discussion.

PART 2
Chapter 9 in the textbook for this module discusses hypothesis testing. Describe a hypothesis test that you would like to conduct. Be sure to include the following:
The null and alternate hypotheses (discussed in section 9.1 in the textbook).
Explain whether this test is a one-tail or two-tail test (section 9.3 in the textbook discusses the difference).
Describe the data you would need to collect to conduct the test.
Please remember to be specific and to put the discussion into your own words. Do not just copy and paste technical information from the textbook or another source. Really strive to understand how you could conduct this hypothesis test.
Week 1: Provide your initial discussion post to the question. Be sure to include references to any resources you used. You should use at least one resource to help you with your initial discussion.

PART 3
In section 13.4 of the textbook for this module, the assumptions of linear regression are discussed. They include:
Linearity: X and Y have a linear relationship.
Homoscedasticity: The errors are constant.
Independence: The X variables are independent of each other.
Normality: Y is normally distributed.
Based on the discussion regarding these assumptions in this textbook section as well as your own additional research, discuss the potential impact of assumption violations on the interpretation of regression results. In other words, what happens to the regression output and the value of the interpretation if one of the assumptions does not hold? Choose ONE assumption and discuss how the violation of this ONE assumption impacts the validity of the regression results.
Keep in mind: you should put the discussion into your own words. Do not just copy and paste technical information from the textbook or another source.
Really strive to understand what the assumption means and how the violation of it impacts the output from a practical perspective.
Week 1: Provide your initial discussion post to the question. Be sure to include references to any resources you used. You should use at least one resource to help you with your initial discussion.

PART 4
Do some research on recent trends in business analytics such as Google Analytics, cloud computing, artificial intelligence, machine learning and so on.
Focus on one trend and discuss its relevancy to your business or any business you would like to discuss.
Consider how these analytics tools would impact business decision making.
Week 1: Provide your initial discussion post to the question. Be sure to include references to any resources you used. You should use at least one resource to help you with your initial discussion.

PART 5
As you complete this course, reflect back and answer these questions:
What two concepts do you think are the most relevant to the business and your MBA degree and why?
Which one or two concepts do you think are the least relevant to the business and your MBA degree and why?
Which concepts did you find particularly difficult to learn and why?