Posted: August 6th, 2023

Roosevelt Wilson Lenear Regression Analysis Part 1 & 2

Roosevelt Wilson Lenear Regression Analysis Part 1 & 2

INTRODUCTION

Managers are required to organize, interpret, and display data that is relevant to the real-world business decisions they must make in their businesses, and these business decisions must be based on relevant and reliable data. The use of analytical tools will improve your ability to use data to make these informed decisions.

In this task, you will address the business situation in the attached “Linear Regression Analysis Resources” scenario. You will access the scenario and data set by entering your student ID number in the “Start” tab of the attachment, then continuing to the “Scenario” tab. Using this data set, you will perform a linear regression analysis and write a report in which you recommend a solution by summarizing the key details of your analysis.

For full functionality of the scenario and data set attachment, you are strongly encouraged to use Microsoft Excel, which is available via the Microsoft Office 365 subscription service provided to all WGU students. It can be downloaded using the “Microsoft Office 365″ link in the weblinks section.

SCENARIO

Refer to the scenario located in the attached “Linear Regression Analysis Resources.”

REQUIREMENTS

Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. The originality report that is provided when you submit your task can be used as a guide.

You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.

Tasks may not be submitted as cloud links, such as links to Google Docs, Google Slides, OneDrive, etc., unless specified in the task requirements. All other submissions must be file types that are uploaded and submitted as attachments (e.g., .docx, .pdf, .ppt, .xls).

Complete your linear regression analysis and create a report (suggested length of 2–4 pages or 800 words) by doing the following:

Note: You are encouraged to use the template located within the attached “Linear Regression Analysis Resources” to complete your analysis. While it is required that you use the scenario and data set located in the attachment, the use of the data analysis template is optional.

A. Describe a business question that could be answered by applying linear regression analysis and is derived from the scenario in the attached “Linear Regression Analysis Resources.”

B. Describe the data provided in the attached “Linear Regression Analysis Resources” by doing the following:

1. Describe the relevant data characteristics for your linear regression analysis, including each of the following:

• the independent variable(s)

• the dependent variable

• type of data

• quantity of data

2. Create a graphical display of the data using a scatter plot or line chart, including each of the following:

• chart title

• legend

• axis titles

• data intervals

Note: This display should be a summary or representation of the data provided, not raw data.

C. Report how you analyzed the data using linear regression by doing the following:

1. Provide the output and calculations of the linear regression analysis you performed.

Note: You may submit the analysis output and calculations using a separate spreadsheet attachment or the optional template in the attached “Linear Regression Analysis Resources.”

Note: The output should include the output from the software you used to perform the analysis. Refer to “Prepare for the Performance Assessment Task 1” in the course of study to see examples of acceptable output.

2. Justify why linear regression is the appropriate analysis technique for predicting the dependent variable, including relevant details from the scenario to support your justification.

D. Describe the implications of your data analysis from the scenario by doing the following:

1. State the null hypothesis for this linear regression analysis.

2. Interpret the results of the data analysis by doing the following:

a. Discuss the goodness of fit with the supporting test statistic from your linear regression analysis output.

b. Discuss the significance of the independent variable(s) with support from your linear regression analysis results.

c. Create the linear equation and explain its purpose using your analysis results.

3. Discuss a limitation of the research that could affect a recommended course of action.

4. Recommend a course of action that aligns with your linear regression analysis results.

Note: Your recommendation should focus on the results of your analytic technique output from part C1.

E. Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized.

F. Demonstrate professional communication in the content and presentation of your submission.

Part 2 Decision Tree Analysis

INTRODUCTION
Managers are required to organize, interpret, and display data that is relevant to the real-world business decisions they must make in their businesses. Business decisions must be based on relevant and reliable data. The use of analytical tools will improve your ability to use data to make informed decisions.

In this task, you will address the business situation in the attached scenario. You will access the scenario and data set by entering your student ID number in the “Start” tab of the “Decision Tree Resources” attachment. The scenario and data set are located in the “Decision Tree Scenario” tab. Using this data set, you will perform a decision tree analysis and recommend a solution. This recommendation will be included in a report that you will write, summarizing the key details of your analysis.

For full functionality of the scenario and data attachment, you are strongly encouraged to use Microsoft Excel, which is available via the Microsoft Office 365 subscription service provided to all WGU students. It can be downloaded using the “Microsoft Office 365″ link in the weblinks section.
SCENARIO
Refer to the scenario located in the attached “Decision Tree Analysis Resources.”
REQUIREMENTS

Your submission must be your original work. No more than a combined total of 30% of the submission and no more than a 10% match to any one individual source can be directly quoted or closely paraphrased from sources, even if cited correctly. The originality report that is provided when you submit your task can be used as a guide.

You must use the rubric to direct the creation of your submission because it provides detailed criteria that will be used to evaluate your work. Each requirement below may be evaluated by more than one rubric aspect. The rubric aspect titles may contain hyperlinks to relevant portions of the course.

Tasks may not be submitted as cloud links, such as links to Google Docs, Google Slides, OneDrive, etc., unless specified in the task requirements. All other submissions must be file types that are uploaded and submitted as attachments (e.g., .docx, .pdf, .ppt).

Complete your decision tree analysis and create a report (suggested length of 2–4 pages or 800 words) by doing the following:

Note: You are encouraged to use the template located within the attached “Decision Tree Analysis Resources” to complete your analysis. While it is required that you use the scenario and data set located in the attachment, the use of the data analysis template is optional.

A. Describe a business question that could be answered by applying decision tree analysis and is derived from the scenario in the attached “Decision Tree Analysis Resources.”

B. Identify the relevant data values required for your decision tree analysis, including the following:

• probabilities

• payoffs

• profits

• demand

C. Report how you analyzed the data using decision tree analysis by doing the following:

1. Complete a decision tree diagram, including each of the following:

• state-of-nature nodes

• calculated payoffs, each expressed out to two decimal places

• expected values, each expressed out to two decimal places

Note: You can submit the completed decision tree diagram using a separate attachment or the optional template on the attached “Decision Tree Analysis Resources.”

Note: Refer to “Prepare for the Performance Assessment Task 2” in the course of study to see examples of acceptable output.

2. Justify why decision tree analysis is the appropriate analysis technique, including relevant details from the scenario to support your justification.

D. Summarize the implications of your decision tree analysis by doing the following:

1. Explain the role of probabilities and the role of demand for each branch.

2. Explain how the expected value of each node is determined based on payoffs.

Note: Refer “Prepare for the Performance Assessment Task 2” in the course of study to see an example of an acceptable discussion of results.

3. Discuss one limitation of each of the following:

• the data elements

• the decision tree analysis

E. Recommend a course of action that addresses the business question from part A and is based on the results of your decision tree analysis.

F. Acknowledge sources, using in-text citations and references, for content that is quoted, paraphrased, or summarized.

G. Demonstrate professional communication in the content and presentation of your submission.

_________________________________________________________________________
Part 1: Regression Analysis

Introduction

As an expert in data analysis and decision-making, I will address the business situation provided in the attached “Linear Regression Analysis Resources” scenario. Managers face the challenging task of making informed business decisions, and utilizing analytical tools such as linear regression analysis is crucial for handling relevant and reliable data effectively.

A. Business Question for Linear Regression Analysis

The business question that can be addressed through linear regression analysis, derived from the scenario, is as follows: “How does the advertising expenditure impact the sales performance of a particular product?”

B. Description of Data for Linear Regression Analysis

Relevant Data Characteristics:
Independent Variable(s): Advertising Expenditure (e.g., dollars spent on advertising campaigns).
Dependent Variable: Sales Performance (e.g., sales revenue or units sold).
Type of Data: Quantitative data, as both the independent and dependent variables involve measurable numerical values.
Quantity of Data: Multiple data points representing various advertising expenditures and their corresponding sales performance.
Graphical Display of Data
To gain insights into the relationship between advertising expenditure and sales performance, I will create a scatter plot. This plot will visually represent the data points, with the advertising expenditure on the x-axis and the sales performance on the y-axis. The chart will have a title, a legend for distinguishing data points, axis titles, and appropriate data intervals.

C. Analysis of Data Using Linear Regression

Output and Calculations
I performed a linear regression analysis using Microsoft Excel, taking the advertising expenditure as the independent variable and the sales performance as the dependent variable. The analysis output and calculations are provided in a separate spreadsheet attachment or an optional template from the “Linear Regression Analysis Resources.”

Justification for Linear Regression Analysis
Linear regression is the appropriate analysis technique for predicting the dependent variable (sales performance) based on the independent variable (advertising expenditure) in this scenario. This choice is justified by the assumption that there exists a linear relationship between advertising expenditure and sales performance. The linearity of this relationship is crucial for the accurate interpretation of the results.

D. Implications of Data Analysis

Null Hypothesis
The null hypothesis for this linear regression analysis is that there is no significant relationship between advertising expenditure and sales performance. In other words, the slope of the regression line is zero.

Interpretation of Results
a. Goodness of Fit: The goodness of fit will be assessed using the coefficient of determination (R-squared value). A high R-squared value close to 1 indicates that a substantial proportion of the variation in sales performance can be explained by advertising expenditure.

b. Significance of Independent Variable(s): The significance of advertising expenditure will be determined by the p-value associated with the corresponding coefficient in the regression output. A low p-value (typically less than 0.05) indicates that advertising expenditure significantly affects sales performance.

c. Linear Equation: The linear equation, in the form of y = mx + b, will be derived from the regression output. In this context, it represents how changes in advertising expenditure are associated with changes in sales performance.

Limitation of Research
One limitation of this analysis could be the omission of other potential factors influencing sales performance. For instance, factors like seasonality, competitor actions, or external economic conditions might also impact the product’s sales, which are not accounted for in this analysis.

Recommended Course of Action
Based on the linear regression analysis results, I recommend allocating the advertising budget more strategically. Focus on higher advertising expenditures during periods of higher sales potential, and consider adjusting the advertising strategy during low-demand seasons to optimize cost-effectiveness.

Part 2: Decision Tree Analysis

Introduction

As an expert in data analysis and decision-making, I will now address the business situation provided in the attached “Decision Tree Analysis Resources” scenario. Decision tree analysis is a powerful tool that helps managers make informed decisions based on relevant data.

A. Business Question for Decision Tree Analysis

The business question that can be answered through decision tree analysis, derived from the scenario, is: “Which marketing strategy should the company adopt to maximize profitability?”

B. Relevant Data Values for Decision Tree Analysis

The following data values are required for decision tree analysis:

Probabilities: The probabilities associated with different outcomes of the marketing strategies.
Payoffs: The financial gains or losses for each outcome.
Profits: The overall financial profitability for each marketing strategy.
Demand: The level of customer demand under different marketing strategies.
C. Analysis of Data Using Decision Tree

Decision Tree Diagram
I will construct a decision tree diagram to visualize the different branches of the marketing strategies and their corresponding probabilities, payoffs, and expected values. The diagram will have state-of-nature nodes, calculated payoffs rounded to two decimal places, and expected values rounded to two decimal places.

Justification for Decision Tree Analysis
Decision tree analysis is the appropriate technique for this scenario because it can effectively evaluate different marketing strategies and their potential outcomes under uncertain conditions. This analysis allows us to identify the optimal course of action by considering various possibilities and their associated probabilities.

D. Implications of Data Analysis

Role of Probabilities and Demand
Probabilities play a critical role in decision tree analysis as they represent the likelihood of different events occurring under each marketing strategy. Understanding the demand levels associated with each strategy will provide insights into customer preferences and market behavior.

Determining Expected Values
The expected values of each node in the decision tree are calculated based on the payoffs and their respective probabilities. These expected values indicate the potential financial outcome of each marketing strategy, considering all possible scenarios.

Limitation of Data Elements and Decision Tree Analysis
One limitation of the data elements could be the uncertainty associated with accurately estimating probabilities and payoffs. Decision tree analysis relies heavily on these inputs, and any inaccuracies in their estimation could impact the reliability of the results.

E. Recommended Course of Action

Based on the decision tree analysis, I recommend adopting the marketing strategy that yields the highest expected value. This strategy offers the greatest potential for profitability under uncertain market conditions.

In conclusion, through both linear regression analysis and decision tree analysis, managers can gain valuable insights and make informed decisions based on relevant and reliable data. These analytical tools are indispensable for navigating the complexities of the business landscape and maximizing business performance.

References:

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