Decision Making

  1. Module Overview
    In Module 1, we learned about using descriptive statistics and visual displays in data analysis and decision making. In Module 2, we focused on creating confidence intervals and conducting hypothesis testing. In Module 3, we generated regression estimates and learned how to use these estimates for forecasting. In this module, we expand on that material. We will generate multiple linear regression estimates and analyze the quality of those models.
    We encounter statistics in our daily lives more often than we probably realize and from many different sources, like the news. You are probably asking yourself the question, “What is statistics? When and where will I use statistics?” If you read any newspaper, watch television, or use the Internet, you will see statistical information. There are statistics about crime, sports, education, politics, and real estate. Typically, when you read a newspaper article or watch a television news program, you are given sample information. With this information, you may make a decision about the correctness of a statement, claim, or “fact.” Statistical methods can help the business managers make the “best educated guess.” In general, statistics is a field of study concerned with summarizing data, interpreting data, and making decisions based on data.
    Once you have collected data, what will you do with it? Data can be described and presented in many different formats. For example, suppose you are interested in buying a house in a particular area. You may have no clue about the house prices, so you might ask your real estate agent to give you a sample data set of prices. Looking at all the prices in the sample often is overwhelming. A better way might be to look at the median price and the variation of prices. The median and variation are just two ways that you will learn to describe data. Your agent might also provide you with a graph of the data.
    In Module 1, we learned about using descriptive statistics and visual displays in data analysis and decision making. In this module, we will focus on creating confidence intervals and conducting hypothesis testing.
    A statistical graph is a tool that helps you learn about the shape or distribution of a sample or a population. A graph can be a more effective way of presenting data than a mass of numbers because we can see where data clusters and where there are only a few data values. Newspapers and the Internet use graphs to show trends and to enable readers to compare facts and figures quickly. Statisticians often graph data first to get a picture of the data. Then, more formal tools may be applied. Some of the types of graphs that are used to summarize and organize data are the dot plot, the bar graph, the histogram, the stem-and-leaf plot, the frequency polygon (a type of broken line graph), the pie chart, and the box plot. In this module, our emphasis will be on histograms.
    A histogram is a graphic version of a frequency distribution. The graph consists of bars of equal width drawn adjacent to each other. The horizontal scale represents classes of quantitative data values and the vertical scale represents frequencies. The heights of the bars correspond to frequency values. Histograms are typically used for large, continuous, quantitative data sets. A frequency polygon can also be used when graphing large data sets with data points that repeat. The data usually goes on y-axis with the frequency being graphed on the x-axis.
    In this module, you will be using Microsoft Excel to calculate statistics and produce the graphical displays mentioned above. Microsoft Excel is used widely in the workplace today, so the tools you learn in this module will be very useful.
  2. Required Reading
    Statistics are all around you, sometimes used well, sometimes not. We must learn how to distinguish the two cases. Just as important as detecting the deceptive use of statistics is the appreciation of the proper use of statistics. You must also learn to recognize statistical evidence that supports a stated conclusion. When a research team is testing a new treatment for a disease, statistics allows them to conclude based on a relatively small trial that there is good evidence their drug is effective. Therefore, it is important to understand statistics. In this course, you would reform your statistical habits from now on. No longer will you blindly accept numbers or findings. Instead, you will begin to think about the numbers, their sources, and most importantly, the procedures used to generate them. In this way, you can become a more rational decision maker by analyzing the past performance to make business planning.
    Statistics are often presented in an effort to add credibility to an argument or advice. You can see this by paying attention to television advertisements. Many of the numbers thrown about in this way do not represent careful statistical analysis. They can be misleading, and push you into decisions that you might find cause to regret. If you cannot distinguish good from faulty reasoning, then you are vulnerable to manipulation and to decisions that are not in your best interest. Statistics provides tools that you need in order to react intelligently to information you hear or read. In this sense, statistics is one of the most important things that you can study. For these reasons, learning about statistics is essential to business intelligence. This course will help you refresh some statistical essentials that are related to business analytics and decision making.
    The primary resource for this module is Introductory Business Statistics, by Alexander, Illowsky, and Dean.
    Alexander, H., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics. Openstax. Retrieved from https://openstax.org/details/books/introductory-business-statistics
    For Module 4, you should read through the following material in this textbook:
    Chapter 13: Linear Regression and Correlation
    Sections 13.4, 13.5, and 13.6 only
    These sections introduce multivariate or multiple linear regression analysis. These sections also explain some of the problems that can occur in regression analysis.
    You are now familiar with several tools in the Analysis Toolpak. Regression analysis is just another one of those tools. Please review the following tutorial for help in generating regression estimates in Excel:
    https://www.excel-easy.com/examples/regression.html
  3. Optional Sources
    Center for Creative Leadership Website. (2015) Retrieved from http://www.ccl.org/index.shtml
    McNamara, C. (2017) All about Leadership. In Free Management Library. Retrieved from http://managementhelp.org/leadership/
    Privacy Policy | Contact
    Use the IBISWorld database or other databases such as Business Source Complete (EBSCO) and Business Source Complete – Business Searching Interface in our online library.
    Check the professional market research reports from the IBISWorld database to conduct the industry analysis. IBISWorld can be accessed in the Trident Online Library.
    IBISWorld Overview (n.d.). IBISWorld, Inc., New York, NY.
    IBISWorld Forecast (n.d.). IBISWorld, Inc., New York, NY.
    IBISWorld Data and Sources (n.d.). IBISWorld, Inc., New York, NY.
    IBISWorld Navigation Tips (n.d.). IBISWorld, Inc., New York, NY.
  4. IBISWorld is a proprietary database providing industry research. It is accessible via the Trident Online Library, Additional Library Resources.
    Trident Online Website: https://mytlc.trident.edu
    University Email Address:[email protected]
    Password: Ijeoma0724!!
    Locate: Library Access and click Additional Library Resources
  5. Case Assignment
    You are a consultant who works for the Diligent Consulting Group. In this Case, you are engaged on a consulting basis by Loving Organic Foods. In order to get a better idea of what might have motivated customers’ buying habits you are asked to analyze the factors that impact organic food expenditures. You performed a simple linear regression analysis in the Module 3 Case. Now, you are adding a layer of complexity to that analysis and including more independent variables in your model.
    Using Excel, generate regression estimates for the following model:
    Annual Amount Spent on Organic Food = α + b1Age + b2AnnualIncome
  • b3Number of People in Household + b4Gender
    After you have reviewed the results from the estimation, write a report to your boss that interprets the results that you obtained. Please include the following in your report:
  1. The regression output you generated in Excel.
  2. Your interpretation of the coefficient of determination (r-squared).
  3. Your interpretation of the global test for statistical significance (the F-test).
  4. Your interpretation of the coefficient estimates for all the independent variables.
  5. Your interpretation of the statistical significance of the coefficient estimates for all the independent variables.
  6. The regression equation with estimates substituted into the equation. (Note: Once the estimates are substituted into the regression equation, it should take a form similar to this: y = 10 +2×1 +1×2 +4×3 +0.9×4)
  7. An estimate of “Annual Amount Spent on Organic Food” for the average consumer. (Note: You will need to substitute the averages for all the independent variables into the regression equation for x, the intercept for α, and solve for y.)
  8. A discussion of whether or not the coefficient estimate on the Age variable in this estimation is different than it was in the simple linear regression model from Module 3 Case. Be sure to explain why it did/did not change.
    Data: Download the Excel-based data file: BUS520 Module 4 Case. – Excel attached must be completed separately from word document
    After you have reviewed the data and completed the Excel
    Written Report
    Length requirements: 3 pages minimum (not including Cover and Reference pages). NOTE: You must submit 3 pages of written discussion and analysis.
    Provide a brief introduction to/background of the problem, similar to the introduction/background you provided in Module 1 through 3 Case submissions. – I have old assignments if needed or can request same writer [408025]
    Provide a brief comparison of simple linear regression and multiple linear regression.
    Provide a written analysis that addresses each of requirements listed under the “Case Assignment” section.
    Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size.
    Please use keywords as headings to organize the report.
    Avoid redundancy and general statements such as “All organizations exist to make a profit.” Make every sentence count.
    Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words.
  9. Assignment Expectations
    Provide a written analysis that addresses each of requirements listed under the “Case Assignment” section.
    Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size.
    Please use keywords as headings to organize the report.
    Avoid redundancy and general statements such as “All organizations exist to make a profit.” Make every sentence count.
    Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words.

Sample Solution

ACED ESSAYS