Reference no: EM131203174

Quantitative Analysis & Decision Making

The final paragraph (three or four sentences) of your initial post should summarize the one or two key points that you are making in your initial response.

Your posting should be the equivalent of 1 to 2 single-spaced pages (500-1000 words) in length.

References in APA format.

You are designing a direct marketing campaign for an online clothing retailer. As part of your design, you quantify the expected response rates by ethnic group. Your definition of the term "ethnicity" follows that of the U.S. Census Bureau (e.g., Hispanic, Asian, African American, etc.). You want to test your campaign using 1,000 randomly selected households, but you want your sample to mimic the US population in terms of the proportion of different ethnicities (e.g., if Hispanics constitute about 12 percent of the US population, 12 percent of your sample should be Hispanic).

- Assess the appropriateness of using a simple random sample as the test's sampling plan.

- Evaluate other potential sampling plans, and describe why such other sampling plans might be more or less appropriate than using a simple random sample. Support your discussion with relevant examples, research, and rationale.

In the past several weeks, you have been introduced to a range of statistical data analysis tools. Consider what you have learned in the context of progression of data, information, and knowledge. What are the specific techniques you would consider most helpful in transforming information into knowledge (as opposed to just translating data into information)? Support your discussion with relevant examples, research, and rationale.

Experience teaches us that the bulk of the technical material covered in this course will, unfortunately, be forgotten shortly thereafter. If you were to commit just three concepts you have learned in this course to your long-term memory, which concepts would you select and why?

Week 1 Summery:This week introduced some of the foundational concepts of describing business statistics: measures of central tendency and measures of variability. The goal for this week was to familiarize you with those concepts, as well as to set the tone for the remainder of this course. As noted in the introduction, statistics is a means to the end that is gaining informational advantage. It is well known that companies have access to tremendous amounts of data, yet many are still information deprived. Why? There are numerous reasons, not the least of which is that many of today's managers are still, in a way, afraid of data, largely because they lack familiarity with basic data analytic or statistical concepts. This is one of the reasons that the focus of this course is not on computing statistics as much as it is on developing a strong understanding of how even simple statistical steps can yield intriguing, decision-shaping insights.

What should you expect from the course in the next five weeks? It is important that you consider each of the topics that follow in the context of an appropriate decision-making situation. Think of each of the topics as a tool that will help you in answering an important business question by using objective analysis. As noted earlier, our explicit goal is to present an array of key statistical concepts in a manner that reflects their decision-making utility-not abstract equations. Think of data as raw material and statistics as a tool used to transform data into something that will assist the organization's decision-making process. This is the ultimate reason why the sometimes esoteric ideas presented in this course are ultimately quite useful.

Next week, you will continue to learn about regression analysis and also gain an understanding of goodness-of-fit testing and forecasting.

Week 2 Summery: This week, you discussed probability as one of the best tools available to business managers to manage risks associated with unknown outcomes. You also discussed the relationship between population and sample, a key determinant of the quality of insights used in business. Finally, you learned to recognize different types of discrete random variables and to apply the probability distributions that are commonly used in analyzing experiments involving such variables.

Week 3 Summery: This week, you learned about benchmarking as a commonly used tool in business decision making. This will prepare you for next week's focus on the methods of finding differences between two quantities: t-tests and chi-squared tests. Many characteristics that your organization may study in a benchmarking effort may require the analysis of data that is not limited to integer values. When working with such data (that can contain decimal values), you will likely be working with the Normal Distribution. You also learned the importance of distinguishing between a sample and the population from which it is taken.

Week 4 Summery:This week, you learned about three statistical techniques-hypothesis testing, correlation, and regression analysis. You also learned about types of errors that can occur in hypothesis testing, types of relationships, the distinction between correlation and causation, types of regression, and the distinction between simple and multiple regression.

Week 5 Summery: This week, you learned about goodness-of-fit tests, and the ability of such tests to confirm (or disprove) the nature of a statistical distribution that has been hypothesized. You also learned about the process of estimating future outcomes called forecasting, and, specifically, the widely used version of forecasting known as time series analysis. Remember that time series analysis is a trend projection and requires limited data for analysis.

Week 6 Summery

This week, you learned how statistics can be used in business as a decision-aiding tool. You also learned about decision support systems and their functions in statistical analysis.

In the beginning of this course, you learned about the continuum of data-information-knowledge as the basis for understanding the value of statistics in business. The intent of this course was to provide you with an overview of some statistical applications pertinent to business. From a technical point of view, you learned about the computational logic of these techniques. From a business utility point of view, you learned about the value of different methodologies within the decision-making context.

Why is all this important? Any midsize or large company invests considerable resources in data collection and maintenance. The general belief is that systematic data analysis will lead to effective decisions. Yet, relatively little effort is spent figuring out what insights might be hidden in the data. To use an often-repeated phrase, "Many organizations are data-rich but information-poor." Why? There are many reasons for this, but one of the dominant ones is the fear of quantitative analysis still evident in many informational users. This course hopefully helped prepare you to use quantitative analysis to support business decisions.

?Evaluate the applicability of each of the concepts that you've selected to a business environment in which you have worked in the past (or with which you might be familiar).

?Describe how you would apply each of the concepts. Be specific.

?Explain your goal in applying each concept to the environment that you have described.

2 pages each question