Here’s a step-by-step practical guide that can help you to understand business analysis tools and offer insights to ensure you evolve as an incisive entrepreneur.
If you are an entrepreneur, creator, store owner, or retailer, then this book is for you. It will help you choose the appropriate path for your company to follow to lead your business to the next level. There are spreadsheets in appendixes that are designed to trigger the right business questions to ask and show how to use basic tools of fundamental mathematics, retail math, business metrics, and financial ratios.
Business-related probability and proportion questions of markup, markdown, and profit margin are included. In addition, the book will give you the understanding for the need-to-know normal curve and its relevance and importance in statistical methods. Extracts:
Hypothesis tests define why you are conducting statistical analysis and what particular meaning the results have for the study being conducted for your company. The findings of the statistical tests answer the business questions that are being researched. However, for this to be accomplished, the objectives of the study need to be clearly defined.
This cannot be emphasized enough. The researcher needs to understand what the storyline is for their research at hand. In other words, what are the business questions, and why are the researchers trying to find answers to those questions in the order and manner that they are doing it? What is the storyline of the journey the business is on, and why is the business trying to find those answers? That is what needs to be defined and delineated. Clear objectives about the motivation of the storyline need to be defined prior to the analysis so that the appropriate data can be collected and the data can be analyzed in the right way with the relevant statistical methods. The work that is done prior to collecting the data and conducting the statistical analysis is the crux of whether the results make any sense at all.
I have seen cases where variables were randomly chosen, the data was collected, and the data was cleaned and scrubbed carefully and then brought to a statistician to be analyzed; however, the business questions were not asked prior to collecting the data. The statistician could not do anything with the data, and the data was deemed worthless, because very few of the business objectives were able to be defined and explained. All that work of collecting the data and no clear and coherent story could be told from those results. How disappointing is that?
There are five steps to follow to determine whether a statistical test is significant, determine whether the results are meaningful, state why the results have a meaning, and determine what that meaning is.
Step 1: The Hypothesis, or Reason for the Business Question
The first step needed with a statistical procedure is to understand what the study’s hypothesis is investigating. You need to have a clear idea of what the project is about and why you are conducting it; clearly know your research questions.
Step 2: Confidence Level
The second step is determining the level of confidence used. The confidence coefficient (1 - alpha) is the probability that the null hypothesis is true, or that there is not enough evidence to reject the null hypothesis.
Step 3: Mathematical Operations and Statistical Formulas
The third step is using the statistical test that was decided on when organizing the data with the research questions in mind and then performing the mathematical operations for those statistical tests. In other words, the data is run through the formula, and all the results are calculated. The statistical tests are essential to know ahead of time in order to have the data configured so that those procedures can be used.
Step 4: Results
The fourth step is stating the results in the order that the results will have on your business. The results need to be laid out so that the story can be told. The next two chapters cover two different types of statistical procedures:, the Pearson correlation and the independent t-test, which will further expand upon this section. Appendix A reviews the different data types that are used for t-tests and Pearson correlations.
Step 5: Descriptive Analysis
The fifth step is elaborating on the results and explaining the conclusions for future studies. This is where graphs are important to use to demonstrate the points of the results and to start putting together the story of the statistics.
All five steps are needed to perform a hypothesis test for different statistics. It cannot be stressed enough how important it is to understand the business question at the beginning of the investigation and to determine the variables that will help answer the research questions before the investigation has begun because this will help with knowing the statistical methods that will be involved. Once the analysis has been conducted, the researcher will notice other areas or questions that they may have for further investigation. In the next chapter, these five steps are used for both a Pearson correlation and independent t-tests.
Rhoda Okunev is an Associate in Professional Studies at Columbia University’s School of Applied Analytics department. Rhoda teaches math and statistics at Nova Southeastern University. Rhoda taught at the Fashion Institute of Technology Continuing Education department in the Retail Analytics department where she created her own Applied Analytics course. She also taught math and statistics at the Fashion Institute of Technology in New York.