Hi there,

I need some help with my assignment. I have the final project due this week but I need some help correcting some issues my professor found part of my draft.

Here is the assignment:

The final project for this course is the creation of a statistical analysis report. Operations management professionals are often relied upon to make decisions regarding operational processes. Those who utilize a data-driven, structured approach have a clear advantage over those offering decisions based solely on intuition. You will be provided with a scenario often encountered by an operations manager. Your task is to review the "A-Cat Corp.: Forecasting" scenario, the addendum, and the accompanying data in the case scenario and addendum. In Module Seven, you will submit your selection of statistical tools and data analysis, which are critical elements III and IV. Specifically, the following critical elements must be addressed:

III. Identify statistical tools and methods to collect data: A. Identify the appropriate family of statistical tools that you will use to perform your analysis. What are your statistical assumptions concerning the data that led you to selecting this family of tools? In other words, why did you select this family of tools for statistical analysis? B. Determine the category of the provided data in the given case study. Be sure to justify why the data fits into this category type. What is the relationship between the type of data and the tools? C. From the identified family of statistical tools, select the most appropriate tool(s) for analyzing the data provided in the given case study. D. Justify why you chose this tool to analyze the data. Be sure to include how this tool will help predict the use of the data in driving decisions. E. Describe the quantitative method that will best inform data-driven decisions. Be sure to include how this method will point out the relationships between the data. How will this method allow for the most reliable data?

IV. Analyze data to determine the appropriate decision for the identified problem: A. Outline the process needed to utilize your statistical analysis to reach a decision regarding the given problem. B. Explain how following this process leads to valid, data-driven decisions. In other words, why is following your outlined process important? C. After analyzing the data sets in the case study, describe the reliability of the results. Be sure to include how you know whether the results are reliable. D. Illustrate a data-driven decision that addresses the given problem. How does your decision address the given problem? How will it result in operational improvement?

The work I submitted:

7-2 Milestone Two: Statistical Tools and Data Analysis

The family of statistical tools I would use to perform this analysis are the Analysis of Variance (ANOVA) and the Regression Analysis. The ANOVA test compares the means of two or more groups (Holt, 2016). In this case the refrigerator sales may or may not have an impact on the number of transformers required. Using R-squared will help determine any variations of the transformers as it relates to refrigerator sales. Since the data is from 2006 to 2008 I would be relying on using the time series method. This is because management is relying on the previous year's data.

The regression analysis will help determine if the null hypothesis should be rejected of accepted. The null hypothesis is that the required transformers and sales of refrigerators have no relationship. The alternative hypothesis is that the required transformers and sales of refrigerators are related. In considering the data from 2006 to 2008 I found that the p-value does fall below the .05 alpha or level of significance. Given the p-value it is safe to conclude that the transformers required and the refrigerator sales are related. This means we can reject the null hypothesis that there is no relationship between these two variables.

The ANOVA testing can aid in determining how the transformers needed are impacted by the number of refrigerator sales. This becomes evident when R-squared shows a variation of 85.74%. With such a high percentage there seems to be a relationship between the number of transformers and refrigerator sales.

The data used comes from the previous years. The information that was gathered for this analysis is from research. The data we gathered are from 2006 to 2008. I did not have to perform any experiments to gather the information but instead used previous years data. The goal is to see if there is a relationship between the two variables.

Based on the data from the ANOVA test I found that the p-value being less than .05 alpha value means that we can reject the null hypothesis. The is in fact a significant difference in the average number of transformers from 2006 to 2008 as they relate to the sales of refrigerators. The data from the regression analysis shows a relationship between the transformers required and the sales of refrigerators. At .9259 one can see a strong relationship and R-square showing .8574 or 85.74% variation between the two variables.

My Professors Feedback for the work I submitted:

A good start but mixing ANOVA refrigerators and transformers is not the direction to take to solve this.

The assignment was asking for four things:

1) The descriptive statistics for 2006 to 2010 data with analysis for transformer sales

2) ANOVA for 2006-2010 data for monthly transformer sales with analysis

3) The predictive relationship of refrigerator sales to transformer sales.You can use regression or correlation here. Regression will give you more useful data to interpret.

4) What method you are going to use as a basis for your sales forecasting model for transformers.And why you picked that method. If you plot the monthly sales data, you will see that regression with monthly or quarterly seasonal adjustment is the correct technique.

Items 1-3 is what the case asked for.Running these analyses will lead you to item 4, the best way to solve the forecast model.

Please consider using the above for data techniques to solve this case and come up with a model that will predict future transformer sales.If you applied these models correctly, you will have your solution and be able to support your work.

FYI, ANOVA applied only to the transformer data will show you if there is a change over time which would indicate the trend.

Thank you.