Identify statistical tools and methods to collect data:1.Identify the appropriate family of statistical tools that you will use to perform your analysis. (The details concerning the statistical tools must include the methods, and particularly the statistical assumptions of the statistical tools and the category of data). 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? 2 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. 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? Analyze data to determine the appropriate decision for the identified problem:1.Outline the process needed to utilize your statistical analysis to reach a decision regarding the given problem. .Explain how following this process leads to valid, data-driven decisions. In other words, why is following your outlined process important? 2.Thoroughly addressed the requirements given in the addendum using all the data given in addendum and the A-Cat Corp.: Forecasting document including the use of descriptive statistics, a one-sample t-test, ANOVA, and regression analysis equation as the model for forecasting transformer requirements. 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. 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? Recommend operational improvements to stakeholders: Summarize your analysis plan for both internal and external stakeholders. Be sure to use audience-appropriate jargon when summarizing for both groups of stakeholders. Explain how your decision addresses the given problem and how you reached that decision. Be sure to use audience appropriate jargon for both groups of stakeholders. Justify why your decision is the best option for addressing the given problem to both internal and external stakeholders and how it will result in operational improvement. Be sure to use audience-appropriate jargon when communicating with stakeholders. Requirements: You will submit a 3-to 4-page paper and a spreadsheet that provides justification of the appropriate statistical tools that are needed to analyze the company’s data, a hypothesis, the results of your analysis, any references from your hypothesis test, and a forecasting model that addresses the company’s problem and your recommendations to management (6) APA sources is required.

In Unit 5, there are three main topics (problem types): describing correlations, measuring correlations (with the r value), and creating and evaluating scatterplots. You will be exposed to all three topics, and will have the opportunity to discuss and compare these topics with your fellow learners.Using any Excel dataset, choose two quantitative variables from the dataset. For example, you might choose “age” and “weight.” Next, do the following:What is the name of the dataset you have chosen? What are the names of the two quantitative variables you are investigating?Using Excel, calculate the relationship (correlation) between these two variables. Write down your calculated r value.Given the r value you calculated in number 1 above, explain what the r value tells you about the relationship between the two variables. For example, is the relationship positive or negative? Is the relationship strong, medium, or weak?Using the two variables you have chosen, create and attach to your post a scatterplot. Does the scatterplot have a linear appearance? What does the scatterplot tell you about the relationship between your two variables?Please create personalized and substantive responses to at least two other student main posts. In your response, include the following:Choose any two classmates and review their main posts.Find the dataset that each student used, and repeat their r value calculation in Excel for the variables they chose. Are they correct? Do you agree with how they described the relationship between the two variables? Explain in one paragraph and include both your results and the student results.For the same two classmates, also review their scatterplots. Discuss the “shape” of the scatterplot - is it mostly linear or is it all scattered around? Does the scatterplot seem to have a positive (upwards from left to right) or negative slope? Does their scatterplot visually match what the r value is suggesting? What can you say about the relationship based on the scatterplot?Discussion RequirementsReading and ResourcesRead the assigned chapters from the following textbooks:Bennett, J., Briggs, W.L. & Triola, M.F. (2013) Statistical Reasoning for Everyday Life (4th ed.). Upper Saddle, NJ: Pearson.Chapter 7: “Correlation and Causality”Reading the textbook and reviewing the textbook examples are excellent methods for starting each unit. Reading the textbook offers context and explanations for new concepts and methods. Completing the textbook examples on paper (and with Excel) is a great way to practice and learn the new methods and concepts introduced. Student feedback has suggested that reading the textbook and practicing the textbook examples has been particularly helpful if completed before the unit Seminar. Some students have reported that keeping a notebook handy, and recording new definitions or concepts encountered while reading is helpful, more organized, and stress reducing.This chapter includes a section that offers examples using technologies such as Excel. In addition, at the end of each chapter section, or at the end of the chapter, are review exercises that are very helpful for practicing and preparing.In this course, students may use Excel for any statistical calculations. Excel can be used to evaluate data in many ways. Excel can be used to calculate numerical measures, such as measures of center (such as mean and median) and measures of variation (variance, standard deviation, and range), as well as many other measures such as min, max, and correlation (r-value). Excel can also be used to create visualizations, such as histograms, bar graphs, pie graphs, scatterplots, and others. Excel may also be used to create linear regression equations. Because Excel is a very common tool, the Internet and YouTube both contain considerable support resources and tutorials. TEXTBOOKSBennett, J., Briggs, W., Triola, M. (2014) Statistical Reasoning: For Everyday Life.(4th ed)

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