There are 3 groups of questions: Introductory, Decision-Forcing, and Challenge.Introductory Questions help you understand the case. If you can answer all of them, you are ready to take further analysis of this case. DO NOT answer them in your case report.Decision Forcing Questions help you analyze Matsushita’s and Sony’s problems and solutions. You MUST answer these questions in your case report.Challenge Questions help you evaluate Matsushita’s and Sony’s different supply chain management strategies. You MUST answer these questions in your case report.Introductory QuestionsWhat bad news had Mr. Yukio Shohtoku (managing director of Matsushita) been hearing?What is the “eradicating and creating” strategy of Matsushita?Why does Mr. Yoshihiro Taya (vice president of Sony MECS) worry about the “cycle time” of Sony’s digital camera and camcorder products?How did Sony reengineer its supply chain to reduce the lead-time of its digital camera and camcorder production?Decision Forcing QuestionsWhat are the differences between Matsushita and Sony’s products?What are the differences between Matsushita and Sony’s supply priorities?Why did Matsushita and Sony’s former supply chains mismatch their product characteristics with their supply priorities?What are the advantages of manufacturing in China? What are the disadvantages of manufacturing in China?Challenge QuestionsWhose supply chain reengineering strategy is a short-term solution and whose is a long-term solution?What are the pros and cons of these strategies?The report should be at least two pages, single space, 1 inch margins, and #12 fond.

Download the file Sample Data. Prepare a chart similar to the one in the downloaded file to indicate whether the correlation between variables A and B were found to be positive, negative, or minimal. Provide explanation and justification for your decisions. In your own words, explain what it means if the correlation of 2 variables is positive, negative, or minimal (close to 0), and give an example of each. What do you deduce from the correlations? Explain if you believe these to be short or long-term objectives and outcomes. What are the implications for Big D Incorporated regarding its client in the outdoor sporting goods? What are the implications for the penetration into the indoor sporting goods market? Also, how can you use the correlation tools to identify the variables in the research toward the expansion into the indoor sporting goods market?

Purpose  This taskprovides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models. Resources: Microsoft Excel® DAT5/65 Week 5 Data File Instructions:  The Excel file for this task contains a database with information about the tax assessment value assigned to medical office buildings in a city. The following is a list of the variables in the database: FloorArea: square feet of floor space Offices: number of offices in the building Entrances: number of customer entrances Age: age of the building (years) AssessedValue: tax assessment value (thousands of dollars) Use the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics. Construct a scatter plot in Excel with FloorArea as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables? Use Excel’s Analysis ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue? Construct a scatter plot in Excel with Age as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables? Use Excel’s Analysis ToolPak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of AssessmentValue? Construct a multiple regression model. Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2? Which predictors are considered significant if we work with ?=0.05? Which predictors can be eliminated? What is the final model if we only use FloorArea and Offices as predictors? Suppose our final model is: AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?