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Supply chain management decision support tools and application case study

Supply chain management decision support tools and application case study

Q This is a personal case study. Each case study will be unique to the student submitting it. • By Wednesday of Week 1 forecasts the scores you think you will achieve for Weeks, 1, 2, 3, and 4 and send them to your professor. • "During Week 5, using your actual scores for Weeks 1, 2, 3, and 4 and your forecast values, calculate a regression equation, r, and R2 for this data. • Prepare a case study document to answer the following questions. The case study should have a cover page, intro, and body that follows APA format. If a reference is used, such as our text, then a reference page should be included, and appropriate citation(s) should appear in the body of your case study. See the sample case study above for guidance. 1. 1. What data did you use in your regression equation? Which is the independent and dependent variable? 2. 2. What is the regression equation, r, and R2 for your data? 3. 3. Statistically, what does r and R2 tell you? 4. 4. Based on your empirical evidence, how who well did you forecast your results? Justify your response. You can use any method you like to complete this assignment; however, you need to show your work. Two items in Excel are useful. The correl function will return the correlation coefficient and add-in called "Data Analysis" has a function called regression that will provide all the rest of the information you should need.

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This Case Study asks to provide a detailed regression analysis based on the student’s grade of this course from week 1 to week 4. Regression analysis can be defined as the method to find out the relationship between the two variables. The two variables have been named as the dependent variable and the independent variable. The variable that has to be predicted is the depended variables, and on the other hand, the variable that has an impact on the dependent variable is the independent variable (Gallo, 2015). The regression analysis has several uses in the data analysis, including the prediction or forecasting, model specification and parameter estimation (Gunst, 1980). The following is the table provides the data