Unit 4 Success Guide Support Materials

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Use Excel to analyze data and perform regression in three questions related to business analytics. Explain your answers thoroughly and include the regression output tables for questions 1 and 2. Submit your completed assignment using the provided template from Doc Sharing.

Paper For Above instruction

The assignment focuses on applying business analytics techniques, specifically linear regression, to real-world data sets using Excel. The three questions encompass forecasting, estimating relationships through regression, and assessing predictive capability via scatter plots and trend lines. Critical to the successful completion of this assignment is a clear understanding of regression analysis, interpretation of output metrics, and the ability to communicate the analytical results effectively in writing.

Question 1:

A dataset provided by the U.S. Census Bureau includes rental and leasing revenue figures for office machinery and equipment in the United States over a seven-year period. Utilizing Excel’s regression tool, generate a linear regression model to predict revenue for the year 2011. Present the regression output table and interpret the regression formula. Based on metrics like the R-squared and standard error, assess the confidence level of your forecast and discuss the reliability of the prediction.

Here, the critical task is to understand how well the regression model fits the data (via R-squared) and how precise the forecast might be. A high R-squared indicates a good fit and supports confidence in the prediction, but other metrics should also be considered.

Question 2:

Given survey data from 19 employees, perform a multiple linear regression analysis to model job satisfaction scores based on three predictors: relationship with supervisor, opportunities for advancement, and overall quality of work environment. Use Excel to derive the regression formula, examine the regression output for statistical significance and reliability, and identify variables that may not be strong predictors. Then, apply the regression formula to predict the job satisfaction score of a hypothetical employee with specified predictor values.

Interpretation of the regression coefficients, p-values, and overall model fit (including R-squared) is essential to determine the reliability of estimates and predictor significance. It is important to recognize variables that do not contribute meaningfully to the model and to justify your conclusions based on the metrics.

Question 3:

Construct a scatter plot of bond rates versus prime interest rates using the provided data. Fit a trend line and display the regression formula and R-squared value. Evaluate whether the bond rate can be accurately predicted by the prime interest rate, leveraging the regression output metrics. Discuss implications of the findings regarding the inverse relationship hypothesis and the model’s predictive power.

In this task, visualization and analysis of the scatter plot, along with the trend line’s statistical measures, will determine the predictability of bond rates from prime interest rates.

References

  • Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
  • Kenton, W. (2021). Regression Analysis in Business. Investopedia. https://www.investopedia.com/terms/r/regressionanalysis.asp
  • Gelman, A., & Hill, J. (2007). Data analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
  • Ramanathan, R. (2002). Introductory Operations Research: Principles and Applications. Thompson Learning.
  • Shmueli, G., & Koppius, O. R. (2011). Predictive Analytics in Information Systems Research. MIS Quarterly, 35(3), 553-572.
  • Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. International Joint Conference on Artificial Intelligence.
  • Engel, J. (2010). Economics of the Bond Market. Journal of Financial Economics, 97(3), 414-433.
  • Burns, A., & Mitchell, M. (1946). Measuring Business Cycles. NBER.
  • National Institute of Standards and Technology. (2010). Special Publication 800-14: Generally Accepted Principles and Practices for Securing Information Technology Systems.
  • ISO/IEC 27001 Standard. (2013). Information Security Management Systems.

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