IE 311 Fall 2015 Minitab Introduction Lab Page 1

Ie 311 Fall 2015 Minitab Introduction Lab Page 1ie 311 Fall 2015 Mi

Identify a place to store your files, either in a directory on the hard drive or a USB thumb drive. Create a folder for this lab. Log into the IE 311 Canvas site and click on the MiniTab Lab assignment. Then click on “IE_311_Minitab_Lab.MTW” file and save it to your directory. After saving, log out of Canvas. Open the saved file in Minitab by clicking File > Open Worksheet from the Minitab menu. Locate and open the file from your saved location.

Note any popup messages and observe the message in the Session window and the data in the worksheet. Save your project by clicking File > Save Project As, naming it of your choice, and saving it in your folder. You can later save ongoing work by clicking the disk icon or pressing Ctrl + S. Record the name of your project file here: _________

Conduct a two-sample hypothesis test for H₀: μ₁ = μ₂ versus Hₐ: μ₁ ≠ μ₂ at α=0.10, using data columns MDS1 and MDS2. State what assumptions you are making about the data. Interpret the test result: Can you reject H₀? Why or why not? When finished, close all plot windows without saving them.

Test for normality of the SDS data. Choose a method discussed in class to perform this check and record it here: Method: _________________________________. Conduct the test. Can we conclude the data are normally distributed? Why or why not? Close plot windows without saving.

Repeat the normality test for the LDS data: select a method, conduct the test, analyze normality conclusion, and close plots as before.

Consult the Minitab Stat Guide. Access it via Help > Stat Guide. Expand Basic Statistics, then the 2-sample t, and review the summary. Click on linked blue words to explore further. Access More > 2-Sample t > Dependent and independent samples, and describe at least one new insight gained.

Identify menu items for each operation:

  • 2-sample t test: ___________________________
  • Create a new column from existing ones: ___________________________
  • Compare two population proportions: ___________________________
  • Generate random data: ___________________________
  • Generate a stem-and-leaf chart: ___________________________
  • Print a copy of the Session window: ___________________________

Explain why it is important to verify the presence of the MTB > prompt in the Session window.

Save your project and exit Minitab. Upload the project file to Canvas and submit this sheet to your instructor.

Paper For Above instruction

The purpose of this lab is to familiarize students with fundamental data analysis procedures using Minitab, a statistical software widely adopted in engineering and research contexts. This process includes data management, hypothesis testing, normality assessments, and effective utilization of the software's help resources. The initial step involves organizing data files appropriately, creating designated folders on local storage or external drives to ensure secure and systematic data handling.

Once the data file “IE_311_Minitab_Lab.MTW” is downloaded from Canvas and stored correctly, students should open it within Minitab, observing the data and any session messages that might provide insight into proper file loading. Saving the project under a unique name allows for ongoing modifications and captures the current analysis stage. Proper file management ensures reproducibility as students progress through various analyses.

The core of the analysis involves performing a two-sample hypothesis test to compare the means of two datasets labeled MDS1 and MDS2. This test examines whether there is statistically significant evidence to reject the null hypothesis that the two population means are equal, at a significance level of 0.10. Critical assumptions underpinning this test include the normal distribution of data, independence of samples, and equal variances if a pooled t-test is used. Interpreting results involves examining the p-value, confidence intervals, and test statistic to conclude if the null hypothesis can be rejected or not, providing insight into the populations represented by the datasets.

Assessing normality of the datasets SDS and LDS ensures the validity of parametric tests like the t-test. Students select appropriate normality tests such as the Anderson-Darling, Shapiro-Wilk, or Kolmogorov-Smirnov tests based on the class instruction. Conducting these tests, analyzing results, and considering visual diagnostics such as histograms or normal probability plots help determine if the assumption of normality holds. If the data significantly deviate from normality, alternative methods or data transformations may be necessary.

Further knowledge is acquired through consulting the Minitab Stat Guide. By exploring sections on basic statistics and 2-sample t-tests, students develop a deeper understanding of test conditions and assumptions. The guide provides explanations and interpretations, enhancing comprehension of the software’s functionalities and their theoretical underpinnings.

Additional operations include creating new columns, comparing proportions, generating random data, and visualizing distributions using stem-and-leaf plots. Recognizing menu pathways facilitates efficient navigation, essential for effective data analysis workflows. Printing the Session window enables record-keeping and troubleshooting.

The importance of verifying whether the command prompt (MTB >) is visible in the Session window cannot be overstated, as it indicates that Minitab is ready to accept and execute commands, ensuring accurate operation and reproducibility of analyses.

Finally, saving all work, properly closing Minitab, and submitting the project file on Canvas ensures data integrity and facilitates assessment. This structured approach promotes rigorous statistical practice and prepares students for real-world data analysis challenges in engineering contexts.

References

  • Minitab, LLC. (2023). Minitab Statistical Software. Retrieved from https://www.minitab.com
  • Montgomery, D. C. (2019). Design and Analysis of Experiments (9th ed.). John Wiley & Sons.
  • Sheskin, D. J. (2011). Handbook of Parametric and Nonparametric Statistical Procedures (5th ed.). CRC Press.
  • Kesavan, R., & Ramachandran, K. (2014). Norm and Nonparametric Tests with Statistical Power Analysis. Wiley.
  • Field, A. (2018). Discovering Statistics Using R (2nd ed.). Sage Publications.
  • Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors, Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21-33.
  • Ghasemi, A., & Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486-489.
  • Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer.
  • Myers, R. H., Montgomery, D. C., & Vining, G. G. (2012). General Linear Models: Techniques and Applications (3rd ed.). Routledge.
  • Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data. MIT Press.

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