IND 2030 - Applied Statistics for Industrial Engineers

• Describe and utilize basic statistical terminology
• Utilize inverse cumulative distribution functions as part of statistical analysis and random number generation 
• Create and utilize graphical tools to visualize data
• Identify probability distributions
• Draw inferences from data obtained by testing components and systems, using applicable statistical tests
• Clearly and concisely define a problem statement from a practical and statistical perspective.
• Create a coherent hypothesis statement tied to the problem statement
• Statistically analyze and evaluate the data using probability distributions and make a strongly defendable statistical and practical decision 
• Explain findings using learned communication skills, both written and verbal  

• Sound understanding of mathematics and probability

• Minitab or other statistical software (graphical and analytical application)
• Basic sampling theory
• Data types (continuous versus discrete, etc.)
• Measures of central tendency and dispersion
• Discrete probability distributions: binomial and Poisson, normal approximations of
• Introduction to the Central Limit Theorem
• Continuous probability distributions: normal, uniform, gamma/exponential/Weibull, t, chi-square*, and F*
• One-sample hypothesis testing and statistical inference (normal, binomial, and Poisson)
• One-sample confidence intervals and statistical inference (normal and proportion)
• Two-sample confidence intervals and statistical inference
• Two-sample hypothesis testing and statistical inference (normal and proportion)
• Sample size calculations
• One way analysis of variance
• One way chi-squared testing
• Medians hypothesis testing for non-normal data

• A weekly two-hour lab will use defined projects to exercise student skills as defined in the Course Outcomes section