MICRO-110: Probability & statistics for engineers

Lectures in this course (44)

Explores statistical dependence using covariance to determine the relationship between variables.

Introduces comparison statistics, error rates, hypothesis testing, and real-life examples of treatment effectiveness and weightlifting analysis.

Explores model building using analysis of means to predict removal rates and adhesion strength.

Explores replicates, ANOVA, and factor elimination in full factorial designs using ANOM tables and model equations.

Explores model building using analysis of means for predicting factors' effects and interactions.

Covers the Pearson correlation, relationship direction, form, strength, and regression model assessment.

Explains the least-squares regression line, correlation coefficients, outliers, influential points, and residuals in regression models.

Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.

Covers the basics of statistical hypothesis testing, p-values, confidence levels, and t-tests.

Covers the basics of t tests, including hypothesis testing, significance levels, P-values, Z-tests, and the t distribution.

Covers introductory statistics, engineering cycle, Wheatstone bridge, and mechanical watch tuning.

Covers statistics basics, engineering roles, and development cycles with practical examples.

Explores sampling, inferential statistics, and effective experimentation in statistics.

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

Covers the design of a catapult experiment and higher order equations.

Explores fractional factorial designs, from types to analysis and model building.

Explores exploiting redundancies, confounding effects, and analyzing fractional factorials in experimental designs.

Covers Fractional Factorial Designs, types, examples, balanced designs, and exploiting redundancies in interactions.

Discusses experimental design strategies, factors, responses, treatment combinations, and degrees of freedom.

Covers the basics of full factorial design, coding, graphical outputs, and data analysis.

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