Lectures in course MICRO-110: Probability & statistics for engineers

Review Session: Module 1

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods

Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.

Hypothesis Testing: T-Test Methodology

Explores hypothesis testing methodology using t-tests and the randomization test.

Statistical Measures: Mean, Median, and Dispersion Techniques

Discusses statistical measures of central tendency and dispersion, focusing on mean, median, and their implications in data analysis.

Randomization tests: Understanding Experimental Results

Explores randomization tests as an alternative to t-tests for experimental analysis, using fake data to assess treatment effectiveness.

T-tests and Empirical Tests

Explores t-tests, z-tests, and empirical tests for sample comparison and hypothesis testing.

Central Tendency and Dispersion

Explores replicates, visualization methods, central tendency measures, outliers, dispersion, averages, residuals, and unbiased estimators.

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Thermodynamics: Energy, Work, and Chemical Reactions

Covers thermodynamics, focusing on energy, work, and their roles in chemical reactions.

Experimental Design: Balloon Example

Covers experimental design using a balloon example to illustrate key concepts in factorial experiments and data analysis.

Graphical Analysis of Full Factorials

Explores the graphical analysis of full factorial experiments, emphasizing efficient data analysis and interpretation of main effects and interactions.

Design of Experiments: Statistical Design and Analysis

Covers statistical design of experiments, focusing on minimizing costs and maximizing accuracy.

Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Normal Distributions

Covers the characteristics and importance of the normal distribution in statistical analysis.

Normal Distribution: Characteristics and Z-scores

Explores normal distribution characteristics, Z-scores, probability in inferential statistics, sample effects, and binomial distribution approximation.

Binomial Distributions

Covers the normal distribution, inferential statistics, probability, and the binomial distribution in the context of the 'Dishonest Gambler Problem'.

Statistical Tests: T-Tests and ANOVA

Covers the calculation of paired t-tests, advantages/disadvantages of different t-tests, and the concept of ANOVA.

Analysis of Variance: ANOVA

Covers Analysis of Variance (ANOVA) to compare means of multiple groups efficiently.

Paired Tests: Shoe Sole Experiment

Explores paired tests using a shoe sole experiment, showing significant differences between materials A and B.

Linear Independence: Covariance and Statistical Dependence

Explores covariance, statistical dependence, education-fertility relationship, hypothesis testing, and comparison statistics for continuous outcomes.