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MTHE 367 Engineering Data Analysis Units: 3.50
Exploratory data analysis -- graphical and statistical analysis and presentation
of experimental data. Random sampling. Probability and probability models
for discrete and continuous random variables. Process capability. Normal probability graphs. Sampling distribution of means and proportions. Statistical Quality Control and Statistical Process Control. Estimation using confidence intervals. Testing of hypothesis procedures for means, variances and
proportions -- one and two samples cases. Liner regression, residuals and correlation. ANOVA. Use of statistical software.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0.5)
of experimental data. Random sampling. Probability and probability models
for discrete and continuous random variables. Process capability. Normal probability graphs. Sampling distribution of means and proportions. Statistical Quality Control and Statistical Process Control. Estimation using confidence intervals. Testing of hypothesis procedures for means, variances and
proportions -- one and two samples cases. Liner regression, residuals and correlation. ANOVA. Use of statistical software.
NOT OFFERED 2023-2024
(Lec: 3, Lab: 0, Tut: 0.5)
Requirements: Prerequisites: APSC 171, APSC 172
Corequisites:
Exclusions: STAT 261, STAT 263, STAT 266, STAT 267
Offering Term: W
CEAB Units:
Mathematics 31
Natural Sciences 0
Complementary Studies 0
Engineering Science 11
Engineering Design 0
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Performing calculations for confidence intervals and hypothesis tests in various situations involving one, two, and three or more samples, including simple linear regression and one-way analysis of variance.
- Computing values of random variables or probabilities associated with random variables.
- Conversion of physical problem into appropriate statistical model.
- Solving statistics problems using probability or Poisson or Normal or Exponential distributions or simple linear regression or analysis of variance.
- Recognizing the design of experiments performed and/or performing appropriate statistical analysis to solve engineering problems.
- Analysis of data using statistical tools and derivation of suitable conclusions from empirical data.