Engineering Statistics coded SSCE2193 is a 3-credit compulsory course offered to undergraduate engineering students at UTM.

This course begins with an introduction to statistics in engineering, elementary probability theory and properties of probability distributions. It then covers introduction to sampling distribution, point and interval estimation as well as  hypothesis testing on population parameters such as mean, proportion, standard deviation and variance.  One-way analysis of variance when testing more than two means, goodness-of-fit and independence tests as well as a simple linear regression are also taught in this course.  Students will also be in the use of computer software such as Microsoft Excel using Data Analysis to carry out a practical data analysis. Use of SPSS and R is also lightly exposed to students during course delivery.

Special Discrete Distributions:

  1. Binomial [1] [2] [3] [4] [5] [6]
  2. Poisson [1]
  3. Negative Binomial [1] [2]
  4. Geometri Distribution [1]
  5. Hypergeometric Distribution [1] [2]
  • Sampling distribution of Mean.
  • Sampling distribution of a difference between two Means.
  • Sampling distribution of Proportion.
  • Sampling distribution of a difference between two Proportions.

InEstimation, students are taught on Point and Interval Estimation. Students will learn how to construct confidence interval of the following unknown population parameters:

  • Single population mean.
  • Single population proportion.
  • Single population standard deviation and variance.
  • A difference between two population means.
  • A difference between two population proportions.
  • A ratio of  two population standard deviations and variances.

Hypothesis Tests are performed on the following population parameters:

  • Single population mean.
  • Single population variance.
  • Single population standard deviation and variance.
  • A difference between two population means.
  • A difference between two population proportions.
  • A ratio of  two population standard deviations and variances.
  • Goodness-of-fit test.
  • Independence Test.
  • Homogeneity Test.