Academics
  /  
Courses
  /  
Descriptions
IEMS 303: Statistics


VIEW ALL COURSE TIMES AND SESSIONS

Description

PREREQUISITIES
GEN_ENG 150, 231, 241, and IEMS 302;  or equivalents.

 FOR AY 2026–2027
COMP_SCI 150 or GEN_ENG 150; IEMS 302; 
GEN_ENG 231 not required in Winter  2027 only; is required in Spring 2027;
EA 1 or MATH 240 or GEN_ENG 241


Introduction to the foundations of statistics and statistical computing for data analysis and their applications. Covers descriptive statistics and statistical inference for estimation, testing and prediction. 

  • This course is a major requirement for Industrial Engineering.
  • May not receive credit for both 303 and any of the following:
    • IEMS 201, STAT 320-1, BMD ENG 220, or CHEM ENG 312

LEARNING OBJECTIVES

  • Be able to use the R statistical package to prepare and analyze data
  • Understand estimation, sampling distributions and their properties, including bias and the variance of an estimate
  • Find probabilities involving sample means or totals from both normal and non-normal populations
  • Know when to use, compute, interpret and apply confidence, prediction and tolerance intervals
  • State null and alternative hypotheses, compute and evaluate test statistics, compute P-values, and draw conclusions
  • Estimate simple linear regression models, evaluate whether model assumptions hold with residual and QQ plots, test hypotheses, compute confidence and prediction intervals in R, interpret R-squared

TOPICS

  • Frequency distributions, histograms, measures of center, position and dispersion
  • Distributions of the sample mean, proportion and variance
  • Confidence intervals for means, proportions and variances; prediction and tolerance intervals
  • Single- and two-sample hypothesis tests for means, proportions and variances
  • Simple linear regression: model assumptions, least squares estimates and properties, confidence and prediction intervals, hypothesis tests, and diagnostics
  • Introduction to the multiple linear regression model

MATERIALS
Recommended: Probability and Statistics for Engineering and the Sciences by Jay L. Devoured ISBN 13: 978-1305251809

ADDITIONAL INFORMATION
Examples from manufacturing, medicine, and business