ADMT 1805 Applied Statistics

This course is designed to provide students with a foundation in statistical analysis and computing. This class focuses on the concepts, language, and application of statistics to social sciences. In particular, students will learn to produce and interpret descriptive statistics and graphical and numerical representation of information; additionally, students will learn about measures of location, dispersion, position, and dependence, and how to conduct exploratory data analysis. Additional topics will include elementary probability theory (to aid with interpretation and understanding), point and interval estimation, hypothesis significance testing, and linear regression. To help facilitate understanding of the data analysis process, students will routinely apply what they have learned to real data using SPSS. Students will become familiar with SPSS through class lectures, in-class lab exercises, and take-home assignments. SPSS is available to BC students through BC Apps. Real-world data sets will be provided to students for use in coursework.

Course overview

Department
Advancing Studies
School
ADV
Credits
4
Level
Undergraduate
Offered
Periodically in the Fall, Periodically in the Spring, Periodically in the Summer

Course evaluations

Response-weighted averages from Boston College’s official course evaluations (Avalanche/Blue).

Course rating
3.98 / 5 · Very Good
52 responses
Instructor rating
4.12 / 5 · Very Good
52 responses
Terms evaluated
9
Most recent: Spring 2026

Scales: overall ratings run from 1 (poor) to 5 (excellent); agreement questions from 1 (strongly disagree) to 5 (strongly agree); weekly effort from 1 (up to 1 hour) to 5 (10 or more hours).

Evaluations by term and instructor
TermInstructorCourse overallInstructor overallWell organizedClear learning objectivesIntellectually challengingHelpful assignmentsApplicable beyond courseAttendance necessaryWeekly effortResponses
Spring 2026Noman Khanani3.84.34.54.54.34.54.34.34–6 hrs/week4 of 8
Summer 2025Noman Khanani4.64.74.84.84.64.84.64.410+ hrs/week9 of 10
Spring 2025Noman Khanani4.24.34.74.74.34.84.82.54–6 hrs/week6 of 12
Fall 2024Walter Stone4.84.55.05.04.85.04.85.04–6 hrs/week4 of 12
Summer 2024Noman Khanani2.81.53.84.34.83.53.84.710+ hrs/week4 of 6
Spring 2024Noman Khanani3.73.74.34.54.84.54.34.8—6 of 14
Spring 2023Noman Khanani3.64.44.04.14.64.04.33.510+ hrs/week7 of 10
Fall 2022Noman Khanani3.83.84.04.04.84.54.35.04–6 hrs/week4 of 11
Spring 2022Jordan Lawson4.34.64.44.64.84.64.54.97–9 hrs/week8 of 15

Data freshness

Course and evaluation data last updated 2026-10-09. Source details and limitations are documented in Data Sources and Methodology.

Instructors

  • Jordan Lawson
    Spring 2022 · Official rating 4.63/5
  • Noman Khanani
    Spring 2027, Fall 2026, Spring 2026, Summer 2025, Spring 2025, Summer 2024, Spring 2024, Spring 2023, Fall 2022 · Official rating 3.98/5
  • Walter Stone
    Fall 2026, Fall 2024 · Official rating 4.50/5

Sections

  • Section 01
    Spring 2027 · Noman Khanani · Course will meet biweekly - Attend 1st class for more information.;Gasson Hall 206 Tu 06:15PM-08:45PM · Offered
  • Section 01
    Fall 2026 · Noman Khanani · On-line Asynchronous;WCAS Only · Offered
  • Section 02
    Fall 2026 · Walter Stone · Gasson Hall 202 M 06:15PM-07:45PM;WCAS Only · Offered