# Statistics (STAT) courses

Cuesta College · 2026-2027 Catalog · 2 courses

Source: https://cuesta.curriqunet.com/catalog/iq/29980/29981/32584

"Offered" lists sections in this catalog year's class schedules: Summer 2026, Fall 2026.

### STAT 147S — STATISTICS SUPPORT

- **Units:** 1
- **Hours:** 1 hours per week: (1.00 lecture hours/0.00 lab hours/0.00 activity hours)
- **Corequisite:** STAT C1000
- **Offered:** Fall 2026, 2 sections (/c/cuesta-college/schedule/fall-2026/stat.md)
- Credit - Degree Applicable
- P/NP Only

Provides just-in-time instruction and review of core prerequisite skills, competencies, and concepts necessary for success in STAT C1000, Introduction to Statistics. Includes topics from prealgebra, elementary and intermediate algebra, and the development of critical thinking skills needed for statistical analysis. Intended for students who are concurrently enrolled in STAT C1000.

### STAT C1000 — INTRODUCTION TO STATISTICS

- **Units:** 4
- **Hours:** 4 hours per week: (4.00 lecture hours/0.00 lab hours/0.00 activity hours)
- **Prerequisite:** Placement as determined by the college’s multiple measures assessment process or completion of a course taught at or above the level of intermediate algebra.
- **C-ID:** MATH 110
- **Transfers to:** CSU, UC
- **Offered:** Summer 2026, 6 sections listed as STAT 247 (/c/cuesta-college/schedule/summer-2026/stat.md); Fall 2026, 20 sections listed as STAT 247 (/c/cuesta-college/schedule/fall-2026/stat.md)
- Credit - Degree Applicable
- Letter Grade Only

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines.