# Mathematics (STAT) courses

Solano Community College · 2025-2026 Catalog · 2 courses

Source: https://solano.elumenapp.com/catalog/catalog2025-2026/department/mathematics

"Offered" lists sections in this catalog year's class schedules: Spring 2026.

### STAT 311 — Introduction to Statistics Support

- **Units:** 0.5
- **Hours:** 2 hours lab weekly, 36 contact hours total
- **Corequisite:** STAT C1000 (Introduction to Statistics)
- **Offered:** Spring 2026, 3 sections (/c/solano-community-college/schedule/spring-2026/stat.md)
- Pass/No Pass only
- Not transferable

A review of the core prerequisite skills, competencies, and concepts needed in statistics. Intended for students who are concurrently enrolled in STAT C1000, Introduction to Statistics. Topics include concepts from arithmetic, pre-algebra, elementary and intermediate algebra, and descriptive statistics that are needed to understand the basics of college-level statistics. Concepts are taught through the context of descriptive data analysis. Additional emphasis is placed on solving and graphing linear equations and modeling with linear functions. Enrollment in the course may occur as a result of the recommendation of a counselor or math instructor based on a Multiple Measures Evaluation.

### STAT C1000 — Introduction to Statistics

- **Units:** 4
- **Hours:** 4 hours lecture weekly, 72 contact hours total
- **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:** Spring 2026, 19 sections listed as STAT 1000 (/c/solano-community-college/schedule/spring-2026/stat.md)
- General Education: SCC: Area 2; Cal-GETC: Area 2

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.