# Statistics (STAT) courses

Victor Valley College · 2026-2027 Catalog · 2 courses

Source: https://catalog.vvc.edu/course-descriptions/stat/

"Offered" lists sections in this catalog year's class schedules: Summer 2026, Fall 2026, Winter 2027, Spring 2027.

### STAT C1000 — Introduction to Statistics

- **Units:** 4
- **Hours:** 72 lecture hours
- **Corequisite:** Prerequisites: 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: If HS GPA is greater than or equal to 2.3, direct placement is into STAT-C1000 If HS GPA is less than 2.3, the placement is STAT-C1000E (embedded support)
- **Transfers to:** CSU, UC
- **Offered:** Summer 2026, 5 sections (/c/victor-valley-college/schedule/summer-2026/stat.md); Fall 2026, 15 sections (/c/victor-valley-college/schedule/fall-2026/stat.md); Winter 2027, 2 sections (/c/victor-valley-college/schedule/winter-2027/stat.md); Spring 2027, 18 sections (/c/victor-valley-college/schedule/spring-2027/stat.md)

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. Formerly MATH-120. .

### STAT C1000E — Introduction to Statistics

- **Units:** 6
- **Hours:** 108 lecture hours
- **Transfers to:** CSU, UC
- **Offered:** not on the Summer 2026, Fall 2026, Winter 2027, Spring 2027 schedules

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. This course has embedded support. Formerly MATH-120+80. (UC credit limitation) .