Statistics
2 courses · Shasta College · 2026-2027 Catalog
Classes: Fall 2026
STAT-C1000 Introduction to Statistics
- Units
- 4
- Hours
- 72 lecture total (when offered in the distance education format, hours will total 216)
- 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.
- Formerly
- MATH 14
- Transfers to
- CSU, UC (with credit limitation)
- C-ID
- MATH 110
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 may be offered in a distance education format.
STAT-C1000E Introduction to Statistics
- Units
- 6
- Hours
- 108 lecture 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.
- Formerly
- MATH 14S
- Transfers to
- CSU, UC (with credit limitation)
This course includes an embedded support component for students with minimal mathematical background and technical skills. It covers prerequisite topics and allows for additional instructor contact time. 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. This course may be offered in a distance education format.