# Mathematics CCN (STAT) courses

Palo Verde College · 2026-2027 Catalog · 1 courses

Source: https://catalog.paloverde.edu/index.php?catoid=2

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

### STAT C1000 — Introduction to Statistics

- **Units:** 4
- **Hours:** 72 hours total, 4 hours lecture weekly
- **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.
- **Transfers to:** CSU, UC
- **Offered:** Fall 2026, 4 sections listed as STAT-C1000 (/c/palo-verde-college/schedule/fall-2026/stat.md); Spring 2027, 4 sections listed as STAT-C1000 (/c/palo-verde-college/schedule/spring-2027/stat.md)
- Student Learning Outcomes: Assess how data were collected and recognize how data collection affects what conclusions can be drawn from the data. Identify appropriate graphs and summary statistics for variables and relationships between them and correctly interpret information from graphs and summary statistics. Describe and apply probability concepts and distributions. Demonstrate an understanding of, and ability to use, basic ideas of statistical processes, including hypothesis tests and confidence interval estimation. Identify appropriate statistical techniques and use technology-based statistical analysis to describe, interpret, and communicate results. Evaluate ethical issues in statistical practice.
- General Education: CalGETC: Transfer General Education Requirements ( 2- Mathematical Concepts and Quantitative Reasoning )
- Course Duration (Weeks): 18

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.