Skip to content

UC_DAVIS · STA · COURSE SHEET

STA 1374 creditsSee offeringsHas prerequisites

Applied Time Series Analysis

CT Score
1500
#0 of 0 in STA
Difficulty
/10
Workload
h/wk
Median grade
from grade distribution
Would take again
n = 0 verified

Grade distribution

No distribution published for this course.

By professor: same course, different game
PROFESSORSCOREGRADEHRS/WKTERMS
Professor ratings →
Sections

No section data for this term yet.

In the tree
STA 108 C- or better; ( MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). STA 137 — Applied Time Series Analysis (4 units) Course Description: Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. Prerequisite(s): STA 108 C- or better; ( MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). This course version is effective from, and including: Fall Quarter 2026. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). STA 137 — Applied Time Series Analysis (4 units) Course Description: Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. Prerequisite(s): STA 108 C- or better; ( MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). This course version is effective from, and including: Fall Quarter 2026STA 137unlocks 0 courses

No mapped courses require this one yet. See on the map →

Study groups
0 active

🔒 Verified-only writes. You post as “sophomore, lecture section”: real enough to trust, anonymous enough to be honest.