Collection Development Policies:
Statistics
Purpose: To support instruction through the Master's level and
faculty research in statistics. The Program in Statistics is an interdisciplinary, intercollegiate
program supported by the Division of Sciences and the College of Agricultural, Human, and Natural Resource Sciences. Many faculty in the Program share appointments in departments in these areas
as well as in the College of Business. Consequently, collecting is done primarily in the
sciences but also in the social sciences and medicine.
General Collection Guidelines:
- Languages:
English is the primary language of collection with some material in German.
- Chronological Guidelines:
Research interest is primarily in the twentieth century.
- Geographical Guidelines:
Not applicable.
- Treatment of the Subject:
Lower-division textbooks generally are not purchased. Upper-division textbooks and
popular works are acquired selectively. Emphasis is on graduate level texts and
research material.
- Types of Material:
Acquisitions are primarily in the form of monographs and periodicals, but also
include proceedings/ transactions of conferences, reference works, technical
reports, and government documents.
- Date of Publication:
Emphasis is on the acquisition of current imprints. In the case of non-current
publications there will be no preference given to original prints or editions as
opposed to reprints.
Observations and Qualifications by Subject with
Collection Level:
Probability Theory and Distributions:
C(1)
Sampling Distributions:
C(1)
Estimation:
D / B
Hypothesis Testing:
C(1) / B
Association and Dependence:
D / B
Regression Analysis:
C(1) / B
Analysis of Variance:
C(1) / B
Sampling:
B
Experimental Design:
B
Theory of Stochastic Processes:
C(1)
Inference for Stochastic Processes:
C(1)
Operations Research:
D / C(1)
Applications:
B
Geostatistics, physics and chemistry; biostatistics and biomathematics; statistics
in agriculture; medical and epidemiological statistics; demography, population
models, ecology and environmental statistics, econometrics.
Teaching and Training Methods:
D / C(1)
Time Series:
B
Multivariate Analysis:
C(1) / B
Statistical Computing:
C(1) / B
Data Analysis: B
Applied Probability: D / B
Linear Models: D / B
Joel Cummings
Spring 2004