Go to Main Content

Purdue Self-Service

 

HELP | EXIT

Catalog Entries

 

Fall 2019
May 08, 2021
Transparent Image
Information Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course. The Schedule Type links will be available only when the schedule of classes is available for the selected term.

STAT 35000 - Introduction To Statistics
Credit Hours: 3.00. A data-oriented introduction to the fundamental concepts and methods of applied statistics. Exploratory analysis of data. Sample design and experimental design. Probability distributions and simulation. Sampling distributions. The reasoning of statistical inference. Confidence intervals and tests for one and two samples. Inference for contingency tables, regression, and correlation. Introduction to regression with several explanatory variables. Essential use is made of statistical software throughout. Intended primarily for students majoring in the mathematical sciences. For statistics majors and minors, credit should be allowed in no more than one of STAT 30100, 35000, 50100, and in no more than one of STAT 50300 and STAT 51100. Prerequisite: two semesters of college calculus. Typically offered Fall Spring.
0.000 OR 3.000 Credit hours

Syllabus Available
Levels: Graduate, Professional, Undergraduate
Schedule Types: Distance Learning, Individual Study, Lecture, Practice Study Observation, Recitation
All Sections for this Course

Offered By: College of Science
Department: Statistics

Course Attributes:
Upper Division

May be offered at any of the following campuses:     
      IUPUI
      Northwest- Westville
      Northwest- Hammond
      West Lafayette

Learning Outcomes: 1. Understand basic terms, graphs, and symbols and be able to interpret statistics in the media. 2. Understand and be able to explain statistical processes and be able to fully interpret statistical results. 3. Understand why and how statistical investigations are conducted and the "big ideas" that underlie statistical investigations. 4. Be able to use a statistical package (R or SAS) to analyze data and interperate result. 5. Big Ideas in Statistics: variability, distributions, and models; causation vs correlation; practical significance vs statistical significance, etc.



Return to Previous New Search XML Extract
Transparent Image
Skip to top of page
Release: 8.7.2.4