STAT250 APPLIED STATISTICS

Course Code:2460250
METU Credit (Theoretical-Laboratory hours/week):5 (4.00 - 2.00)
ECTS Credit:6.0
Department:Statistics
Language of Instruction:English
Level of Study:Undergraduate
Course Coordinator:
Offered Semester:Spring Semesters.

Course Objectives

Describe the goals of various statistical methodologies conceptually. Apply statistical techniques in the context of everyday life and further studies in different disciplines. Understand different sampling strategies. Use descriptive statistics and graphical methods to summarize data accurately. Use inferential statistics to make valid judgments based on the data available. Select the appropriate course tools to analyze a particular problem. Upon completion of this course, students should be able to think critically about data and apply standard statistical inference procedures to draw conclusions from such analyses.


Course Content

Sampling distributions. Sample drawing techniques. Estimation and testing for one or two population characteristics. Maximum likelihood estimation of parameters. Measures of association. Simple and multiple regression. Introduction to design of experiments, analysis of variance; one-way, multiway classifications. Multiple comparisons. Basic nonparametric procedures. Elementary time series analysis; trends, seasonality, forecasting. Indexing. Some applications in medicine, science, engineering and social sciences.


Course Learning Outcomes

At the end of this course, students are expected to:

  • think critically about data and apply standard statistical inference procedures to draw conclusions from such analyses
  • compelete a project with real dataset.

 


Program Outcomes Matrix

Level of Contribution
#Program Outcomes0123
1Applying the knowledge of statistics, mathematics and computer to statistical problems and developing analytical solutions.
2Defining, modeling and solving real life problems that involve uncertainty, and interpreting results.
3To decide on the data collection technique, and apply it through experiment, observation, questionnaire or simulation.
4Analysing small and big volumes of data and interpreting results.
5Utilizing up-to-date techniques, computer hardware and software required for statistical applications; developing software programs and numerical solutions for specific problems when necessary.
6Taking part in intradisciplinary and interdisciplinary teamwork, using time efficiently, taking leadership responsibilities and being entrepreneurial.
7Taking responsibility in individual work and offering authentic solutions.
8Following contemporary developments and publications in statistical science, conducting research, being open to novelty and thinking critically.
9Efficiently communicating in Turkish and English to define and analyze statistical problems and to interpret the results.
10Having a professional and ethical sense of responsibility.
11Developing computational solutions to statistical problems that cannot be solved analytically.
12Having theoretical background and developing new theories in statistics, building relations between theoretical and practical knowledge.
13Serving the society with the expertise in the field.

0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution