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Design experimentu a statistika AGA47E
Summer 2015 | Regular Academic Session | Czech University of Life Sciences| Exchange


Lecture Hours: Po: 7:00 - 08:20 @SIC
Lab Session: Po: 8:45 - 10-15 @AII | Po: 10:30 - 12:00 @AII | Ut: 10:30 - 12-00 @AII | Ut: 12:15 - 13:45 @AII
Office Hours: Ut: 09:00 - 10:30   |   (or by appointment)   |   @ A211


Recommended Text (Books):
  • Kaps, M. and Lamberson, W. (2004). Biostatistics for Animal Science. CABI Publishing. ISBN: 9781845935405.

  • Rasch, D., Verdooren, L.R. and Gowers, J.I. (1999). Fundamentals in the Design and Analysis of Experiments and Surveys. Wisseshaftsverlag GmbH. Oldenbourg. ISBN: 3486249665.

  • Hayter, A. (2013). Probability and Statistics for Engineers and Scientists. Custom Edition, Duxbury, ISNBN: 978-1111827045.

  • Andel, J. (2007). Statisticke Metody. Matfyzpress Praha. ISBN 80-86732-08-8 (3. vydani).


  • Course description:
    Experimental Design and Statistics (AGA47A) is an introductory statistics course for graduate students. The emphasis is on data description and standard evaluation, basic inference and simple model building skills. Students will also have an opportunity to work with a variety of data sets using some common statistical and evaluation tools provided in R (Team Development Core 2014).

    Course outline:
    The course outline is also available at Moodle (University Information System).

    Syllabus:
  • Introduction to Probability and Statistics | (Lecture 1 | PDF transparences)
  • Probability and Random Variables | (Lecture 2 | PDF transparences)
  • Discrete and Continuous Probability Distributions | (Lecture 3, Lecture 4 | PDF transparences)
  • Inferences on a Population Mean and Comparing Two Mean | (Lecture 5 | PDF transparences)
  • Statistical Tests (one sample, two samples, multiple samples) | (Lecture 6, Lecture 7 | PDF transparences)
  • Analysis of Variance (one-way, two-way) | (Lecture 8 | PDF transparences)
  • Simple Linear Regression and Correlation | (Lecture 9 | PDF transparences)
  • Other Regression Techniques, Nonparametric tests | (Lecture 10 | PDF transparences)
  • Design of Experiments| (Lecture 11 | PDF transparences)
  • Review

  • Course notes (PDF transparences):   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10
    Lab sessions (PDF files):   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | . . .
    Homework assignments (PDF files):   1 | 2 | 3

    Data files for homework assignments:   datasetHW1.csv | passengerData2.csv
    Data files for lab sessions:   Ldata1.csv | Ldata2.csv | Ldata3.csv | passengerData.csv | passengerData2.csv
    Instructions, Data files and R script for the final project:   Zadani a instrukce | finalProjectData2015.RData | generator.r


    Statistical Software:   For all lab sessions students are required to bring their own laptops with the statistical R Software installed on it. The R software is a GNU General Public Licence software supporting all three main platforms (Windows, Macintosh, Linux). The software can be downloaded from http://cran.rstudio.com. An installation instruction manual is provided on the given website.


    Exams:
    • Final Exam: will be announced
    • Lab Exam: will be announced
    • Final Project: instructions


    Grading:
    The attendance for both (lectures as well as lab sessions) is obligatory for all students. All students of the course will be subjected to a common grading procedure. At the term end there will be a record of each student’s raw grades for his/her attendance, homework assignments, final project and written exams. Based on these raw grades and the assigned weights to each component, firstly a lab credit and a term overall percentage w ill be computed for each student. If a student receives no credit or less than 60% of the points from the all components counted together they will have to re-sit the written part of the exam and to take an oral part of the exam to explain and understand mistakes made during the exam.
    Students are also allowed, even encouraged to ask for an oral exam if they have an impression they could perform better than the overall score assigned by the grading assessment. However, they have to present a good knowledge of all topics covered in the class during the oral exam.
    For the Lab credit student needs to obtain at least one third of all points assigned for the lab sessions (Homework Assignments, Lab Exam and Final Project).


    Grading Assessment:
    • Credit Assesment:
      • 12 points for four homework assignments
      • 10 points for the final project
      • 10 points for the lab exam
      • 8 points for the student's attendance
      • To get a lab credit each student needs to get at least 21 out of 40 points.
    • Final Course Grade:
      • Final exam: weight 70 %
      • Studnet's overall performance: weight 20 %
      • Student's attendance during the lectures: weight 10 %
      • To get a final score each student needs to get at least 60 % out of 100 %.

    There will be four homework assignments in the course. More information regarding these homeworks will be posed later.

    Academic Integrity and Honesty:
    “The Czech University of Life Sciences in Prague is committed to the standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to avoid any behavior which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offense.”


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