Zał. nr 4 do ZW 33/2012
FACULTY OF ARCHITECTURE
COURSE SYLLABUS
STATISTICS:
Course title in English Statistics
Academic major:
SPATIAL MANAGEMENT
Study cycle and study mode:
First cycle, full-time
Course type:
obligatory
Kod przedmiotu
GPA002307W, GPA002307W
Group of courses
No
Number of contact hours
Number of student workload
hours
Grading policy
Mark (X) for final course in a
group of courses
ECTS points:
including ECTS points for
practical hours (P)
including ECTS points for
contact hours (CH)
Lecture
30
90
Tutorial
30
60
Lab
Project
Seminar
Examination pass with
grade
3
2
2
2
2
PREREQUISITES RELATING TO KNOWLEDGE, SKILLS AND OTHER COMPETENCIES
No prerequisites.
COURSE OBJECTIVES
C1: revision of basics of probability calculus, probability distributions and their usefulness for the description of
phenomena in spatial management
C2: basic statistics and the possibility of its application in the analysis of the phenomena related to spatial
planning
COURSE LEARNING OUTCOMES
Related to knowledge:
PEK_W01 identify the basics of descriptive statistics for the general population sample and the empirical
distributions
PEK_W02 identify bases of probability calculus
PEK_W03 Demonstrate knowledge of theoretical probability distributions and their applications
PEK_W04 identify the basic concepts of statistics
PEK_W05 Understand what statistical inference consists in
PEK_W06 identify basics of correlation analysis and regression
Related to skills:
PEK_U01 Demonstrate the ability to apply the concepts of probability theory for solving problems
PEK_U02 choose and use the probability distributions of random variables for calculating the probabilities of
events
PEK_U03 select and apply appropriate statistical tool for research
PEK_U04 Demonstrate the ability to interpret results
Related to social competencies:
PEK_K01 understand the need of self-education
PEK_K02 Demonstrate the ability to work independently and in a group when collecting, processing and
analyzing data
CURRICULUM CONTENT
Mode of teaching - lectures
Lec1
Lec2
Lec3
Lec4
Lec5
Lec6
Lec7
Lec8
Lec9
Lec10
Lec11
Lec12
Lec13
Lec14
Lec15
Mass phenomena. General population, sample , sampling techniques. Descriptive
statistics. Moments of trial and their functions.
Graphic presentation of results. Histogram. Characteristics of the empirical
distribution.
Random events and operations on them. Basic concepts of probability theory.
Application of combinatorics to calculate probability. Conditional probability.
Independent events.
Random variables - discreet, constant. The distribution of the random variable density and distribution function. Parameters of the distribution of the random variable
(the expected value, variance, standard deviation).
Bernoulli diagram and binomial distribution. Geometric distribution. Poisson
distribution.
Limit theorems. The distributions of continuous random variable: rectangular
distribution, normal distribution (right three sigma), the exponential distribution.
Probability distributions vs. different phenomena in the developed space. Basic
concepts in statistics.
Theory of estimation. Testing statistical hypotheses. Parametric tests Tests of
average.
Nonparametric tests - a test of conformity 2, λ - Kolmogorov.
Variance analysis and its applications. F - Snedecor test.
Analysis of relations - correlation and regression analysis. The coefficient of
correlation and its testing
Linear regression, testing the significance of coefficients.
Curvilinear regression. Examples of use
Multi-factorial analysis.
Total hours
Mode of teaching - tutorials
Tut1
Tut2
Tut3
Number
hours
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
30
Number of
hours
Building statistical rows based on raw data. Using the Excel spreadsheet. Graphical
2
illustration of results.
Creating a histogram. Calculation of the characteristics for the various forms of
2
empirical distributions.
Space of elementary events. Calculation of probability. Conditional probability.
2
Tut4
Tut5
Tut6
Tut7
Tut8
Tut9
Tut10
Tut11
Tut12
Tut13
Tut14
Tut15
Application of combinatorics to calculate probability.
Selected distributions of the random variable displacement: the distribution of zeroone scheme of Bernoulli, binomial distribution.
Geometric distribution, Poisson distribution.
Rectangular distribution. Normal distribution.
Test. Tasks for the calculation of probabilities in the empirical and theoretical
distributions.
Distribution tables. Reading tables and using Excel. Parametric hypothesis testing average.
Parametric tests - cont
Compliance test .
Calculating the coefficient of correlation and testing it with Excela.
Rectilinear regression.
Test. Rectilinear regression - cont
Regression analysis - continued
Total hours
2
2
2
2
2
2
2
2
2
2
2
2
30
TEACHING TOOLS
N1. Traditional lecture using computer presentations and examples of use op software to solve certain problems
N2. Arithmetic exercises : solving problems from the list
ASSESSMENT OF ACHIEVEMENT OF LEARNING OUTCOMES
Lecture
Assessment (F – formative Number of learning
Method of assessing the achievement of learning
(during the semester), S –
outcome
outcome
summative (at the end of
semester)
p
PEK_W01-W06
Written exam
PEK_U01-U04
Tutorial
Assessment (F – formative Number of learning
Method of assessing the achievement of learning
(during the semester), S –
outcome
outcome
summative (at the end of
semester)
F1, F2
PEK_U01-U04
Written tests
PEK_K01-K02
P = average for tests
BASIC AND ADDITIONAL LITERATURE
BASIC LITERATURE:
[1]
[2]
[3]
[4]
[5]
[6]
S.BRANDT, Analiza danych. Metody statystyczne i obliczeniowe, Warszawa 2002
J.GREŃ, Statystyka matematyczna modele i zadania, Warszawa 1975
Z.HELLWIG, Elementy rachunku prawdopodobieństwa i statystyki, Warszawa 1970
W.STARZYŃSKA, Statystyka praktyczna, Warszawa 2000, s.186-210
S.GREGORY, Statistical Methods and the Geographer, London 1964, s.1-208
M.PIŁATOWSKA, Repetytorium ze statystyki , Warszawa PWN 2006
ADDITIONAL LITERATURE:
[1]
M.FISZ, Rachunek prawdopodobieństwa i statystyka matematyczna, Warszawa 1967
COURSE ADVISOR (NAME, SURNAME, E-MAIL)
Elżbieta Litwińska, elzbieta.litwinska@pwr.wroc.pl
EQUIVALENCY MATRIX OF LEARNING OUTCOMES FOR COURSE
STATISTICS:
WITH THE LEARNING OUTCOMES FOR THE SPATIAL MANAGEMENT MAJOR
Course learning
outcome
Relation of course outcome with
learning outcomes formulated
for the major
Course
objectives
PEK_W01
(knowledge)
PEK_W02
K_W01, KW_02
C1
K_W01, KW_02
C1
PEK_W03
K_W01, KW_02
C1
PEK_W04
PEK_W05
PEK_W06
K_W01, KW_02
K_W01, KW_02
K_W01, KW_02
C2
C2
C2
PEK_U01 (skills)
K_U02
C1
PEK_U02
K_U02
C1
PEK_U03
PEK_U04
PEK_K01
(competencies)
PEK_K02
Curriculum content
K_U02, K_U08, K_U25
K_U02, K_U08, K_U25
K_K03
C2
C2
C1, C2
Lec1, Lec2,
Tut1, Tut2
Lec3, Lec4,
Tut3, Tut4
Lec5-7,
Tut5-8
Lec8-15, Tut9-15
Lec9-10, Tut9-11
Lec12-14,
Tut12-14
Lec1-4,
Tut1-4
Lec5-7,
Tut5-8
Lec9-15, Tut9-15
Lec9-15, Tut9-15
Lec9-15, Tut9-15
K_K02
C1, C2
Lec1-15, Tut1-15
No. of
teaching tool
N1, N2
N1, N2
N1, N2
N1, N2
N1, N2
N1, N2
N1, N2
N1, N2
N1, N2
N1, N2
N1, N2
N1, N2