Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)

Sylabus przedmiotu Statistics:

Informacje podstawowe

Kierunek studiów Wymiana międzynarodowa
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta
Obszary studiów
Profil
Moduł
Przedmiot Statistics
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Technologii Wytwarzania
Nauczyciel odpowiedzialny Marcin Chodźko <Marcin.Chodzko@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 4,0 ECTS (formy) 4,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny Grupa obieralna

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW1 30 2,00,50zaliczenie
laboratoriaL1 30 2,00,50zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Mathematics, basics of probability theory.

Cele przedmiotu

KODCel modułu/przedmiotu
C-1The course introduces the theoretical basis of statistical analysis.
C-2The course will introduce the most common methods and statistical models used in engineering.
C-3The course will familiarize you with the use of popular tools used in computer-aided statistical analyzes.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
laboratoria
T-L-1Solving theoretical tasks in the field of: Desciptive statistics. Inferential Statistics. Distributions.4
T-L-2Solving theoretical tasks in the field of: Bivariate Data. Pearson Correlation.2
T-L-3Solving theoretical tasks in the field of: Binomial distribution. Poisson distribution. Multinomial and hypergeometric distribution.2
T-L-4Solving theoretical tasks in the field of: Normal distribution. Standard normal distribution. Normal aproximation to the Binomial.2
T-L-5Solving theoretical tasks in the field of: Estimation. Degrees of freedom. Characteristics of estimators. Confidence intervals.4
T-L-6Solving theoretical tasks in the field of: Hypothesis testing. Type I and II errors. Steps in hypothesis testing.2
T-L-7Using computer aided tools for solving problems in field of: Hypothesis testing. Type I and II errors. Steps in hypothesis testing.2
T-L-8Using computer aided tools for solving problems in field of: Testing means.Testing between two means (independent and correlated)4
T-L-9Using computer aided tools for solving problems in field of: Regression. Introduction to linear regression. R. Introduction to multiple regression.4
T-L-10Using computer aided tools for solving problems in field of: Analysis of Variance - basics ANOVA4
30
wykłady
T-W-1Introduction. Variables. Desciptive statistics. Inferential Statistics. Distributions.2
T-W-2Graphing distributions. Histograms. Plots, Charts and Graphs.2
T-W-3Central Tendency. Shapes of Distributions. Measures of Variability.2
T-W-4Bivariate Data. Pearson Correlation.2
T-W-5Probability. Binomial distribution. Poisson distribution. Multinomial and hypergeometric distribution.2
T-W-6Normal distribution. Standard normal distribution. Normal aproximation to the Binomial.2
T-W-7Estimation. Degrees of freedom. Characteristics of estimators. Confidence intervals.2
T-W-8Hypothesis testing. Type I and II errors. Steps in hypothesis testing.2
T-W-9Testing means.Testing between two means (independent and correlated)4
T-W-10Regression. Introduction to linear regression. R. Introduction to multiple regression.6
T-W-11Analysis of Variance - basics ANOVA4
30

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
laboratoria
A-L-1Participation in classes30
A-L-2Independent work on tasks20
50
wykłady
A-W-1Participation in classes30
A-W-2Self-reliant exercises.20
50

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1Lecture information using oral presentation and examples.
M-2Group and individual work on problems given by a teacher.
M-3Practicing, statistics problems solving and results discussion.

Sposoby oceny

KODSposób oceny
S-1Ocena formująca: Periodical check-ups of the statistics knowledge by the students in a form of exercising tasks.
S-2Ocena formująca: Transitional evaluation of the state of progress of overall knowledge.
S-3Ocena podsumowująca: The completion of the lecture is based on the attendance list and the verification test.

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WIMiM_1-_WM50_W01
The student can characterize random variables. Explain the methods of estimating the parameters of random variables. Explain the concept of statistical hypothesis and the principles of its verification. Describe ways to estimate the interdependencies between random variables.
C-1, C-2, C-3T-W-1, T-W-2, T-W-3, T-W-4, T-W-5, T-W-6, T-W-7, T-W-8, T-W-9, T-W-10, T-W-11M-1, M-3S-3, S-1, S-2

Zamierzone efekty uczenia się - umiejętności

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WIMiM_1-_WM50_U01
The student is able to develop and interpret the results of experimental research. Choose appropriate statistical tests to verify basic statistical hypotheses and verify them. Calculate the correlation coefficient and estimate the regression relationship.
C-3T-L-1, T-L-2, T-L-3, T-L-4, T-L-5, T-L-6, T-L-7, T-L-8, T-L-9, T-L-10M-2S-2

Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WIMiM_1-_WM50_K01
Student is aware of the need for continuous training in the development and analysis of observed experimental data.
C-3T-L-1, T-L-2, T-L-3, T-L-4, T-L-5, T-L-6, T-L-7, T-L-8, T-L-9, T-L-10M-2S-2

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
WM-WIMiM_1-_WM50_W01
The student can characterize random variables. Explain the methods of estimating the parameters of random variables. Explain the concept of statistical hypothesis and the principles of its verification. Describe ways to estimate the interdependencies between random variables.
2,0
3,0Fluent solving of given problem within the subject
3,5
4,0
4,5
5,0

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
WM-WIMiM_1-_WM50_U01
The student is able to develop and interpret the results of experimental research. Choose appropriate statistical tests to verify basic statistical hypotheses and verify them. Calculate the correlation coefficient and estimate the regression relationship.
2,0
3,0Fluent solving of given problem within the subject
3,5
4,0
4,5
5,0

Kryterium oceny - inne kompetencje społeczne i personalne

Efekt uczenia sięOcenaKryterium oceny
WM-WIMiM_1-_WM50_K01
Student is aware of the need for continuous training in the development and analysis of observed experimental data.
2,0
3,0Fluent solving of given problem within the subject
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. Douglas C. Montgomery, Applied Statistics and Probability for Engineers, A. John Wiley & Sons, Inc., 2003
  2. T.T. Soong, Fundamentals of Probability and Statistics for Engineers, John Wiley & Sons, Inc., 2004

Literatura dodatkowa

  1. Joaquim P. Marques de Sá, Applied Statistics Using SPSS, STATISTICA, MATLAB, Springer, 2007

Treści programowe - laboratoria

KODTreść programowaGodziny
T-L-1Solving theoretical tasks in the field of: Desciptive statistics. Inferential Statistics. Distributions.4
T-L-2Solving theoretical tasks in the field of: Bivariate Data. Pearson Correlation.2
T-L-3Solving theoretical tasks in the field of: Binomial distribution. Poisson distribution. Multinomial and hypergeometric distribution.2
T-L-4Solving theoretical tasks in the field of: Normal distribution. Standard normal distribution. Normal aproximation to the Binomial.2
T-L-5Solving theoretical tasks in the field of: Estimation. Degrees of freedom. Characteristics of estimators. Confidence intervals.4
T-L-6Solving theoretical tasks in the field of: Hypothesis testing. Type I and II errors. Steps in hypothesis testing.2
T-L-7Using computer aided tools for solving problems in field of: Hypothesis testing. Type I and II errors. Steps in hypothesis testing.2
T-L-8Using computer aided tools for solving problems in field of: Testing means.Testing between two means (independent and correlated)4
T-L-9Using computer aided tools for solving problems in field of: Regression. Introduction to linear regression. R. Introduction to multiple regression.4
T-L-10Using computer aided tools for solving problems in field of: Analysis of Variance - basics ANOVA4
30

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Introduction. Variables. Desciptive statistics. Inferential Statistics. Distributions.2
T-W-2Graphing distributions. Histograms. Plots, Charts and Graphs.2
T-W-3Central Tendency. Shapes of Distributions. Measures of Variability.2
T-W-4Bivariate Data. Pearson Correlation.2
T-W-5Probability. Binomial distribution. Poisson distribution. Multinomial and hypergeometric distribution.2
T-W-6Normal distribution. Standard normal distribution. Normal aproximation to the Binomial.2
T-W-7Estimation. Degrees of freedom. Characteristics of estimators. Confidence intervals.2
T-W-8Hypothesis testing. Type I and II errors. Steps in hypothesis testing.2
T-W-9Testing means.Testing between two means (independent and correlated)4
T-W-10Regression. Introduction to linear regression. R. Introduction to multiple regression.6
T-W-11Analysis of Variance - basics ANOVA4
30

Formy aktywności - laboratoria

KODForma aktywnościGodziny
A-L-1Participation in classes30
A-L-2Independent work on tasks20
50
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1Participation in classes30
A-W-2Self-reliant exercises.20
50
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WIMiM_1-_WM50_W01The student can characterize random variables. Explain the methods of estimating the parameters of random variables. Explain the concept of statistical hypothesis and the principles of its verification. Describe ways to estimate the interdependencies between random variables.
Cel przedmiotuC-1The course introduces the theoretical basis of statistical analysis.
C-2The course will introduce the most common methods and statistical models used in engineering.
C-3The course will familiarize you with the use of popular tools used in computer-aided statistical analyzes.
Treści programoweT-W-1Introduction. Variables. Desciptive statistics. Inferential Statistics. Distributions.
T-W-2Graphing distributions. Histograms. Plots, Charts and Graphs.
T-W-3Central Tendency. Shapes of Distributions. Measures of Variability.
T-W-4Bivariate Data. Pearson Correlation.
T-W-5Probability. Binomial distribution. Poisson distribution. Multinomial and hypergeometric distribution.
T-W-6Normal distribution. Standard normal distribution. Normal aproximation to the Binomial.
T-W-7Estimation. Degrees of freedom. Characteristics of estimators. Confidence intervals.
T-W-8Hypothesis testing. Type I and II errors. Steps in hypothesis testing.
T-W-9Testing means.Testing between two means (independent and correlated)
T-W-10Regression. Introduction to linear regression. R. Introduction to multiple regression.
T-W-11Analysis of Variance - basics ANOVA
Metody nauczaniaM-1Lecture information using oral presentation and examples.
M-3Practicing, statistics problems solving and results discussion.
Sposób ocenyS-3Ocena podsumowująca: The completion of the lecture is based on the attendance list and the verification test.
S-1Ocena formująca: Periodical check-ups of the statistics knowledge by the students in a form of exercising tasks.
S-2Ocena formująca: Transitional evaluation of the state of progress of overall knowledge.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Fluent solving of given problem within the subject
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WIMiM_1-_WM50_U01The student is able to develop and interpret the results of experimental research. Choose appropriate statistical tests to verify basic statistical hypotheses and verify them. Calculate the correlation coefficient and estimate the regression relationship.
Cel przedmiotuC-3The course will familiarize you with the use of popular tools used in computer-aided statistical analyzes.
Treści programoweT-L-1Solving theoretical tasks in the field of: Desciptive statistics. Inferential Statistics. Distributions.
T-L-2Solving theoretical tasks in the field of: Bivariate Data. Pearson Correlation.
T-L-3Solving theoretical tasks in the field of: Binomial distribution. Poisson distribution. Multinomial and hypergeometric distribution.
T-L-4Solving theoretical tasks in the field of: Normal distribution. Standard normal distribution. Normal aproximation to the Binomial.
T-L-5Solving theoretical tasks in the field of: Estimation. Degrees of freedom. Characteristics of estimators. Confidence intervals.
T-L-6Solving theoretical tasks in the field of: Hypothesis testing. Type I and II errors. Steps in hypothesis testing.
T-L-7Using computer aided tools for solving problems in field of: Hypothesis testing. Type I and II errors. Steps in hypothesis testing.
T-L-8Using computer aided tools for solving problems in field of: Testing means.Testing between two means (independent and correlated)
T-L-9Using computer aided tools for solving problems in field of: Regression. Introduction to linear regression. R. Introduction to multiple regression.
T-L-10Using computer aided tools for solving problems in field of: Analysis of Variance - basics ANOVA
Metody nauczaniaM-2Group and individual work on problems given by a teacher.
Sposób ocenyS-2Ocena formująca: Transitional evaluation of the state of progress of overall knowledge.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Fluent solving of given problem within the subject
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięWM-WIMiM_1-_WM50_K01Student is aware of the need for continuous training in the development and analysis of observed experimental data.
Cel przedmiotuC-3The course will familiarize you with the use of popular tools used in computer-aided statistical analyzes.
Treści programoweT-L-1Solving theoretical tasks in the field of: Desciptive statistics. Inferential Statistics. Distributions.
T-L-2Solving theoretical tasks in the field of: Bivariate Data. Pearson Correlation.
T-L-3Solving theoretical tasks in the field of: Binomial distribution. Poisson distribution. Multinomial and hypergeometric distribution.
T-L-4Solving theoretical tasks in the field of: Normal distribution. Standard normal distribution. Normal aproximation to the Binomial.
T-L-5Solving theoretical tasks in the field of: Estimation. Degrees of freedom. Characteristics of estimators. Confidence intervals.
T-L-6Solving theoretical tasks in the field of: Hypothesis testing. Type I and II errors. Steps in hypothesis testing.
T-L-7Using computer aided tools for solving problems in field of: Hypothesis testing. Type I and II errors. Steps in hypothesis testing.
T-L-8Using computer aided tools for solving problems in field of: Testing means.Testing between two means (independent and correlated)
T-L-9Using computer aided tools for solving problems in field of: Regression. Introduction to linear regression. R. Introduction to multiple regression.
T-L-10Using computer aided tools for solving problems in field of: Analysis of Variance - basics ANOVA
Metody nauczaniaM-2Group and individual work on problems given by a teacher.
Sposób ocenyS-2Ocena formująca: Transitional evaluation of the state of progress of overall knowledge.
Kryteria ocenyOcenaKryterium oceny
2,0
3,0Fluent solving of given problem within the subject
3,5
4,0
4,5
5,0