Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Administracja Centralna Uczelni - Wymiana międzynarodowa (S1)

Sylabus przedmiotu Statistics and Data Handling for Industry and Research:

Informacje podstawowe

Kierunek studiów Wymiana międzynarodowa
Forma studiów studia stacjonarne Poziom pierwszego stopnia
Tytuł zawodowy absolwenta
Obszary studiów
Profil
Moduł
Przedmiot Statistics and Data Handling for Industry and Research
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Konstrukcji, Mechaniki i Technologii Okrętów
Nauczyciel odpowiedzialny Zbigniew Sekulski <Zbigniew.Sekulski@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 6,0 ECTS (formy) 6,0
Forma zaliczenia zaliczenie Język angielski
Blok obieralny Grupa obieralna

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW2 30 3,00,50zaliczenie
ćwiczenia audytoryjneA2 30 3,00,50zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Students should be comfortable with math at the level of elementary algebra (e.g., equation of a straight line, plotting points, taking powers & roots, percentages).

Cele przedmiotu

KODCel modułu/przedmiotu
C-1Through hands-on experience with real data from a wide variety of applications, students will learn basic methods required for statistical data analysis and interpretation. The emphasis will be on formulating questions, choosing appropriate statistical techniques for a given problem, verifying whether the assumptions behind the techniques are met by the dataset, drawing appropriate conclusions from the analysis, and communicating the results.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
ćwiczenia audytoryjne
T-A-1Histograms; expected values; variance; skewness.2
T-A-2Random number generators.2
T-A-3Illustrating the Central Limit Theorem.4
T-A-42-D random variable; covariance and correlation sample and population estimators.4
T-A-5Interval estimation.4
T-A-6Statistical hypotheses – mean, variance, standard deviation, correlation.4
T-A-7Linear regression.4
T-A-8Analysis of variance (ANOVA).4
T-A-9Evaluation of skills.2
30
wykłady
T-W-1Preliminaries: Statistics and Data Analysis, introduction to combinatorics.2
T-W-2Probabilities – an introduction: chance event (definition, examples), Venn diagrams, axiomatic definition of probability, conditional probability, Bayes theorem.2
T-W-3Random variable and its characteristics: distribution function (probability (density) function; cumulative distribution function), random variable (RV) of the discrete type (examples), random variable of the continuous (examples), basic parameters of a random variable (expected value – the basic idea and formulae moments and central moments, expected value of RV – E(X), variance as the expected value of the square of deviation of the RV from E(X) higher moments: skewness, kurtosis of the RV), descriptive statistics (quantiles, “box-with-whiskers”), two-dimensional random variable, n-dimensional random variable, covariance and correlation; covariance matrix.6
T-W-4Some most popular distributions of random variables (RVs) of the discrete type: one-point (single-valued) RVs, two-point (two-valued) RVs, Bernoulli (binomial) distribution, multinomial distribution, Poisson distribution.2
T-W-5Some most popular distributions of random variables of the continuous type: the holy grail of statistics – the Normal distribution, the exponential distribution (its connection with the Poisson distribution), the 2-d Normal distribution, central limit theorem (examples), Chebyshev inequality and the law of large numbers (practical applications), other important distributions (Student’s t distribution; chi-square distribution; Snedecor’s f distribution; gamma distribution; Weibull distribution), putting statistics into work, statistical sample and statistical population, estimator – the general idea and properties, point estimators – fundamental formulae, the sample arithmetic average (arithmetic mean), the sample mean square deviation from the mean, the sample correlation coefficient (for a 2-d RVs), three sigma rule.6
T-W-6Interval estimation and confidence intervals: confidence intervals for the mean, confidence intervals for the variance, question of the adequate sample size.2
T-W-7Parametric statistical tests – basic ideas: tests concerning the expected value of RV, testing the equality of expected values of 2 RV’s, testing the equality of variances.2
T-W-8Non-parametric statistical tests: type of distribution tests, non-parametric tests – test of independence using the Pearson’s test.2
T-W-9Linear regression (and correlation): method of least squares, the simple regression model, the multiple linear regression – an outline, testing the correation coefficient.2
T-W-10Analysis of variance (ANOVA): the single-factor ANOVA model, confidence intervals for the treatment means, contrasts problems, random effects model.2
T-W-11Knowledge evaluation.2
30

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
ćwiczenia audytoryjne
A-A-1Participation in classes.30
A-A-2Preparation of homeworks.60
90
wykłady
A-W-1Participation in classes.30
A-W-2Own study of the literature.50
A-W-3Preparation to the knowledge evaluation.10
90

Metody nauczania / narzędzia dydaktyczne

dla tego przedmiotu nie są określone metody nauczania ani narzędzia dydaktyczne

Sposoby oceny

dla tego przedmiotu nie są określone sposoby oceny

Zamierzone efekty kształcenia - wiedza

Zamierzone efekty kształceniaOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WTMiT_1-_??_W01
The course gives students fundamentals to statistical analysis.

Zamierzone efekty kształcenia - umiejętności

Zamierzone efekty kształceniaOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WTMiT_1-_??_U01
The ability to use the acquired knowledge to solve practical problems.

Zamierzone efekty kształcenia - inne kompetencje społeczne i personalne

Zamierzone efekty kształceniaOdniesienie do efektów kształcenia dla kierunku studiówOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
WM-WTMiT_1-_??_K01
Improvement of social and personal competencies including self-awareness, self-management, social awareness, relationship skills, responsible decision-making and others. Encouraging dialogue and mutual respect between peoples of different nations, cultures and faiths.

Treści programowe - ćwiczenia audytoryjne

KODTreść programowaGodziny
T-A-1Histograms; expected values; variance; skewness.2
T-A-2Random number generators.2
T-A-3Illustrating the Central Limit Theorem.4
T-A-42-D random variable; covariance and correlation sample and population estimators.4
T-A-5Interval estimation.4
T-A-6Statistical hypotheses – mean, variance, standard deviation, correlation.4
T-A-7Linear regression.4
T-A-8Analysis of variance (ANOVA).4
T-A-9Evaluation of skills.2
30

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Preliminaries: Statistics and Data Analysis, introduction to combinatorics.2
T-W-2Probabilities – an introduction: chance event (definition, examples), Venn diagrams, axiomatic definition of probability, conditional probability, Bayes theorem.2
T-W-3Random variable and its characteristics: distribution function (probability (density) function; cumulative distribution function), random variable (RV) of the discrete type (examples), random variable of the continuous (examples), basic parameters of a random variable (expected value – the basic idea and formulae moments and central moments, expected value of RV – E(X), variance as the expected value of the square of deviation of the RV from E(X) higher moments: skewness, kurtosis of the RV), descriptive statistics (quantiles, “box-with-whiskers”), two-dimensional random variable, n-dimensional random variable, covariance and correlation; covariance matrix.6
T-W-4Some most popular distributions of random variables (RVs) of the discrete type: one-point (single-valued) RVs, two-point (two-valued) RVs, Bernoulli (binomial) distribution, multinomial distribution, Poisson distribution.2
T-W-5Some most popular distributions of random variables of the continuous type: the holy grail of statistics – the Normal distribution, the exponential distribution (its connection with the Poisson distribution), the 2-d Normal distribution, central limit theorem (examples), Chebyshev inequality and the law of large numbers (practical applications), other important distributions (Student’s t distribution; chi-square distribution; Snedecor’s f distribution; gamma distribution; Weibull distribution), putting statistics into work, statistical sample and statistical population, estimator – the general idea and properties, point estimators – fundamental formulae, the sample arithmetic average (arithmetic mean), the sample mean square deviation from the mean, the sample correlation coefficient (for a 2-d RVs), three sigma rule.6
T-W-6Interval estimation and confidence intervals: confidence intervals for the mean, confidence intervals for the variance, question of the adequate sample size.2
T-W-7Parametric statistical tests – basic ideas: tests concerning the expected value of RV, testing the equality of expected values of 2 RV’s, testing the equality of variances.2
T-W-8Non-parametric statistical tests: type of distribution tests, non-parametric tests – test of independence using the Pearson’s test.2
T-W-9Linear regression (and correlation): method of least squares, the simple regression model, the multiple linear regression – an outline, testing the correation coefficient.2
T-W-10Analysis of variance (ANOVA): the single-factor ANOVA model, confidence intervals for the treatment means, contrasts problems, random effects model.2
T-W-11Knowledge evaluation.2
30

Formy aktywności - ćwiczenia audytoryjne

KODForma aktywnościGodziny
A-A-1Participation in classes.30
A-A-2Preparation of homeworks.60
90
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1Participation in classes.30
A-W-2Own study of the literature.50
A-W-3Preparation to the knowledge evaluation.10
90
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty kształceniaWM-WTMiT_1-_??_W01The course gives students fundamentals to statistical analysis.
PoleKODZnaczenie kodu
Zamierzone efekty kształceniaWM-WTMiT_1-_??_U01The ability to use the acquired knowledge to solve practical problems.
PoleKODZnaczenie kodu
Zamierzone efekty kształceniaWM-WTMiT_1-_??_K01Improvement of social and personal competencies including self-awareness, self-management, social awareness, relationship skills, responsible decision-making and others. Encouraging dialogue and mutual respect between peoples of different nations, cultures and faiths.