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Statistics and Information Technologies

Informacje ogólne

Kod przedmiotu: R.2sa.STA.SI.RROAY Kod Erasmus / ISCED: (brak danych) / (brak danych)
Nazwa przedmiotu: Statistics and Information Technologies
Jednostka: Katedra Statystyki i Ekonometrii
Grupy:
Punkty ECTS i inne: (brak)
zobacz reguły punktacji
Język prowadzenia: angielski
Skrócony opis:

Statistics and Information Technologies course takes basic statistical concepts and extends them to topics of special relevance in agricultural studies. The course is intended to provide an insight what is needed to successfully analyze data in behavioural sciences. The aim of the course is to train students to become interdisciplinary collaborators and to reveal the value of statistical thinking in interdisciplinary collaborations.

The unit of Information Technologies has objectives:

•to enable students’ understanding of basic concepts of technology in information processing,

•to make students acquainted with computer software (such as spreadsheets and word processors) installation, application, maintenance and administration.

Pełny opis:

Lectures: 10 h

Classes: 25 h

Labs: 10 h

Lectures:

1. Science, observations, and statistics. Research methods, data structures, measurement, and statistics. Frequency distributions.

2. The concept of sampling from a population. What are samples and populations?

3. Central tendency and the shape of distribution. Descriptive statistics. Interpreting descriptive statistics.

4. Variability and measures of variability.

5. Probability and the normal distribution.

6. Hypothesis tests with t-statistics. Hypothesis testing and statistical significance. Statistical hypothesis testing and statistical significance in science.

7. Comparing a mean to a hypothetical value.

One-sample t test.

The results of a one-sample t test.

8. Introduction to comparing two groups.

Assumption of equal variances.

One- and two-tail P value.

The t-tests for two independent samples. The t-tests for two related samples.

9. Linear relationship. Linear regression model testing. The significance of the regression equation.

10. Measures of relationship. Pearson correlation – interpreting the measure. Hypothesis tests with the Pearson correlation.

Classes: 25 h

1. Frequency distributions. Histograms. Bar graphs.

2. Central tendency and the shape of distribution.

3-4. Measures of central tendency.

Mean.

Median.

Mode.

Selecting a measure of central tendency.

5-6. Variability and measures of variability.

Standard deviation and variance.

Coefficient of variation.

7. Quartiles and range.

Geometric mean.

Skewness and kurtosis.

8. Descriptive Statistics. Interpreting descriptive statistics.

9-10. Standardized distributions.

Z-scores.

Z-scores and location in a distribution.

Z-scores for samples.

11. Probability and the normal distribution.

Normality tests.

12. The distribution of sample means.

Inferential statistics.

13. Standard error of the mean.

The difference between the standard deviation and standard error of the mean.

14-15. The Logic of hypothesis testing.

Uncertainty and errors in hypothesis testing.

Directional hypothesis tests.

Concerns about hypothesis testing: effect size.

Statistical Power.

16. Inferences about means and means differences.

Hypothesis tests with t-statistics.

Testing a population parameter.

Testing a mean.

17. The t-tests for two independent samples.

Assumptions underlying the independent-measures t tests.

18. The t-tests for two related samples.

Assumptions underlying the related-measures t tests.

19. Using sample statistics to estimate population parameters: estimation.

Precision and confidence in estimation.

Point estimate.

Confidence interval.

Comparison of hypothesis tests and estimation.

19-20. Linear relationship.

Linear regression model.

Analysis of regression.

21. Testing the significance of the regression equation.

22. Multiple regression.

23-24. Measures of relationship.

Pearson Correlation.

Interpreting the measure.

Hypothesis tests with the Pearson correlation.

25. Spearman correlation.

Other measures of relationship.

Labs: 10 h

1-2. Word processors.

Creating documents, editing and formatting text, correcting spelling errors, adjusting margins, saving, printing, and opening files.

3. Introduction to work with spreadsheets.

4. The spreadsheets user interface (menus, toolbars, shortcut menu, etc.). Navigating within a worksheet. Selecting ranges.

5. Entering data.

6. Simple formulas (sum, average, autosum, etc.).

7. Relative references vs. absolute references.

8. Worksheet manipulation. Formatting worksheets. Formatting cells. Page setup and printing options.

9. Creating charts. Managing existing charts.

10. Working with multiple worksheets in a workbook. Data linking between worksheets and workbooks.

Importing and exporting data. Retrieving data from a database.

Subject statistic

1. Number of hours and ECTS credits - compulsory subject Hours: 152; ECTS: 6

2. Number of hours and ECTS credits - facultative subject Hours: -; ECTS: -

3. Total number of hours and ECTS credits, a student must earn by direct contact with academics (lectures, classes, seminars....) Hours: 60; ECTS: 3,26

4. Total number of hours and ECTS credits, a student earns in the course of a practical nature, such as laboratory, field trips and design classes Hours: 15; ECTS: 0,81

5. Expected personal workload (without or with academics participation during consultations) necessary for realization of subject objectives. Hours: 92; ECTS: 2,74

Literatura:

Brase Ch.H., Brase C.P, Understandable Statistics: Concepts and Methods, Brooks/Cole, Boston, 2012.

Cohen P., Cohen J., West S.G., Aiken L.S., Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates Inc., New Jersey, 2002.

Gotelli N.J., Ellison A.M., A primer of ecological statistics, Sinauer Associates, Sunderland, 2004.

Johnson R.A., Bhattacharyya G.K., Statistics: Principles and Methods, John Wiley & Sons, New York, 2010.

Rees D. G., Essential statistics, Chapman & Hall, London, 1995.

Efekty kształcenia:

Knowledge

Student can prepare and analyze simple data sets

Student acquires knowledge about basic statistical methods

Student acquires knowledge about basic data processing methods

Skills

Student can prepare data

Student can analyze data

Student can interpret outcome of the analysis

Social competences

Student gains the ability to gather and interpret relevant data

Student can communicate information, ideas, and solutions

Student is conscious of the continuous knowledge enhancing necessity

Przedmiot nie jest oferowany w żadnym z aktualnych cykli dydaktycznych.
Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Rolniczy im. Hugona Kołłątaja w Krakowie.