Statystyka 1755-F12-STAT-J

(po angielsku) 1. Obligatory hours realized with the teacher participation: – lecture participation – 12 hours – laboratories participation – 23 hours, – consultations participation, including scientific and research consultations 3 hours, – final exam participation – 2 hours Total obligatory hours realized with the teacher participation: 40 hours, which corresponds to 1.60 ECTS point. 2. Student workload balance: – lecture participation – 12 hours, – laboratories participation – 23 hours, – consultations participation, including scientific and research consultations – 5 hours, – preparation for laboratories – 35 hours, – preparation for tests and final exam – 25 hours, A total work amount: 100 hours, which corresponds to 4 ECTS point. 3. Workload related to conducting research: – participation in classes (including research results and scientific studies in the field of statistics) - 15 hours, – participation in scientific consultations - 5 hours, – preparation for laboratories including scientific results: 15 hours, – preparation for final exam including research results and scientific studies in the field of statistics - 5 hours. A total student workload related to the conducted research is 40 hours, which corresponds to 1.60 ECTS point. 4. Time required for the preparation and participation in evaluating process: – - preparation for test – 10 hours, – - preparation for final exam – 10hours, Total time required for the preparation and participation in evaluating process: 20 hours, which corresponds to 0.8 ECTS point. 5. Time required for the practical training completion – not applicable.

(po angielsku) Student: W1: knows and understands the definition of random variable and probability - K_B.W25 W2: knows and understands probability distributions of the discrete random variable - K_B.W25 W3: knows moments and central moments of probability distribution of a discrete random variable - K_B.W25 W4: knows cumulative distribution function for the discrete random variable- K_B.W25 W5: knows function of probability density for the continuous random variable- K_B.W25 W6: knows and understands probability distributions of the continuous random variable and has basic knowledge of typical probability distributions- K_B.W25 W7: possesses knowledge about confidence interval- K_B.W25 W8: knows the definition of statistical hypothesis and understands the idea of hypothesis testing- K_B.W26 W9: knows measures of linear correlation and methods of computing parameters of the linear regression-K_B.W25

(po angielsku) Student: U1: is able to calculate probability of random variables-K_B.U11 U2: is able to determine moments and cumulative distribution function for basic probability distributions-K_B.U11 U3: is able to compute moments estimators for a statistical sample- K_B.U11 U4: is able to apply statistical tools (Statistica, SPSS, SAS, R)- K_B.U12 U5: is able to determine confidence interval for t-Student distribution K_B.U11 U6: is able to put research statistical hypothesis K_F.U2 U7: is able to compute parameters of the linear regression- K_B.U11

(po angielsku) Student: K1: understands the need for self education and enlarging knowledge-K2

(po angielsku) Lectures: Expository teaching methods – informative (conventional) lecture, participatory lecture, problem-based lecture Laboratories: Exploratory teaching methods – practical problem solving using professional statistical software Expository teaching methods – discussion, description Observation/demonstration teaching methods

- wykład informacyjny (konwencjonalny)

przedmiot obligatoryjny

(po angielsku) A student starting education in the subject of Statistics should have knowledge of mathematics at the high school level

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