Кто читает:: Международная лаборатория прикладного сетевого анализа

Статус:: Курс адаптационный

Когда читается:: 1-й курс, 1 модуль

Преподаватели

Зайцев Дмитрий Геннадьевич

Лушникова Полина Олеговна

Full Syllabus Ask Question

Abstract

The word statistics scares many students. This course is intended for those students who are not confident in their knowledge and abilities on statistics. The course starts with the most basic concepts about sampling and distributions and ends with more advanced concepts about hypotheses and their testing. These topics will help students in more complex courses in the programme.

Learning Objectives

to give students the opportunity to get acquainted with the basic concepts of statistics
to teach how to use statistical terms correctly and work with basic statistical concepts.

Expected Learning Outcomes

know the difference between different measurement scales
be able to explain the use of different methods in relation to a certain measurement scale
know which charts are suitable for which type of data
be able to formulate null and alternative statistical hypotheses
be able to explain the rejection of statistical hypotheses
be able to estimate the mean and variance of a sample

Course Contents

The Where, Why, and How of Data Collection
Data collection methods. Descriptive and inferential procedures. Sampling methods.
Graphs, Charts, and Tables—Describing Your Data
Frequency distribution. Histograms. Bar charts, pie charts, stem-and-leas diagrams. Line charts, scatter diagrams.
Describing Data Using Numerical Measures
Mean, median, mode. Range, variance, standard deviation. Box and whisker graph. Z- scores.
Introduction to Probability
Approaches to assessing probabilities. Addition rule and Multiplication rule. Conditional probability. Bayes’s Theorem.
Discrete Probability Distributions
The expected value. Binomial distribution. Poisson and hypergeometric distribution.
Introduction to Continuous Probability Distributions
Normal distribution. Normal distribution table. Uniform and exponential distributions.
Introduction to Sampling Distributions
Sampling error. Standard deviation of sampling distribution. Central Limit Theorem.
Estimating Single Population Parameter
Point estimate and confidence interval estimate. Z and t distributions. Sample size.
Introduction to Hypothesis Testing
Null and alternative hypothesis. Type I and Type II errors. Decision rule. Test statistic, critical values, p-value.
Estimation and Hypothesis Testing for Two Population Parameters
Logic of hypothesis testing. Independent population mean. Paired sample.
Hypothesis Tests and Estimation for Population Variances
Hypothesis tests for a single population variance. Chi-square distribution. Test variance difference.
Analysis of Variance
ANOVA. F – statistics.

Assessment Elements

Tests
There will be three tests during the course. They are based on lecture as well as reading materials. Each test is assigned to chapters in the main course book.
Final test
The final test includes 10 multiple choice questions about all topics studied during the course.

Interim Assessment

Interim assessment (1 module)
0.4 * Final test + 0.6 * Tests

Bibliography

Recommended Core Bibliography

Agresti, A. (2017). Statistics: The Art and Science of Learning From Data, Global Edition. Pearson.

Recommended Additional Bibliography

James T. McClave, & Terry Sincich. (2013). Statistics: Pearson New International Edition. Pearson.

Магистерская программа «Аналитика данных и прикладная статистика / Data Analytics and Social Statistics»

Introduction to Statistics