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# Introduction to Statistics

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс адаптационный
Когда читается:
1-й курс, 1 модуль

#### Преподаватели

Лушникова Полина Олеговна

### Course Syllabus

#### 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

#### Recommended Core Bibliography

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