2026/2027
Статистика I
Статус:
Маго-лего
Кто читает:
Департамент образовательных программ
Где читается:
Институт образования
Когда читается:
2, 3 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Преподаватели:
Юсупова Элен Магомедовна
Язык:
английский
Кредиты:
6
Контактные часы:
58
Course Syllabus
Abstract
In the Statistics I course, the types and properties of variable distributions are discussed, as well as general rules for testing statistical hypotheses, methods of descriptive statistics, correlation coefficients, linear and logistic regression analysis, factorial and cluster analysis, and principal component analysis. The work takes place in the R software. In this course, students will learn how to set research goals and choose appropriate statistical methods for the analysis, implement and interpret quantitative data analysis results, use statistical packages, and work with open datasets. Besides, students will become familiar with current research studies in education and their methodology.
Learning Objectives
- The main goal of the course is to teach students the basics of statistics, the main statistical tools, and how to use to conduct analysis using these tools.
Expected Learning Outcomes
- Classify variables by measurement scale to select, construct, and interpret appropriate descriptive statistics, including measures of central tendency, variation, and contingency tables
- Apply the properties of probability distributions (particularly the normal distribution) to evaluate data normality and standardize variables using Z-scores for cross-distribution comparisons.
- Calculate point estimates and interpret confidence intervals to make inferences about population parameters
- Formulate null and alternative hypotheses for research questions and interpret p-values at appropriate significance levels to make valid statistical decisions
- Select, compute, and test the statistical significance of appropriate correlation coefficients (e.g., Pearson, Spearman) based on data characteristics and measurement scales
- Select and perform appropriate statistical tests (e.g., t-tests, ANOVA, or non-parametric alternatives) to compare group means, and interpret the results, including main effects and post-hoc comparisons
- Fit and estimate linear and logistic regression models (including dummy variables and interaction terms) using statistical software to analyze continuous and categorical outcomes.
- Diagnose appropriate model assumptions (e.g., linearity, residual distribution) and evaluate goodness-of-fit using relevant metrics (e.g., R-squared for linear, classification metrics for logistic).
- Interpret regression coefficients in the context of the research question, using original units for linear models and log-odds, odds ratios, or probabilities for logistic models.
- Critically evaluate and verify AI-generated statistical outputs for regression analyses, identifying limitations, inaccuracies, and methodological inconsistencies.
- Select, perform, and interpret the appropriate dimensionality reduction or latent variable technique (PCA or FA), using eigenvalues, factor loadings, and explained variance to define underlying data structures
- Compare clustering algorithms (hierarchical and k-means) to select the appropriate method, conduct the analysis, and interpret the results to profile homogeneous subgroups within a dataset.
Course Contents
- Introduction to statistics
- Normal distribution
- Introduction to hypothesis testing
- Correlation analysis
- Hypothesis testing for means & Analysis of variance
- Linear Regression
- Logistic regression
- Factor Analysis
- Cluster Analysis
Assessment Elements
- Homework (2nd module)
- Exam, 3rd module
- Homework on Regression (3rd module)
- Exercises
Interim Assessment
- 2026/2027 3rd module0.1 * Exercises + 0.4 * Exam, 3rd module + 0.2 * Homework (2nd module) + 0.2 * Homework on Regression (3rd module)
