- 名称：普林斯顿大学《统计学》公开课【 中英字幕】
- 分类：名校公开课
- 观看人数：加载中...
- 时间：2022-01-17 23:02
- 0.1 Course introduction (14.22)
- 1.1 Experimental research - Example 1 Polio vaccine (11.17)
- 1.2 Experimental research - Example 2 Memory training (16.21)
- 1.3 Experimental research - Example 3 The concept of random (6.27)
- 2.1 Correlational research - Example 1 Personality (9.07)
- 2.2 Correlational research - Example 2 Intelligence (9.09)
- 2.3 Correlational research - Example 3 Sports-related concussion (11.24)
- 3.1 Variables and distributions - Types of variables (14.53)_x264
- 3.2 Variables and distributions - Distributions_Histograms (22_39)_x264
- 3.3 Variables and distributions - Scales of measurement (6.46)_x264
- 4.1 Summary statistics - Measures of central tendency (15.22)_x264
- 4.2 Summary statistics - Measures of variability (17.01)_x264
- 5.1 Correlation - Overview (15.58)_x264
- 5.2 Correlation - Calculation of R (15.29)_x264
- 5.3 Correlation - Assumptions (12.13)_x264
- 6.1 Measurement - Reliability (14.31)
- 6.2 Measurement - Validity (9.50)
- 6.3 Measurement - Sampling (11.03)
- 7.1 Introduction to regression - Overview (19.02)
- 7.2 Introduction to regression - Calculation of regression coefficients (8.22)
- 7.3 Introduction to regression - Assumptions (7.34)
- 8.1 Null Hypothesis Significance Tests (NHST) - Overview (17.05)
- 8.2 Null Hypothesis Significance Tests (NHST) - Problems & remedies (15.55)
- 9.1 Central limit theorem - Sampling distributions (11.51)
- 9.2 Central limit theorem - The central limit theorem (14.16)
- 10.1 Confidence intervals - Confidence intervals for sample means (18.19)
- 10.2 Confidence intervals - Confidence intervals for regression coefficients (10
- 11.1 Multiple regression - Multiple regression (15.28)
- 11.2 Multiple regression - Matrix algebra (14.50)
- 11.3 Multiple regression - Estimation of coefficients (9.34)
- 12.1 Multiple regression continued - The general linear model (7.55)
- 12.2 Multiple regression continued - Dummy coding (12.24)
- 13.1 Moderation - Example 1 (27.13)
- 13.2 Moderation - Centering predictors (15.15)
- 13.3 Moderation - Example 2 (6.44)
- 14.1 Mediation - Standard approach (18.33)
- 14.2 Mediation - Path analysis (14.41)
- 15.1 Group comparisons (t-tests) - Introduction (14.12)
- 15.2 Group comparisons (t-tests) - Dependent t-tests (12.48)
- 15.3 Group comparisons (t-tests) - Independent t-tests (18.50)
- 16.1 Group comparisons (ANOVA) - One-way ANOVA (22.17)
- 16.2 Group comparisons (ANOVA) - Post-hoc tests (7.35)
- 17.1 Factorial ANOVA - Factorial ANOVA (16.37)
- 17.2 Factorial ANOVA - Example (8.43)
- 18.1 Repeated measures ANOVA - Repeated measures_ Pros and cons (21.33)
- 18.2 Repeated measures ANOVA - Repeated measures ANOVA example (11.36)
- 19.1 Chi-square - Chi-square goodness of fit (11.27)
- 19.2 Chi-square - Chi-square test of independence (10.07)
- 20.1 Binary logistic regression - Overview (12.15)
- 20.2 Binary logistic regression - Example (11.57)
- 21.1 Assumptions revisited (correlation and regression) - Assumptions (12.36)
- 21.2 Assumptions revisited (correlation and regression) - Transformations (6.37)
- 22.1 Generalized Linear Model - Parametric vs. non-parametric statistics (10.22)
- 22.2 Generalized Linear Model - Examples (14.28)
- 23.1 Assumptions revisited (t-tests and ANOVA) - Overview (8.32)
- 23.2 Assumptions revisited (t-tests and ANOVA) - Examples (10.21)
- 24.1 Non-parametrics - Research methods and descriptive statistics (5.32)
- 24.2 Non-parametrics - Simple and multiple regression (7.42)
- 24.3 Non-parametrics - Group comparisons t-tests and ANOVA (5.52)
- 24.4 Non-parametrics - Procedures for non-normal distributions and non-linear mo
- 25.1 Lab 1 - Introduction to R (12.03)
- 25.2 Lab 2 - Histograms and summary statistics (37.04)
- 25.3 Lab 3 - Scatterplots and correlations (20.46)
- 25.4 Lab 4 - Regression (28.33)
- 25.5 Lab 5 - Confidence intervals (30.10)
- 25.6 Lab 6 - Multiple regression (21.45)
- 25.7 Lab 7 - Moderation and mediation (15.48)
- 25.8 Lab 8 - Group comparisons (18.18)
- 25.9 Lab 9 - Factorial ANOVA (12.16)
- 25.10 Lab 10 - Binary logistic regression (18_56)
本课程是面向全校文科和理科的本科生开设的一门统计学课程。希望学生通过本课程的学习，能了解统计学的基本思想和方法。本课程的练习题都是真实的不雅观测记录，它们是由任课教师多年收集的来自经济、社会科学、自然科学的近二十个不同学科的社会报告，统计局公告及科学文献记录。此外，学生还在本课程中通过本身的统计实验来验证许多数学规律。
统计学是研究如何搜集和分析数据的一门方法论科学。统计学作为人类认知自然和社会的工具。当代科研工作者应该学会应用统计方法解决他们在各自学科领域和国民经济建设中遇到的实际问题。
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