- 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)
本課程是面向全校文科和理科的本科生開設(shè)的一門統(tǒng)計(jì)學(xué)課程。希望學(xué)生通過本課程的學(xué)習(xí)，能了解統(tǒng)計(jì)學(xué)的基本思想和方法。本課程的練習(xí)題都是真實(shí)的觀測(cè)記錄，它們是由任課教師多年收集的來自經(jīng)濟(jì)、社會(huì)科學(xué)、自然科學(xué)的近二十個(gè)不同學(xué)科的社會(huì)報(bào)告，統(tǒng)計(jì)局公告及科學(xué)文獻(xiàn)記錄。此外，學(xué)生還在本課程中通過自己的統(tǒng)計(jì)實(shí)驗(yàn)來驗(yàn)證許多數(shù)學(xué)規(guī)律。
統(tǒng)計(jì)學(xué)是研究如何搜集和分析數(shù)據(jù)的一門方法論科學(xué)。統(tǒng)計(jì)學(xué)作為人類認(rèn)知自然和社會(huì)的工具。當(dāng)代科研工作者應(yīng)該學(xué)會(huì)應(yīng)用統(tǒng)計(jì)方法解決他們?cè)诟髯詫W(xué)科領(lǐng)域和國民經(jīng)濟(jì)建設(shè)中遇到的實(shí)際問題。
