# Public Health 145 - Spring 2012

- Institution: University of California, Berkeley
- Teacher: Maureen LAHIFF
- Year: 2012
- Level: Undergraduate
- Language: English
- Subjects: Mathematics, Medicine and health
- Source: [Internet Archive](https://archive.org/details/ucberkeley_webcast_itunesu_496302014)
- Licence: [CC BY-NC-ND 3.0](http://creativecommons.org/licenses/by-nc-nd/3.0/) (Non-commercial use only, and no changed versions.)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

PB HLTH 145 LEC 001 Sp12

Statistical Analysis of Continuous Outcome Data

## Lectures

1. [Lecture 1: course overview - intro to regression](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797), 49 min (Audio)
2. [Lecture 2: confidence intervals and tests in regression](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-2), 51 min (Audio)
3. [Lecture 3: residuals and assumption checking](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-3), 49 min (Audio)
4. [Lecture 4: assumption checking - residuals and the normal curve](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-4), 51 min (Audio)
5. [Lecture 5: using fitted values Y-hat; exponential growth](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-5), 50 min (Audio)
6. [Lecture 6: a quadratic model; intro to multiple regression](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-6), 51 min (Audio)
7. [Lecture 7: theory for one X; using multiple regression coefficients](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-7), 50 min (Audio)
8. [Lecture 8: patient satisfaction example; t tests; circle diagrams](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-8), 49 min (Audio)
9. [Lecture 9: sums of squares, R2; restricted vs full F tests, overall F test](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-9), 51 min (Audio)
10. [Lecture 10: Agresti and Finlay crime data](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-10), 51 min (Audio)
11. [Lecture 11: liver function data; categorical Xs; different slopes model](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-11), 51 min (Audio)
12. [Lecture 12: parallel lines model; identical lines mode](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-12), 47 min (Audio)
13. [Lecture 13: follow-up group comparisons](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-13), 49 min (Audio)
14. [Lecture 14: Pearson correlation correlation coefficient r; Fisher's z](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-14), 48 min (Audio)
15. [Lecture 15: partial correlation, partial R2](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-15), 52 min (Audio)
16. [Lecture 16: functional form: lowess plots, splines; robust variace estimates](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-16), 51 min (Audio)
17. [Lecture 17: outliers and influential points; adjusted R2; F tests in terms of R2](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-17), 50 min (Audio)
18. [Lecture 18: model building strategies; stepwise model selection](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-18), 52 min (Audio)
19. [Lecture 19: leaps and bounds algorithm; final cautions about regression](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-19), 52 min (Audio)
20. [Lecture 20: overview of anova; one-way fixed effects anova](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-20), 49 min (Audio)
21. [Lecture 21: anova assumptions; F tests; Tukey multiple comparisons](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-21), 53 min (Audio)
22. [Lecture 22: anova using regression](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-22), 51 min (Audio)
23. [Lecture 23: tests comparing medians for skewed distributions](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-23), 53 min (Audio)
24. [Lecture 24: two-way fixed effects anova; interacction plots](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-24), 49 min (Audio)
25. [Lecture 25: F tests for interaction; assumption checking for two-way anova](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-25), 52 min (Audio)
26. [Lecture 26: more on interaction; Bonferroni-Dunn multiple comparisons](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-26), 52 min (Audio)
27. [Lecture 27: nutritive value of fruit example](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-27), 50 min (Audio)
28. [Lecture 28: sums of squares from regression and from model terms](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-28), 49 min (Audio)
29. [Lecture 29: no interaction: main effects and follow-up comparisons](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-29), 49 min (Audio)
30. [Lecture 30: one-way random effects model; clinics example](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-30), 47 min (Audio)
31. [Lecture 31: maximum likelihood estimation](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-31), 49 min (Audio)
32. [Lecture 32: fitting random effects models with xtreg and xtmixed](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-32), 52 min (Audio)
33. [Lecture 33: introduction to repeated measures](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-33), 51 min (Audio)
34. [Lecture 34: models with one -within subjects- factor correlation structures](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-34), 48 min (Audio)
35. [Lecture 35: fitting repeated measures models; likelihood ratio and Wald tests](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-35), 50 min (Audio)
36. [Lecture 36: multiple comparisons for repeated measures designs; adjusted P values](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-36), 51 min (Audio)
37. [Lecture 37: anova for repeated measures](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-37), 46 min (Audio)
38. [Lecture 38: one within and one between subjects factors](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-38), 48 min (Audio)
39. [Lecture 39: calcium treatment vs. control example; jaw growth example](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-39), 49 min (Audio)
40. [Lecture 40: consolidation for random effects and repeated measures models](https://invisiblenetwork.ai/learn/ia/ucberkeley_webcast_itunesu_496302014/e567a5085797-40), 38 min (Audio)