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1. 1▶16 Analyzing dichtotomous data IEssential Statistics – Philipp Berens, 2020/21Notes
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2. 2▶15 Linear RegressionEssential Statistics – Philipp Berens, 2020/21Notes
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3. 3▶14 Correlation coefficientEssential Statistics – Philipp Berens, 2020/21Notes
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4. 4▶13 Post hoc testingEssential Statistics – Philipp Berens, 2020/21Notes
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5. 5▶12 ANOVAEssential Statistics – Philipp Berens, 2020/21Notes
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6. 6▶09 WilcoxonEssential Statistics – Philipp Berens, 2020/21Notes
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7. 7▶10 Tests for paired samplesEssential Statistics – Philipp Berens, 2020/21Notes
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8. 8▶07 Confidence IntervalsEssential Statistics – Philipp Berens, 2020/21Notes
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9. 9▶06 Statistical InferenceEssential Statistics – Philipp Berens, 2020/21Notes
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10. 10▶08 Two sample t testEssential Statistics – Philipp Berens, 2020/21Notes
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11. 11▶Mod-01 Lec-04 Clustering vs. ClassificationComputer Science - Pattern RecognitionNotes
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12. 12▶Mod-01 Lec-01 Principles of Pattern Recognition I (Introduction and Uses)Computer Science - Pattern RecognitionNotes
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13. 13▶Mod-01 Lec-02 Principles of Pattern Recognition II (Mathematics)Computer Science - Pattern RecognitionNotes
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14. 14▶Mod-01 Lec-03 Principles of Pattern Recognition III (Classification and Bayes Decision Rule)Computer Science - Pattern RecognitionNotes
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15. 15▶Mod-01 Lec-05 Relevant Basics of Linear Algebra, Vector SpacesComputer Science - Pattern RecognitionNotes
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16. 16▶Mod-01 Lec-06 Eigen Value and Eigen VectorsComputer Science - Pattern RecognitionNotes
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17. 17▶Mod-02 Lec-10 Examples of Bayes Decision RuleComputer Science - Pattern RecognitionNotes
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18. 18▶Mod-02 Lec-09 Types of ErrorsComputer Science - Pattern RecognitionNotes
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19. 19▶Mod-02 Lec-12 Training Set, Test SetComputer Science - Pattern RecognitionNotes
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20. 20▶Mod-01 Lec-07 Vector SpacesComputer Science - Pattern RecognitionNotes
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