|
42 | 42 | "cell_type": "markdown", |
43 | 43 | "metadata": {}, |
44 | 44 | "source": [ |
45 | | - "## 1. Probability" |
| 45 | + "## Probability" |
46 | 46 | ] |
47 | 47 | }, |
48 | 48 | { |
|
70 | 70 | }, |
71 | 71 | { |
72 | 72 | "cell_type": "markdown", |
73 | | - "metadata": {}, |
| 73 | + "metadata": { |
| 74 | + "toc-hr-collapsed": false |
| 75 | + }, |
74 | 76 | "source": [ |
75 | | - "## 2. Simulating probabilities" |
| 77 | + "## Simulating probabilities" |
76 | 78 | ] |
77 | 79 | }, |
78 | 80 | { |
|
196 | 198 | "cell_type": "markdown", |
197 | 199 | "metadata": {}, |
198 | 200 | "source": [ |
199 | | - "### Hands-on: more clicking" |
| 201 | + "### Hands-on: clicking" |
200 | 202 | ] |
201 | 203 | }, |
202 | 204 | { |
|
392 | 394 | "cell_type": "markdown", |
393 | 395 | "metadata": {}, |
394 | 396 | "source": [ |
395 | | - "### A proxy for probability\n", |
| 397 | + "### Proportion: A proxy for probability\n", |
396 | 398 | "\n", |
397 | 399 | "As stated above, we have calculated a proportion, not a probability. As a proxy for the probability, we can simulate drawing random samples (with replacement) from the data seeing how many lengths are > 10 and calculating the proportion (commonly referred to as [hacker statistics](https://speakerdeck.com/jakevdp/statistics-for-hackers)):" |
398 | 400 | ] |
|
532 | 534 | "cell_type": "markdown", |
533 | 535 | "metadata": {}, |
534 | 536 | "source": [ |
535 | | - "## Hands-on" |
| 537 | + "### Hands-on: Probabilities" |
536 | 538 | ] |
537 | 539 | }, |
538 | 540 | { |
|
647 | 649 | "cell_type": "markdown", |
648 | 650 | "metadata": {}, |
649 | 651 | "source": [ |
650 | | - "### Empirical cumulative distribution functions (ECDFs)" |
| 652 | + "## Empirical cumulative distribution functions (ECDFs)" |
651 | 653 | ] |
652 | 654 | }, |
653 | 655 | { |
|
699 | 701 | "cell_type": "markdown", |
700 | 702 | "metadata": {}, |
701 | 703 | "source": [ |
702 | | - "## Hands-on" |
| 704 | + "### Hands-on: Plotting ECDFs" |
703 | 705 | ] |
704 | 706 | }, |
705 | 707 | { |
|
739 | 741 | "cell_type": "markdown", |
740 | 742 | "metadata": {}, |
741 | 743 | "source": [ |
742 | | - "## 3. PROBABILITY DISTRIBUTIONS AND THEIR STORIES" |
| 744 | + "## Probability distributions and their stories" |
743 | 745 | ] |
744 | 746 | }, |
745 | 747 | { |
|
846 | 848 | "cell_type": "markdown", |
847 | 849 | "metadata": {}, |
848 | 850 | "source": [ |
849 | | - "## Hands-on" |
| 851 | + "#### Hands-on: Poisson" |
850 | 852 | ] |
851 | 853 | }, |
852 | 854 | { |
|
886 | 888 | "cell_type": "markdown", |
887 | 889 | "metadata": {}, |
888 | 890 | "source": [ |
889 | | - "## Example Poisson distribution: field goals attempted per game" |
| 891 | + "#### Example Poisson distribution: field goals attempted per game" |
890 | 892 | ] |
891 | 893 | }, |
892 | 894 | { |
|
940 | 942 | "cell_type": "markdown", |
941 | 943 | "metadata": {}, |
942 | 944 | "source": [ |
943 | | - "## HANDS ON" |
| 945 | + "#### Hands-on: Simulating Data Generating Stories" |
944 | 946 | ] |
945 | 947 | }, |
946 | 948 | { |
|
1025 | 1027 | "cell_type": "markdown", |
1026 | 1028 | "metadata": {}, |
1027 | 1029 | "source": [ |
1028 | | - "## Exponential distribution" |
| 1030 | + "### Exponential distribution" |
1029 | 1031 | ] |
1030 | 1032 | }, |
1031 | 1033 | { |
|
1172 | 1174 | "cell_type": "markdown", |
1173 | 1175 | "metadata": {}, |
1174 | 1176 | "source": [ |
1175 | | - "## HANDS ON" |
| 1177 | + "#### Hands-on: Simulating Normal" |
1176 | 1178 | ] |
1177 | 1179 | }, |
1178 | 1180 | { |
|
1237 | 1239 | "name": "python", |
1238 | 1240 | "nbconvert_exporter": "python", |
1239 | 1241 | "pygments_lexer": "ipython3", |
1240 | | - "version": "3.7.3" |
1241 | | - } |
| 1242 | + "version": "3.7.2" |
| 1243 | + }, |
| 1244 | + "toc-autonumbering": true |
1242 | 1245 | }, |
1243 | 1246 | "nbformat": 4, |
1244 | 1247 | "nbformat_minor": 2 |
|
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