|
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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