|
75 | 75 | "})"
|
76 | 76 | ]
|
77 | 77 | },
|
| 78 | + { |
| 79 | + "cell_type": "markdown", |
| 80 | + "metadata": {}, |
| 81 | + "source": [ |
| 82 | + "Figure 11.1" |
| 83 | + ] |
| 84 | + }, |
78 | 85 | {
|
79 | 86 | "cell_type": "code",
|
80 | 87 | "execution_count": 5,
|
|
101 | 108 | "ax.set_ylabel('Y', fontsize=14);"
|
102 | 109 | ]
|
103 | 110 | },
|
| 111 | + { |
| 112 | + "cell_type": "markdown", |
| 113 | + "metadata": {}, |
| 114 | + "source": [ |
| 115 | + "Summary of Y values grouped by A" |
| 116 | + ] |
| 117 | + }, |
104 | 118 | {
|
105 | 119 | "cell_type": "code",
|
106 | 120 | "execution_count": 6,
|
|
225 | 239 | "})"
|
226 | 240 | ]
|
227 | 241 | },
|
| 242 | + { |
| 243 | + "cell_type": "markdown", |
| 244 | + "metadata": {}, |
| 245 | + "source": [ |
| 246 | + "Figure 11.2" |
| 247 | + ] |
| 248 | + }, |
228 | 249 | {
|
229 | 250 | "cell_type": "code",
|
230 | 251 | "execution_count": 8,
|
|
251 | 272 | "ax.set_ylabel('Y', fontsize=14);"
|
252 | 273 | ]
|
253 | 274 | },
|
| 275 | + { |
| 276 | + "cell_type": "markdown", |
| 277 | + "metadata": {}, |
| 278 | + "source": [ |
| 279 | + "Summary of Y values grouped by A" |
| 280 | + ] |
| 281 | + }, |
254 | 282 | {
|
255 | 283 | "cell_type": "code",
|
256 | 284 | "execution_count": 9,
|
|
378 | 406 | "cell_type": "markdown",
|
379 | 407 | "metadata": {},
|
380 | 408 | "source": [
|
381 |
| - "## Section 11.2" |
382 |
| - ] |
383 |
| - }, |
384 |
| - { |
385 |
| - "cell_type": "markdown", |
386 |
| - "metadata": {}, |
387 |
| - "source": [ |
388 |
| - "### Program 11.2" |
| 409 | + "\"Finally, suppose that treatment $A$ is a variable representing the dose of treatment in mg/day, and that it takes integer values from 0 to 100 mg.\"" |
389 | 410 | ]
|
390 | 411 | },
|
391 | 412 | {
|
|
423 | 444 | "df3 = pd.DataFrame({'A': A, 'Y': Y, 'constant': np.ones(16)})"
|
424 | 445 | ]
|
425 | 446 | },
|
| 447 | + { |
| 448 | + "cell_type": "markdown", |
| 449 | + "metadata": {}, |
| 450 | + "source": [ |
| 451 | + "Figure 11.3" |
| 452 | + ] |
| 453 | + }, |
426 | 454 | {
|
427 | 455 | "cell_type": "code",
|
428 | 456 | "execution_count": 12,
|
|
449 | 477 | "ax.set_ylabel('Y', fontsize=14);"
|
450 | 478 | ]
|
451 | 479 | },
|
| 480 | + { |
| 481 | + "cell_type": "markdown", |
| 482 | + "metadata": {}, |
| 483 | + "source": [ |
| 484 | + "## Section 11.2" |
| 485 | + ] |
| 486 | + }, |
| 487 | + { |
| 488 | + "cell_type": "markdown", |
| 489 | + "metadata": {}, |
| 490 | + "source": [ |
| 491 | + "### Program 11.2" |
| 492 | + ] |
| 493 | + }, |
452 | 494 | {
|
453 | 495 | "cell_type": "markdown",
|
454 | 496 | "metadata": {},
|
|
525 | 567 | "cell_type": "markdown",
|
526 | 568 | "metadata": {},
|
527 | 569 | "source": [
|
528 |
| - "We can also compute this with a statistics package, like Statsmodels, which will also give us confidence intervals and other values" |
| 570 | + "We can also compute this with a statistics package, like Statsmodels, which will give us confidence intervals and other values" |
529 | 571 | ]
|
530 | 572 | },
|
531 | 573 | {
|
|
687 | 729 | "pred.summary_frame().round(2)"
|
688 | 730 | ]
|
689 | 731 | },
|
| 732 | + { |
| 733 | + "cell_type": "markdown", |
| 734 | + "metadata": {}, |
| 735 | + "source": [ |
| 736 | + "Figure 11.4" |
| 737 | + ] |
| 738 | + }, |
690 | 739 | {
|
691 | 740 | "cell_type": "code",
|
692 | 741 | "execution_count": 21,
|
|
885 | 934 | "summary.tables[1]"
|
886 | 935 | ]
|
887 | 936 | },
|
| 937 | + { |
| 938 | + "cell_type": "markdown", |
| 939 | + "metadata": {}, |
| 940 | + "source": [ |
| 941 | + "<br>\n", |
| 942 | + "Figure 11.5" |
| 943 | + ] |
| 944 | + }, |
888 | 945 | {
|
889 | 946 | "cell_type": "code",
|
890 | 947 | "execution_count": 29,
|
|
1002 | 1059 | "name": "python",
|
1003 | 1060 | "nbconvert_exporter": "python",
|
1004 | 1061 | "pygments_lexer": "ipython3",
|
1005 |
| - "version": "3.7.5" |
| 1062 | + "version": "3.7.3" |
1006 | 1063 | }
|
1007 | 1064 | },
|
1008 | 1065 | "nbformat": 4,
|
|
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