Spaces:
Running
Running
File size: 27,326 Bytes
5585962 af87f7a 5585962 af87f7a 5585962 af87f7a 5585962 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 | <!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Analytics Reasoning Companion - User Guide</title>
<style>
:root {
--primary-color: #2c3e50;
--secondary-color: #3498db;
--accent-color: #e74c3c;
--background-color: #f9f9f9;
--text-color: #333;
--border-color: #ddd;
--highlight-bg: #fff3cd;
}
* {
box-sizing: border-box;
}
body {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
line-height: 1.7;
color: var(--text-color);
max-width: 900px;
margin: 0 auto;
padding: 20px 40px;
background-color: var(--background-color);
}
h1 {
color: var(--primary-color);
border-bottom: 3px solid var(--secondary-color);
padding-bottom: 15px;
margin-top: 40px;
}
h2 {
color: var(--primary-color);
border-bottom: 2px solid var(--border-color);
padding-bottom: 10px;
margin-top: 50px;
}
h3 {
color: var(--secondary-color);
margin-top: 30px;
}
h4 {
color: var(--primary-color);
margin-top: 25px;
}
.subtitle {
color: #666;
font-size: 1.3em;
margin-top: -10px;
margin-bottom: 30px;
}
table {
width: 100%;
border-collapse: collapse;
margin: 20px 0;
background: white;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);
}
th, td {
padding: 12px 15px;
text-align: left;
border: 1px solid var(--border-color);
}
th {
background-color: var(--primary-color);
color: white;
font-weight: 600;
}
tr:nth-child(even) {
background-color: #f8f9fa;
}
tr:hover {
background-color: #e8f4f8;
}
blockquote {
border-left: 4px solid var(--secondary-color);
margin: 25px 0;
padding: 15px 25px;
background-color: #f0f7fb;
font-style: italic;
}
blockquote p {
margin: 0;
}
.callout {
background-color: var(--highlight-bg);
border: 1px solid #ffc107;
border-radius: 5px;
padding: 15px 20px;
margin: 20px 0;
}
.callout-blue {
background-color: #d1ecf1;
border-color: #bee5eb;
}
.callout-green {
background-color: #d4edda;
border-color: #c3e6cb;
}
.callout-red {
background-color: #f8d7da;
border-color: #f5c6cb;
}
code {
background-color: #f4f4f4;
padding: 2px 6px;
border-radius: 3px;
font-family: 'Consolas', 'Monaco', monospace;
}
.example-prompt {
background-color: #e8f5e9;
border-left: 4px solid #4caf50;
padding: 15px 20px;
margin: 15px 0;
font-style: italic;
}
.stage-box {
background: white;
border: 1px solid var(--border-color);
border-radius: 8px;
padding: 20px 25px;
margin: 25px 0;
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
}
.stage-box h3 {
margin-top: 0;
color: var(--primary-color);
}
.stage-number {
display: inline-block;
background-color: var(--secondary-color);
color: white;
width: 30px;
height: 30px;
border-radius: 50%;
text-align: center;
line-height: 30px;
margin-right: 10px;
font-weight: bold;
}
ul, ol {
margin: 15px 0;
padding-left: 25px;
}
li {
margin: 8px 0;
}
.two-column {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 20px;
margin: 20px 0;
}
.column {
background: white;
padding: 20px;
border-radius: 8px;
border: 1px solid var(--border-color);
}
.column h4 {
margin-top: 0;
}
.do-column {
border-left: 4px solid #4caf50;
}
.dont-column {
border-left: 4px solid var(--accent-color);
}
.faq-item {
background: white;
border: 1px solid var(--border-color);
border-radius: 8px;
padding: 20px;
margin: 15px 0;
}
.faq-item h4 {
color: var(--secondary-color);
margin-top: 0;
margin-bottom: 10px;
}
.final-reminder {
background: linear-gradient(135deg, var(--primary-color), var(--secondary-color));
color: white;
padding: 30px;
border-radius: 10px;
margin: 40px 0;
text-align: center;
}
.final-reminder blockquote {
background: rgba(255,255,255,0.1);
border-left-color: white;
color: white;
}
hr {
border: none;
border-top: 1px solid var(--border-color);
margin: 40px 0;
}
.header-section {
text-align: center;
padding: 40px 0;
border-bottom: 2px solid var(--border-color);
margin-bottom: 40px;
}
.header-section h1 {
border: none;
margin: 0;
font-size: 2.5em;
}
footer {
text-align: center;
padding: 30px;
color: #666;
border-top: 1px solid var(--border-color);
margin-top: 50px;
}
@media (max-width: 768px) {
body {
padding: 15px 20px;
}
.two-column {
grid-template-columns: 1fr;
}
table {
font-size: 0.9em;
}
th, td {
padding: 8px 10px;
}
}
</style>
</head>
<body>
<div class="header-section">
<h1>Analytics Reasoning Companion</h1>
<p class="subtitle">User Guide for Learners</p>
</div>
<h2>What Is the Analytics Reasoning Companion?</h2>
<p>The Analytics Reasoning Companion is an AI-powered learning partner designed to accompany you as you work through the <em>Analytics for Managers</em> book. Unlike typical AI tools that give you answers, this companion is designed to <strong>develop your analytical thinking skills</strong> by guiding you through a disciplined reasoning process.</p>
<h3>What It Is</h3>
<ul>
<li><strong>A learning partner</strong> that guides your reasoning through structured stages</li>
<li><strong>A mentor</strong> that helps you develop judgment about when to trust (and distrust) analytical results</li>
<li><strong>A patient teacher</strong> focused on <em>how</em> to think, not <em>what</em> to think</li>
<li><strong>A Socratic questioner</strong> that helps you test and strengthen your conclusions</li>
</ul>
<h3>What It Is NOT</h3>
<ul>
<li><strong>Not a calculator</strong> — it won't run models or produce numerical outputs for you</li>
<li><strong>Not a coding assistant</strong> — it won't write Python, R, or Excel formulas</li>
<li><strong>Not a decision-maker</strong> — it won't tell you what business action to take</li>
<li><strong>Not an answer machine</strong> — it won't give you "the right answer" to check against</li>
</ul>
<blockquote>
<p><strong>The Goal:</strong> By the end of each session, you should have <em>less</em> confidence in "the model says" and <em>more</em> confidence in your own analytical judgment.</p>
</blockquote>
<hr>
<h2>Getting Started</h2>
<h3>Step 1: Access the Companion</h3>
<p>Visit the companion at: <strong>[Link to be provided]</strong></p>
<h3>Step 2: Choose Your Chapter</h3>
<p>When you start a conversation, the companion will ask which chapter you're working on:</p>
<ul>
<li><strong>Regression (Chapter 2)</strong> — Understanding associations with continuous outcomes</li>
<li><strong>Classification (Chapter 3)</strong> — Predicting categorical outcomes and managing decision trade-offs</li>
<li><strong>Clustering (Chapter 4)</strong> — Discovering patterns and segments without labeled outcomes</li>
</ul>
<h3>Step 3: Choose Your Mode</h3>
<p>The companion operates in two modes:</p>
<table>
<thead>
<tr>
<th>Mode</th>
<th>When to Use</th>
<th>What to Expect</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Example Mode</strong></td>
<td>Working through the chapter walkthrough</td>
<td>Heavy guidance, detailed explanations, the companion models good reasoning for you</td>
</tr>
<tr>
<td><strong>Exercise Mode</strong></td>
<td>Practicing independently after completing the chapter</td>
<td>Light guidance, Socratic questioning, the companion probes your reasoning and challenges weak conclusions</td>
</tr>
</tbody>
</table>
<p>Simply tell the companion: <em>"I'm working on the regression chapter example"</em> or <em>"I'm ready for the classification exercise."</em></p>
<hr>
<h2>The 7-Stage Analytical Workflow</h2>
<p>Every analysis follows seven sequential stages. The companion will guide you through each one in order. <strong>You cannot skip stages</strong> — this structure exists to build disciplined habits.</p>
<div class="stage-box">
<h3><span class="stage-number">1</span> Business Understanding</h3>
<p><strong>Purpose:</strong> Anchor the analysis in a real business decision.</p>
<p><strong>What happens:</strong> The companion asks you to articulate:</p>
<ul>
<li>What decision is at stake?</li>
<li>What outcome do you care about?</li>
<li>How will analysis inform action?</li>
</ul>
<p><strong>What to expect:</strong> No technical language yet. This stage is purely about the business problem.</p>
<div class="example-prompt">
<strong>Example prompt from companion:</strong><br>
"Before we look at any data, let's be clear about the decision context. What business question are you trying to answer, and what action might change based on what you learn?"
</div>
</div>
<div class="stage-box">
<h3><span class="stage-number">2</span> Analytical Question Formulation</h3>
<p><strong>Purpose:</strong> Translate the business question into an analytical formulation.</p>
<p><strong>What happens:</strong> You identify:</p>
<ul>
<li>The outcome variable (what you're trying to explain or predict)</li>
<li>The unit of analysis (what does one row of data represent?)</li>
<li>Whether your goal is explanation or prediction</li>
</ul>
<div class="example-prompt">
<strong>Example prompt from companion:</strong><br>
"Is your goal to explain patterns in historical data, or to predict outcomes for new cases? This distinction will affect how we interpret results later."
</div>
</div>
<div class="stage-box">
<h3><span class="stage-number">3</span> Data Understanding</h3>
<p><strong>Purpose:</strong> Build intuition about what the data can and cannot tell you.</p>
<p><strong>What happens:</strong> You examine:</p>
<ul>
<li>Dataset structure (rows, columns, variable types)</li>
<li>Data quality issues (missing values, outliers, skew)</li>
<li>Potential limitations and biases</li>
</ul>
<div class="callout callout-blue">
<strong>Key question the companion will ask:</strong><br>
"Who might be excluded from this dataset? Could those excluded be systematically different from those included?"
</div>
</div>
<div class="stage-box">
<h3><span class="stage-number">4</span> Data Preparation (Conceptual)</h3>
<p><strong>Purpose:</strong> Understand <em>why</em> preparation decisions matter.</p>
<p><strong>What happens:</strong> You discuss:</p>
<ul>
<li>How missing values and outliers affect results</li>
<li>Why different preparation choices lead to different stories</li>
<li>The trade-offs between simplicity and robustness</li>
</ul>
<p><strong>What to expect:</strong> Conceptual discussion, not coding. The companion explains implications, not implementation.</p>
</div>
<div class="stage-box">
<h3><span class="stage-number">5</span> Modeling</h3>
<p><strong>Purpose:</strong> Interpret model outputs as evidence, not truth.</p>
<p><strong>What happens:</strong> The companion presents results and guides you to:</p>
<ul>
<li>Interpret coefficients/predictions in business terms</li>
<li>Understand direction and magnitude of associations</li>
<li>Recognize that results are <strong>associative, not causal</strong></li>
</ul>
<div class="callout callout-red">
<strong>Critical reminder the companion will give:</strong><br>
"These results describe associations observed in this dataset. They do not prove that changing one variable will cause the outcome to change."
</div>
</div>
<div class="stage-box">
<h3><span class="stage-number">6</span> Diagnostics and Refinement</h3>
<p><strong>Purpose:</strong> Build healthy skepticism about model reliability.</p>
<p><strong>What happens:</strong> You examine:</p>
<ul>
<li>What diagnostics check (and what they don't)</li>
<li>Potential weaknesses in the model</li>
<li>Whether results are driven by a small subset of observations</li>
</ul>
<p><strong>What to expect:</strong> The companion frames diagnostics as "risk indicators," not "approval stamps." Passing diagnostics doesn't mean the interpretation is safe.</p>
<div class="example-prompt">
<strong>Example prompt from companion:</strong><br>
"The diagnostics look statistically reasonable, but that doesn't guarantee the interpretation is actionable. What kinds of mistakes could still occur?"
</div>
</div>
<div class="stage-box">
<h3><span class="stage-number">7</span> Interpretation, Reporting, and Action</h3>
<p><strong>Purpose:</strong> Translate results into responsible business conclusions.</p>
<p><strong>What happens:</strong> You synthesize:</p>
<ul>
<li>Key findings in conditional language</li>
<li>Limitations and uncertainties</li>
<li>What questions remain unanswered</li>
<li>What additional evidence would increase confidence</li>
</ul>
<div class="example-prompt">
<strong>Closing question the companion will ask:</strong><br>
"If you had to summarize this analysis in one cautious sentence to a decision-maker, what would you say?"
</div>
</div>
<hr>
<h2>Chapter-Specific Guidance</h2>
<h3>Regression (Chapter 2)</h3>
<p><strong>Key concepts:</strong> Coefficients describe average associations, holding other variables constant.</p>
<p><strong>Common traps to watch for:</strong></p>
<ul>
<li>Treating coefficients as "levers" you can pull to change outcomes</li>
<li>Ignoring that a few extreme observations might drive the relationship</li>
<li>Forgetting about variables that aren't in the dataset but matter in reality</li>
</ul>
<table>
<thead>
<tr>
<th>Good Reasoning</th>
<th>Weak Reasoning</th>
</tr>
</thead>
<tbody>
<tr>
<td><em>"This coefficient suggests an association, but the relationship might be driven by unobserved factors or a small subset of cases."</em></td>
<td><em>"The coefficient is 5, so if we increase X by 1, Y will increase by 5."</em></td>
</tr>
</tbody>
</table>
<h3>Classification (Chapter 3)</h3>
<p><strong>Key concepts:</strong> Predictions are probabilities, not certainties. Threshold choice affects which errors you make.</p>
<p><strong>Common traps to watch for:</strong></p>
<ul>
<li>Celebrating high accuracy without checking the base rate</li>
<li>Accepting the default 0.5 threshold without justification</li>
<li>Treating predictions as yes/no facts rather than probability estimates</li>
</ul>
<table>
<thead>
<tr>
<th>Good Reasoning</th>
<th>Weak Reasoning</th>
</tr>
</thead>
<tbody>
<tr>
<td><em>"The model identifies higher-risk cases, but the threshold should reflect which type of error is more costly in this business context."</em></td>
<td><em>"We got 95% accuracy, so the model is great and we should use it."</em></td>
</tr>
</tbody>
</table>
<h3>Clustering (Chapter 4)</h3>
<p><strong>Key concepts:</strong> Clusters are algorithmic summaries, not natural "types" that exist in reality.</p>
<p><strong>Common traps to watch for:</strong></p>
<ul>
<li>Treating clusters as if they represent real, distinct customer types</li>
<li>Ignoring that different features or scaling would produce different clusters</li>
<li>Letting one variable dominate the clustering without realizing it</li>
</ul>
<table>
<thead>
<tr>
<th>Good Reasoning</th>
<th>Weak Reasoning</th>
</tr>
</thead>
<tbody>
<tr>
<td><em>"These clusters summarize patterns based on the features we chose. The segmentation would look different with different variables or scaling."</em></td>
<td><em>"Cluster 2 customers ARE our loyal high-value segment. We should target them."</em></td>
</tr>
</tbody>
</table>
<hr>
<h2>Tips for Getting the Most Out of the Companion</h2>
<div class="two-column">
<div class="column do-column">
<h4>Do:</h4>
<ol>
<li><strong>Think before responding.</strong> The companion is patient. Take time to formulate your reasoning.</li>
<li><strong>Use tentative language.</strong> Practice saying "suggests," "appears to," "in this dataset" rather than "proves" or "shows."</li>
<li><strong>Embrace uncertainty.</strong> Acknowledging what you don't know is a sign of strong reasoning, not weakness.</li>
<li><strong>Ask yourself: "What could go wrong?"</strong> Before proposing any action, consider what assumptions might be violated.</li>
<li><strong>Complete all seven stages.</strong> The structure exists for a reason. Skipping stages can leave conclusions inadequately tested.</li>
</ol>
</div>
<div class="column dont-column">
<h4>Don't:</h4>
<ol>
<li><strong>Don't try to get "the answer."</strong> There often isn't a single right answer — there's disciplined reasoning.</li>
<li><strong>Don't use causal language casually.</strong> Avoid "X causes Y" or "increasing X will increase Y" unless you have experimental evidence.</li>
<li><strong>Don't skip to action.</strong> Resist the urge to recommend what the business should do before fully understanding limitations.</li>
<li><strong>Don't treat model outputs as facts.</strong> They are estimates based on available data, not ground truth.</li>
<li><strong>Don't be defensive when challenged.</strong> The companion's probing questions are designed to strengthen your thinking.</li>
</ol>
</div>
</div>
<hr>
<h2>What "Success" Looks Like</h2>
<p>You've successfully completed a session when you can articulate your findings responsibly:</p>
<table>
<thead>
<tr>
<th>Weak Outcome</th>
<th>Strong Outcome</th>
</tr>
</thead>
<tbody>
<tr>
<td>"The model says X, so we should do Y."</td>
<td>"The model suggests an association that warrants further investigation."</td>
</tr>
<tr>
<td>"R-squared is 0.7, so the model is good."</td>
<td>"The model explains some variation, but key drivers might be missing."</td>
</tr>
<tr>
<td>"Cluster 3 customers are our best segment."</td>
<td>"Cluster 3 shows a pattern of higher engagement, though this depends on how we defined similarity."</td>
</tr>
<tr>
<td>"95% accuracy means we should deploy this."</td>
<td>"High accuracy is encouraging, but we need to understand the cost of different error types before deciding on a threshold."</td>
</tr>
</tbody>
</table>
<div class="callout callout-green">
<strong>The gold standard:</strong> You leave with more questions than you started with — but better, more focused questions.
</div>
<hr>
<h2>Frequently Asked Questions</h2>
<div class="faq-item">
<h4>Q: Can I upload my own data?</h4>
<p><strong>A:</strong> No. The Analytics Reasoning Companion works exclusively with the book's curated datasets. This allows us to focus on developing your reasoning skills with carefully designed examples. If you want to analyze your own data, use the <strong>Analytics Modeling Sandbox</strong> companion instead.</p>
</div>
<div class="faq-item">
<h4>Q: Why won't the companion just give me the answer?</h4>
<p><strong>A:</strong> Because getting "the answer" isn't the point. The companion is designed to develop your judgment — your ability to know when an answer should or shouldn't be trusted. This skill transfers to every future analysis you'll conduct.</p>
</div>
<div class="faq-item">
<h4>Q: The companion keeps asking me questions instead of explaining. Is that normal?</h4>
<p><strong>A:</strong> Yes, especially in Exercise Mode. The Socratic approach — answering questions with questions — is intentional. It forces you to articulate and examine your own reasoning rather than passively receiving information.</p>
</div>
<div class="faq-item">
<h4>Q: I said something and the companion pushed back. Did I get it wrong?</h4>
<p><strong>A:</strong> Not necessarily wrong, but your statement may need more nuance or supporting evidence. The companion asks follow-up questions when statements:</p>
<ul>
<li>Imply causation from observational data</li>
<li>Express excessive certainty</li>
<li>Skip over important limitations</li>
<li>Propose action without acknowledging uncertainty</li>
</ul>
<p>Being challenged is part of the learning process.</p>
</div>
<div class="faq-item">
<h4>Q: How do I know when I'm done with a session?</h4>
<p><strong>A:</strong> A session is complete when you've worked through all seven stages and can articulate:</p>
<ol>
<li>What the analysis found (in conditional language)</li>
<li>What limitations apply</li>
<li>What questions remain</li>
<li>What additional evidence would increase your confidence</li>
</ol>
<p>The companion will prompt you with a closing question: <em>"If you had to summarize this in one cautious sentence to a decision-maker, what would you say?"</em></p>
</div>
<div class="faq-item">
<h4>Q: Can I go back to an earlier stage?</h4>
<p><strong>A:</strong> The workflow is designed to be sequential. Once you've moved past a stage, the companion won't reopen it. This prevents endless cycling and builds the habit of thorough, stage-by-stage analysis.</p>
</div>
<div class="faq-item">
<h4>Q: What if I disagree with the companion?</h4>
<p><strong>A:</strong> Good! The companion isn't always "right" — it's designed to challenge you. If you disagree, articulate your reasoning. Defending your position with good arguments is exactly the kind of thinking the companion aims to develop.</p>
</div>
<hr>
<h2>Quick Reference: The 7 Stages</h2>
<table>
<thead>
<tr>
<th>Stage</th>
<th>Focus</th>
<th>Key Question</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>1. Business Understanding</strong></td>
<td>The decision</td>
<td>What action might change?</td>
</tr>
<tr>
<td><strong>2. Analytical Question</strong></td>
<td>The formulation</td>
<td>What are we trying to explain or predict?</td>
</tr>
<tr>
<td><strong>3. Data Understanding</strong></td>
<td>The raw material</td>
<td>What can this data tell us — and what can't it?</td>
</tr>
<tr>
<td><strong>4. Data Preparation</strong></td>
<td>The choices</td>
<td>How do preparation decisions affect interpretation?</td>
</tr>
<tr>
<td><strong>5. Modeling</strong></td>
<td>The evidence</td>
<td>What associations does the model reveal?</td>
</tr>
<tr>
<td><strong>6. Diagnostics</strong></td>
<td>The skepticism</td>
<td>What could still go wrong?</td>
</tr>
<tr>
<td><strong>7. Interpretation</strong></td>
<td>The conclusion</td>
<td>What should we cautiously conclude?</td>
</tr>
</tbody>
</table>
<hr>
<div class="final-reminder">
<blockquote>
<p>"The purpose of this companion is not to help you get the right answer. It is to help you learn when an answer should not be trusted too quickly."</p>
</blockquote>
<p>The habits you build here — questioning assumptions, acknowledging uncertainty, distinguishing association from causation — will serve you in every analysis you ever conduct.</p>
<p><strong>Welcome to disciplined analytical thinking.</strong></p>
</div>
<footer>
<p><em>Analytics Reasoning Companion — A companion to "Analytics for Managers"</em></p>
</footer>
</body>
</html>
|