Exam 1
Tammy Brockman
2022-10-16
Section 1 of Exam 1: Regression Model
Red font is directions.
Green font is questions.
Blue font is answers.
- Using the formatting learned in the first class, type the following header in R Markdown:
See above header
Read the csv file titled Training Data into R. All rows of this dataset should be used as your training set in the development of a regression model. This dataset contains information about some of Pfizer’s patent applications. Each patent application has a unique ID given to it, and this ID is listed in the Patent_Number column. The column titled Cites_Patent_Count lists the number of patent citations made within the corresponding patent application, and the column titled Cited_by_Patent_Count lists the number of patents which cite the document identified by the Patent_Number. The Cited_by_Patent_Count column can be seen as a measure of the influence and strength of the innovation, and as such, it can be useful to try to predict this column to help firms understand the potential influence of their innovations.
training <- read.csv("C:/Users/justt/Desktop/School/621/Exams/Exam 1/Training Data.csv")
str(training)## 'data.frame': 626 obs. of 3 variables:
## $ Patent_Number : chr "PL 3341367 T3" "HR P20210871 T1" "CR 20210284 A" "US 2021/0205309 A1" ...
## $ Cites_Patent_Count : int 0 0 0 0 3 0 0 1 0 0 ...
## $ Cited_by_Patent_Count: int 0 0 0 0 0 0 0 0 0 0 ...- Use regression to try to predict the Cited_by_Patent_Count, with Cites_Patent_Count used as a covariate. Based on your results, write an equation using the following format: Cited_by_Patent_Count ≈ B1(Cites_Patent_Count) + B2, where B1 and B2 are real numbers
training <- training[ ,-1]
colnames(training)## [1] "Cites_Patent_Count" "Cited_by_Patent_Count"colnames(training) <- c("B1", "B2")
model1 <- lm(B2 ~ B1, data = training)
model1##
## Call:
## lm(formula = B2 ~ B1, data = training)
##
## Coefficients:
## (Intercept) B1
## 0.05226 0.00267summary(model1)##
## Call:
## lm(formula = B2 ~ B1, data = training)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9469 -0.0523 -0.0523 -0.0523 3.7661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0522603 0.0130896 3.993 7.32e-05 ***
## B1 0.0026705 0.0005322 5.018 6.81e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3223 on 624 degrees of freedom
## Multiple R-squared: 0.03879, Adjusted R-squared: 0.03725
## F-statistic: 25.18 on 1 and 624 DF, p-value: 6.811e-07Fill-in the blanks for the following statements:
- Your regression equation provides an “estimate” of the actual values for the numbers B1 and B2. There is a 90% probability that the actual value of B1 lies between _________ and ________.
confint(model1, level = 0.90)## 5 % 95 %
## (Intercept) 0.030697775 0.073822761
## B1 0.001793859 0.003547102There is a 90% probability that the actual value of B1 lies between 0.001793859 and 0.003547102.
- There is a 90% probability that the actual value of B2 lies between ________ and ________.
confint(model1, level = 0.90)## 5 % 95 %
## (Intercept) 0.030697775 0.073822761
## B1 0.001793859 0.003547102There is a 90% probability that the actual value of B2 lies between 0.030697775 and 0.073822761.
- Based on Cook’s Distance, are there outliers that may possibly need to be removed and/or treated differently in the dataset? Create a plot using R to support your answer.
cooks.distance(model1)## 1 2 3 4 5 6
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.810625e-05 2.174933e-05
## 7 8 9 10 11 12
## 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 13 14 15 16 17 18
## 5.071376e-05 2.174933e-05 2.174933e-05 8.574799e-05 2.174933e-05 2.174933e-05
## 19 20 21 22 23 24
## 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05 3.021095e-02 2.372145e-05
## 25 26 27 28 29 30
## 2.174933e-05 2.174933e-05 2.583357e-05 2.372145e-05 7.152876e-03 2.174933e-05
## 31 32 33 34 35 36
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 37 38 39 40 41 42
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 43 44 45 46 47 48
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 49 50 51 52 53 54
## 2.174933e-05 2.174933e-05 2.174933e-05 2.583357e-05 2.174933e-05 2.174933e-05
## 55 56 57 58 59 60
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 61 62 63 64 65 66
## 2.174933e-05 2.174933e-05 2.174933e-05 3.612723e-05 2.174933e-05 2.174933e-05
## 67 68 69 70 71 72
## 2.174933e-05 6.914970e-03 2.174933e-05 2.174933e-05 3.612723e-05 2.174933e-05
## 73 74 75 76 77 78
## 6.580877e-05 6.111181e-02 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 79 80 81 82 83 84
## 2.810625e-05 5.071376e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 85 86 87 88 89 90
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 3.322685e-05
## 91 92 93 94 95 96
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 7.152876e-03 6.580877e-05
## 97 98 99 100 101 102
## 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05 4.284078e-04 2.174933e-05
## 103 104 105 106 107 108
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 109 110 111 112 113 114
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 115 116 117 118 119 120
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 121 122 123 124 125 126
## 2.174933e-05 2.174933e-05 2.174933e-05 2.810625e-05 2.174933e-05 4.275628e-05
## 127 128 129 130 131 132
## 9.239691e-02 5.071376e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05
## 133 134 135 136 137 138
## 2.174933e-05 1.273912e-02 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 139 140 141 142 143 144
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 145 146 147 148 149 150
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 151 152 153 154 155 156
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 157 158 159 160 161 162
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 163 164 165 166 167 168
## 2.810625e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 169 170 171 172 173 174
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 175 176 177 178 179 180
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 7.381892e-02 2.174933e-05
## 181 182 183 184 185 186
## 2.838903e-02 2.174933e-05 2.174933e-05 2.174933e-05 1.570643e-03 2.174933e-05
## 187 188 189 190 191 192
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 193 194 195 196 197 198
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 199 200 201 202 203 204
## 2.174933e-05 1.335152e-04 7.152876e-03 2.174933e-05 3.612723e-05 2.174933e-05
## 205 206 207 208 209 210
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 211 212 213 214 215 216
## 2.372145e-05 2.174933e-05 2.810625e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 217 218 219 220 221 222
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05
## 223 224 225 226 227 228
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05
## 229 230 231 232 233 234
## 2.174933e-05 2.174933e-05 8.849406e-01 2.174933e-05 2.174933e-05 2.174933e-05
## 235 236 237 238 239 240
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 241 242 243 244 245 246
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 247 248 249 250 251 252
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 253 254 255 256 257 258
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 259 260 261 262 263 264
## 3.021095e-02 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 3.612723e-05
## 265 266 267 268 269 270
## 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 271 272 273 274 275 276
## 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 277 278 279 280 281 282
## 1.222411e-04 2.174933e-05 2.174933e-05 2.174933e-05 2.810625e-05 2.174933e-05
## 283 284 285 286 287 288
## 2.174933e-05 2.583357e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 289 290 291 292 293 294
## 2.174933e-05 2.174933e-05 2.174933e-05 3.929307e-05 2.174933e-05 2.174933e-05
## 295 296 297 298 299 300
## 2.174933e-05 9.727993e-02 9.369997e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 301 302 303 304 305 306
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 307 308 309 310 311 312
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 313 314 315 316 317 318
## 2.174933e-05 2.174933e-05 2.174933e-05 3.963990e-04 2.174933e-05 2.174933e-05
## 319 320 321 322 323 324
## 7.152876e-03 2.174933e-05 1.796726e-02 2.174933e-05 2.174933e-05 2.810625e-05
## 325 326 327 328 329 330
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 331 332 333 334 335 336
## 2.174933e-05 2.372145e-05 2.174933e-05 1.913967e-02 2.174933e-05 2.174933e-05
## 337 338 339 340 341 342
## 2.174933e-05 7.185379e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 343 344 345 346 347 348
## 2.174933e-05 2.485777e-03 2.174933e-05 3.322685e-05 2.174933e-05 1.118882e-04
## 349 350 351 352 353 354
## 2.174933e-05 2.174933e-05 7.702738e-04 2.372145e-05 2.174933e-05 2.174933e-05
## 355 356 357 358 359 360
## 6.996241e-03 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05
## 361 362 363 364 365 366
## 2.174933e-05 2.174933e-05 3.322685e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 367 368 369 370 371 372
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 7.152876e-03 6.030046e-05
## 373 374 375 376 377 378
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 379 380 381 382 383 384
## 2.174933e-05 3.056231e-05 7.152876e-03 2.174933e-05 2.174933e-05 2.174933e-05
## 385 386 387 388 389 390
## 2.174933e-05 2.174933e-05 1.335152e-04 2.174933e-05 2.174933e-05 2.174933e-05
## 391 392 393 394 395 396
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05
## 397 398 399 400 401 402
## 2.174933e-05 2.174933e-05 5.528323e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 403 404 405 406 407 408
## 2.174933e-05 2.174933e-05 3.021095e-02 2.174933e-05 2.372145e-05 2.174933e-05
## 409 410 411 412 413 414
## 3.056231e-05 7.152876e-03 2.174933e-05 2.174933e-05 3.929307e-05 2.174933e-05
## 415 416 417 418 419 420
## 2.174933e-05 2.174933e-05 7.111225e-02 2.174933e-05 5.528323e-05 7.152876e-03
## 421 422 423 424 425 426
## 2.174933e-05 4.655102e-05 8.574799e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 427 428 429 430 431 432
## 2.174933e-05 2.174933e-05 2.174933e-05 3.056231e-05 2.174933e-05 2.583357e-05
## 433 434 435 436 437 438
## 2.174933e-05 2.583357e-05 2.174933e-05 2.810625e-05 2.174933e-05 2.174933e-05
## 439 440 441 442 443 444
## 2.174933e-05 2.174933e-05 1.222411e-04 2.174933e-05 5.071376e-05 2.174933e-05
## 445 446 447 448 449 450
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 451 452 453 454 455 456
## 3.056231e-05 2.174933e-05 2.174933e-05 2.174933e-05 6.030046e-05 2.174933e-05
## 457 458 459 460 461 462
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 9.369997e-05
## 463 464 465 466 467 468
## 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 469 470 471 472 473 474
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 475 476 477 478 479 480
## 2.174933e-05 2.810625e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 481 482 483 484 485 486
## 3.322685e-05 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05 2.174933e-05
## 487 488 489 490 491 492
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.372145e-05 2.174933e-05
## 493 494 495 496 497 498
## 2.174933e-05 2.174933e-05 2.174933e-05 6.773844e-03 2.583357e-05 2.583357e-05
## 499 500 501 502 503 504
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 505 506 507 508 509 510
## 6.775879e-01 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 511 512 513 514 515 516
## 2.174933e-05 2.174933e-05 2.583357e-05 2.583357e-05 2.810625e-05 2.372145e-05
## 517 518 519 520 521 522
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 523 524 525 526 527 528
## 2.174933e-05 2.174933e-05 3.612723e-05 2.174933e-05 2.810625e-05 2.583357e-05
## 529 530 531 532 533 534
## 2.174933e-05 2.174933e-05 7.848345e-05 2.174933e-05 1.150560e-03 2.174933e-05
## 535 536 537 538 539 540
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 541 542 543 544 545 546
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 547 548 549 550 551 552
## 2.174933e-05 2.583357e-05 2.174933e-05 2.174933e-05 2.810625e-05 2.174933e-05
## 553 554 555 556 557 558
## 2.174933e-05 7.152876e-03 2.174933e-05 2.174933e-05 3.021095e-02 2.174933e-05
## 559 560 561 562 563 564
## 2.174933e-05 7.185379e-05 2.174933e-05 2.174933e-05 3.612723e-05 2.174933e-05
## 565 566 567 568 569 570
## 2.174933e-05 2.174933e-05 2.635251e+00 1.523294e-02 2.174933e-05 2.174933e-05
## 571 572 573 574 575 576
## 2.174933e-05 8.756098e-03 2.174933e-05 2.174933e-05 6.914970e-03 2.174933e-05
## 577 578 579 580 581 582
## 2.174933e-05 3.021095e-02 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 583 584 585 586 587 588
## 5.282779e-03 2.174933e-05 2.174933e-05 2.174933e-05 3.664304e-04 2.174933e-05
## 589 590 591 592 593 594
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 595 596 597 598 599 600
## 6.914970e-03 2.810625e-05 2.174933e-05 2.174933e-05 2.174933e-05 3.056231e-05
## 601 602 603 604 605 606
## 2.174933e-05 2.463827e+00 3.056231e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 607 608 609 610 611 612
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 613 614 615 616 617 618
## 2.174933e-05 2.174933e-05 4.275628e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 619 620 621 622 623 624
## 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05 2.174933e-05
## 625 626
## 2.174933e-05 2.583357e-05max(cooks.distance(model1))## [1] 2.635251Yes, there are outliers that may possibly need to be removed and/or treated differently in the dataset. The Max is 2.635251, which is greater than 1.0 value for Cook’s Distance.
plot(model1)
This shows that there are 2 points outside the 1.0 range for Cook’s Distance. Points 602 and 567 should be considered outliers and should be removed. This could be the result of bad data capture, or data anomolies.
- Check the normality of residuals by creating a QQ-plot. Based on the plot, are the residuals normally distributed?
qqnorm(model1$residuals, main = "model1")
qqline(model1$residuals)
No, the residuals are not normally distributed. Normal Unit Scaling is recommended.
Use the regression model that you created in #2 to predict the Cited_by_Patent_Count in each of the following scenarios:
- Cites_Patent_Count = 340
model1 <- lm(B2 ~ B1, data = training)
hatvalues(model1)## 1 2 3 4 5 6
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001602470 0.001649210
## 7 8 9 10 11 12
## 0.001649210 0.001628178 0.001649210 0.001649210 0.001649210 0.001649210
## 13 14 15 16 17 18
## 0.001684219 0.001649210 0.001649210 0.001966906 0.001649210 0.001649210
## 19 20 21 22 23 24
## 0.001628178 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178
## 25 26 27 28 29 30
## 0.001649210 0.001649210 0.001612598 0.001628178 0.001649210 0.001649210
## 31 32 33 34 35 36
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 37 38 39 40 41 42
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 43 44 45 46 47 48
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 49 50 51 52 53 54
## 0.001649210 0.001649210 0.001649210 0.001612598 0.001649210 0.001649210
## 55 56 57 58 59 60
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 61 62 63 64 65 66
## 0.001649210 0.001649210 0.001649210 0.001604795 0.001649210 0.001649210
## 67 68 69 70 71 72
## 0.001649210 0.001612598 0.001649210 0.001649210 0.001604795 0.001649210
## 73 74 75 76 77 78
## 0.001801030 0.057059770 0.001649210 0.001649210 0.001649210 0.001649210
## 79 80 81 82 83 84
## 0.001602470 0.001684219 0.001649210 0.001649210 0.001649210 0.001649210
## 85 86 87 88 89 90
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001598568
## 91 92 93 94 95 96
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001801030
## 97 98 99 100 101 102
## 0.001649210 0.001649210 0.001628178 0.001649210 0.004156863 0.001649210
## 103 104 105 106 107 108
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 109 110 111 112 113 114
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 115 116 117 118 119 120
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 121 122 123 124 125 126
## 0.001649210 0.001649210 0.001649210 0.001602470 0.001649210 0.001633604
## 127 128 129 130 131 132
## 0.053239702 0.001684219 0.001649210 0.001649210 0.001649210 0.001628178
## 133 134 135 136 137 138
## 0.001649210 0.025500044 0.001649210 0.001649210 0.001649210 0.001649210
## 139 140 141 142 143 144
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 145 146 147 148 149 150
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 151 152 153 154 155 156
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 157 158 159 160 161 162
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 163 164 165 166 167 168
## 0.001602470 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 169 170 171 172 173 174
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 175 176 177 178 179 180
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001801030 0.001649210
## 181 182 183 184 185 186
## 0.038683734 0.001649210 0.001649210 0.001649210 0.008314883 0.001649210
## 187 188 189 190 191 192
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 193 194 195 196 197 198
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 199 200 201 202 203 204
## 0.001649210 0.002352401 0.001649210 0.001649210 0.001604795 0.001649210
## 205 206 207 208 209 210
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 211 212 213 214 215 216
## 0.001628178 0.001649210 0.001602470 0.001649210 0.001649210 0.001649210
## 217 218 219 220 221 222
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178 0.001649210
## 223 224 225 226 227 228
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178
## 229 230 231 232 233 234
## 0.001649210 0.001649210 0.012638032 0.001649210 0.001649210 0.001649210
## 235 236 237 238 239 240
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 241 242 243 244 245 246
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 247 248 249 250 251 252
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 253 254 255 256 257 258
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 259 260 261 262 263 264
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001604795
## 265 266 267 268 269 270
## 0.001649210 0.001628178 0.001649210 0.001649210 0.001649210 0.001649210
## 271 272 273 274 275 276
## 0.001649210 0.001649210 0.001628178 0.001649210 0.001649210 0.001649210
## 277 278 279 280 281 282
## 0.002264399 0.001649210 0.001649210 0.001649210 0.001602470 0.001649210
## 283 284 285 286 287 288
## 0.001649210 0.001612598 0.001649210 0.001649210 0.001649210 0.001649210
## 289 290 291 292 293 294
## 0.001649210 0.001649210 0.001649210 0.001616473 0.001649210 0.001649210
## 295 296 297 298 299 300
## 0.001649210 0.060213954 0.002033102 0.001649210 0.001649210 0.001649210
## 301 302 303 304 305 306
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 307 308 309 310 311 312
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 313 314 315 316 317 318
## 0.001649210 0.001649210 0.001649210 0.003992536 0.001649210 0.001649210
## 319 320 321 322 323 324
## 0.001649210 0.001649210 0.005257305 0.001649210 0.001649210 0.001602470
## 325 326 327 328 329 330
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 331 332 333 334 335 336
## 0.001649210 0.001628178 0.001649210 0.005667733 0.001649210 0.001649210
## 337 338 339 340 341 342
## 0.001649210 0.001850871 0.001649210 0.001649210 0.001649210 0.001649210
## 343 344 345 346 347 348
## 0.001649210 0.010654413 0.001649210 0.001598568 0.001649210 0.002181848
## 349 350 351 352 353 354
## 0.001649210 0.001649210 0.005667733 0.001628178 0.001649210 0.001649210
## 355 356 357 358 359 360
## 0.001717704 0.001649210 0.001649210 0.001628178 0.001649210 0.001649210
## 361 362 363 364 365 366
## 0.001649210 0.001649210 0.001598568 0.001649210 0.001649210 0.001649210
## 367 368 369 370 371 372
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001756641
## 373 374 375 376 377 378
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 379 380 381 382 383 384
## 0.001649210 0.001597793 0.001649210 0.001649210 0.001649210 0.001649210
## 385 386 387 388 389 390
## 0.001649210 0.001649210 0.002352401 0.001649210 0.001649210 0.001649210
## 391 392 393 394 395 396
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178 0.001649210
## 397 398 399 400 401 402
## 0.001649210 0.001649210 0.001717704 0.001649210 0.001649210 0.001649210
## 403 404 405 406 407 408
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178 0.001649210
## 409 410 411 412 413 414
## 0.001597793 0.001649210 0.001649210 0.001649210 0.001616473 0.001649210
## 415 416 417 418 419 420
## 0.001649210 0.001649210 0.032597334 0.001649210 0.001717704 0.001649210
## 421 422 423 424 425 426
## 0.001649210 0.001656186 0.001966906 0.001649210 0.001649210 0.001649210
## 427 428 429 430 431 432
## 0.001649210 0.001649210 0.001649210 0.001597793 0.001649210 0.001612598
## 433 434 435 436 437 438
## 0.001649210 0.001612598 0.001649210 0.001602470 0.001649210 0.001649210
## 439 440 441 442 443 444
## 0.001649210 0.001649210 0.002264399 0.001649210 0.001684219 0.001649210
## 445 446 447 448 449 450
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 451 452 453 454 455 456
## 0.001597793 0.001649210 0.001649210 0.001649210 0.001756641 0.001649210
## 457 458 459 460 461 462
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.002033102
## 463 464 465 466 467 468
## 0.001649210 0.001628178 0.001649210 0.001649210 0.001649210 0.001649210
## 469 470 471 472 473 474
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 475 476 477 478 479 480
## 0.001649210 0.001602470 0.001649210 0.001649210 0.001649210 0.001649210
## 481 482 483 484 485 486
## 0.001598568 0.001649210 0.001649210 0.001628178 0.001649210 0.001649210
## 487 488 489 490 491 492
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178 0.001649210
## 493 494 495 496 497 498
## 0.001649210 0.001649210 0.001649210 0.001597793 0.001612598 0.001612598
## 499 500 501 502 503 504
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 505 506 507 508 509 510
## 0.051013082 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 511 512 513 514 515 516
## 0.001649210 0.001649210 0.001612598 0.001612598 0.001602470 0.001628178
## 517 518 519 520 521 522
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 523 524 525 526 527 528
## 0.001649210 0.001649210 0.001604795 0.001649210 0.001602470 0.001612598
## 529 530 531 532 533 534
## 0.001649210 0.001649210 0.001906163 0.001649210 0.007029858 0.001649210
## 535 536 537 538 539 540
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 541 542 543 544 545 546
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 547 548 549 550 551 552
## 0.001649210 0.001612598 0.001649210 0.001649210 0.001602470 0.001649210
## 553 554 555 556 557 558
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 559 560 561 562 563 564
## 0.001649210 0.001850871 0.001649210 0.001649210 0.001604795 0.001649210
## 565 566 567 568 569 570
## 0.001649210 0.001649210 0.299599101 0.004326640 0.001649210 0.001649210
## 571 572 573 574 575 576
## 0.001649210 0.002264399 0.001649210 0.001649210 0.001612598 0.001649210
## 577 578 579 580 581 582
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 583 584 585 586 587 588
## 0.015981449 0.001649210 0.001649210 0.001649210 0.003833662 0.001649210
## 589 590 591 592 593 594
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 595 596 597 598 599 600
## 0.001612598 0.001602470 0.001649210 0.001649210 0.001649210 0.001597793
## 601 602 603 604 605 606
## 0.001649210 0.292432463 0.001597793 0.001649210 0.001649210 0.001649210
## 607 608 609 610 611 612
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 613 614 615 616 617 618
## 0.001649210 0.001649210 0.001633604 0.001649210 0.001649210 0.001649210
## 619 620 621 622 623 624
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 625 626
## 0.001649210 0.001612598max(hatvalues(model1))## [1] 0.2995991x_new = c(1, 340)
X= model.matrix(model1)
t(x_new)%*%solve(t(X)%*%X)%*%x_new## [,1]
## [1,] 0.3086801- Cites_Patent_Count = 300
model1 <- lm(B2 ~ B1, data = training)
hatvalues(model1)## 1 2 3 4 5 6
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001602470 0.001649210
## 7 8 9 10 11 12
## 0.001649210 0.001628178 0.001649210 0.001649210 0.001649210 0.001649210
## 13 14 15 16 17 18
## 0.001684219 0.001649210 0.001649210 0.001966906 0.001649210 0.001649210
## 19 20 21 22 23 24
## 0.001628178 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178
## 25 26 27 28 29 30
## 0.001649210 0.001649210 0.001612598 0.001628178 0.001649210 0.001649210
## 31 32 33 34 35 36
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 37 38 39 40 41 42
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 43 44 45 46 47 48
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 49 50 51 52 53 54
## 0.001649210 0.001649210 0.001649210 0.001612598 0.001649210 0.001649210
## 55 56 57 58 59 60
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 61 62 63 64 65 66
## 0.001649210 0.001649210 0.001649210 0.001604795 0.001649210 0.001649210
## 67 68 69 70 71 72
## 0.001649210 0.001612598 0.001649210 0.001649210 0.001604795 0.001649210
## 73 74 75 76 77 78
## 0.001801030 0.057059770 0.001649210 0.001649210 0.001649210 0.001649210
## 79 80 81 82 83 84
## 0.001602470 0.001684219 0.001649210 0.001649210 0.001649210 0.001649210
## 85 86 87 88 89 90
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001598568
## 91 92 93 94 95 96
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001801030
## 97 98 99 100 101 102
## 0.001649210 0.001649210 0.001628178 0.001649210 0.004156863 0.001649210
## 103 104 105 106 107 108
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 109 110 111 112 113 114
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 115 116 117 118 119 120
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 121 122 123 124 125 126
## 0.001649210 0.001649210 0.001649210 0.001602470 0.001649210 0.001633604
## 127 128 129 130 131 132
## 0.053239702 0.001684219 0.001649210 0.001649210 0.001649210 0.001628178
## 133 134 135 136 137 138
## 0.001649210 0.025500044 0.001649210 0.001649210 0.001649210 0.001649210
## 139 140 141 142 143 144
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 145 146 147 148 149 150
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 151 152 153 154 155 156
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 157 158 159 160 161 162
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 163 164 165 166 167 168
## 0.001602470 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 169 170 171 172 173 174
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 175 176 177 178 179 180
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001801030 0.001649210
## 181 182 183 184 185 186
## 0.038683734 0.001649210 0.001649210 0.001649210 0.008314883 0.001649210
## 187 188 189 190 191 192
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 193 194 195 196 197 198
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 199 200 201 202 203 204
## 0.001649210 0.002352401 0.001649210 0.001649210 0.001604795 0.001649210
## 205 206 207 208 209 210
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 211 212 213 214 215 216
## 0.001628178 0.001649210 0.001602470 0.001649210 0.001649210 0.001649210
## 217 218 219 220 221 222
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178 0.001649210
## 223 224 225 226 227 228
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178
## 229 230 231 232 233 234
## 0.001649210 0.001649210 0.012638032 0.001649210 0.001649210 0.001649210
## 235 236 237 238 239 240
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 241 242 243 244 245 246
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 247 248 249 250 251 252
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 253 254 255 256 257 258
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 259 260 261 262 263 264
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001604795
## 265 266 267 268 269 270
## 0.001649210 0.001628178 0.001649210 0.001649210 0.001649210 0.001649210
## 271 272 273 274 275 276
## 0.001649210 0.001649210 0.001628178 0.001649210 0.001649210 0.001649210
## 277 278 279 280 281 282
## 0.002264399 0.001649210 0.001649210 0.001649210 0.001602470 0.001649210
## 283 284 285 286 287 288
## 0.001649210 0.001612598 0.001649210 0.001649210 0.001649210 0.001649210
## 289 290 291 292 293 294
## 0.001649210 0.001649210 0.001649210 0.001616473 0.001649210 0.001649210
## 295 296 297 298 299 300
## 0.001649210 0.060213954 0.002033102 0.001649210 0.001649210 0.001649210
## 301 302 303 304 305 306
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 307 308 309 310 311 312
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 313 314 315 316 317 318
## 0.001649210 0.001649210 0.001649210 0.003992536 0.001649210 0.001649210
## 319 320 321 322 323 324
## 0.001649210 0.001649210 0.005257305 0.001649210 0.001649210 0.001602470
## 325 326 327 328 329 330
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 331 332 333 334 335 336
## 0.001649210 0.001628178 0.001649210 0.005667733 0.001649210 0.001649210
## 337 338 339 340 341 342
## 0.001649210 0.001850871 0.001649210 0.001649210 0.001649210 0.001649210
## 343 344 345 346 347 348
## 0.001649210 0.010654413 0.001649210 0.001598568 0.001649210 0.002181848
## 349 350 351 352 353 354
## 0.001649210 0.001649210 0.005667733 0.001628178 0.001649210 0.001649210
## 355 356 357 358 359 360
## 0.001717704 0.001649210 0.001649210 0.001628178 0.001649210 0.001649210
## 361 362 363 364 365 366
## 0.001649210 0.001649210 0.001598568 0.001649210 0.001649210 0.001649210
## 367 368 369 370 371 372
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001756641
## 373 374 375 376 377 378
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 379 380 381 382 383 384
## 0.001649210 0.001597793 0.001649210 0.001649210 0.001649210 0.001649210
## 385 386 387 388 389 390
## 0.001649210 0.001649210 0.002352401 0.001649210 0.001649210 0.001649210
## 391 392 393 394 395 396
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178 0.001649210
## 397 398 399 400 401 402
## 0.001649210 0.001649210 0.001717704 0.001649210 0.001649210 0.001649210
## 403 404 405 406 407 408
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178 0.001649210
## 409 410 411 412 413 414
## 0.001597793 0.001649210 0.001649210 0.001649210 0.001616473 0.001649210
## 415 416 417 418 419 420
## 0.001649210 0.001649210 0.032597334 0.001649210 0.001717704 0.001649210
## 421 422 423 424 425 426
## 0.001649210 0.001656186 0.001966906 0.001649210 0.001649210 0.001649210
## 427 428 429 430 431 432
## 0.001649210 0.001649210 0.001649210 0.001597793 0.001649210 0.001612598
## 433 434 435 436 437 438
## 0.001649210 0.001612598 0.001649210 0.001602470 0.001649210 0.001649210
## 439 440 441 442 443 444
## 0.001649210 0.001649210 0.002264399 0.001649210 0.001684219 0.001649210
## 445 446 447 448 449 450
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 451 452 453 454 455 456
## 0.001597793 0.001649210 0.001649210 0.001649210 0.001756641 0.001649210
## 457 458 459 460 461 462
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.002033102
## 463 464 465 466 467 468
## 0.001649210 0.001628178 0.001649210 0.001649210 0.001649210 0.001649210
## 469 470 471 472 473 474
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 475 476 477 478 479 480
## 0.001649210 0.001602470 0.001649210 0.001649210 0.001649210 0.001649210
## 481 482 483 484 485 486
## 0.001598568 0.001649210 0.001649210 0.001628178 0.001649210 0.001649210
## 487 488 489 490 491 492
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001628178 0.001649210
## 493 494 495 496 497 498
## 0.001649210 0.001649210 0.001649210 0.001597793 0.001612598 0.001612598
## 499 500 501 502 503 504
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 505 506 507 508 509 510
## 0.051013082 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 511 512 513 514 515 516
## 0.001649210 0.001649210 0.001612598 0.001612598 0.001602470 0.001628178
## 517 518 519 520 521 522
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 523 524 525 526 527 528
## 0.001649210 0.001649210 0.001604795 0.001649210 0.001602470 0.001612598
## 529 530 531 532 533 534
## 0.001649210 0.001649210 0.001906163 0.001649210 0.007029858 0.001649210
## 535 536 537 538 539 540
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 541 542 543 544 545 546
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 547 548 549 550 551 552
## 0.001649210 0.001612598 0.001649210 0.001649210 0.001602470 0.001649210
## 553 554 555 556 557 558
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 559 560 561 562 563 564
## 0.001649210 0.001850871 0.001649210 0.001649210 0.001604795 0.001649210
## 565 566 567 568 569 570
## 0.001649210 0.001649210 0.299599101 0.004326640 0.001649210 0.001649210
## 571 572 573 574 575 576
## 0.001649210 0.002264399 0.001649210 0.001649210 0.001612598 0.001649210
## 577 578 579 580 581 582
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 583 584 585 586 587 588
## 0.015981449 0.001649210 0.001649210 0.001649210 0.003833662 0.001649210
## 589 590 591 592 593 594
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 595 596 597 598 599 600
## 0.001612598 0.001602470 0.001649210 0.001649210 0.001649210 0.001597793
## 601 602 603 604 605 606
## 0.001649210 0.292432463 0.001597793 0.001649210 0.001649210 0.001649210
## 607 608 609 610 611 612
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 613 614 615 616 617 618
## 0.001649210 0.001649210 0.001633604 0.001649210 0.001649210 0.001649210
## 619 620 621 622 623 624
## 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210 0.001649210
## 625 626
## 0.001649210 0.001612598max(hatvalues(model1))## [1] 0.2995991x_new = c(1, 300)
X= model.matrix(model1)
t(x_new)%*%solve(t(X)%*%X)%*%x_new## [,1]
## [1,] 0.2398486- Was the prediction made in part a of #3 considered to be extrapolation?
Yes, this is extrapolation because it’s value of 0.3086801 is greater than the max leverage of 0.2995991.
- Was the prediction made in part b of #3 considered to be extrapolation?
No, this is not extrapolation because it’s value of 0.2398486 is less than the max leverage of 0.2995991.
Fill in the blanks based on the regression model that you created in #2, and the corresponding prediction that you made in part b of #3:
- When Cites_Patent_Count is 300 (as in part b of #3), there is a 95% chance that Cited_by_Patent_Count will be between _________ and __________.
B2_pred = data.frame(B1 = c(300))
B2_pred## B1
## 1 300predict(model1, B2_pred, type = "response")## 1
## 0.8534046predict(model1, B2_pred, interval = "prediction", level = .95, type = "response")## fit lwr upr
## 1 0.8534046 0.1486074 1.558202When Cites_Patent_Count is 300 (as in part b of #3), there is a 95% chance that Cited_by_Patent_Count will be between 0.1486074 and 1.558202.
- When Cites_Patent_Count is 300, there is a 95% chance that the mean response will fall between _________ and _________.
predict(model1, B2_pred, interval = "confidence", level = 0.95, type = "response")## fit lwr upr
## 1 0.8534046 0.5434141 1.163395When Cites_Patent_Count is 300, there is a 95% chance that the mean response will fall between 0.5434141 and 1.163395.
Section 2 of Exam 1: Joining Data
- Type the following header in R Markdown, using formatting learned in the first class:
See above header
- Type the following in R Markdown, being sure to make the phrase “mutating joins” appear in bold in your HTML document:
The four types of mutating joins learned in class are:
- Inner join
- Full join
- Left join
- Right join
- Open the Training Data and Sequence Counts csv files, and notice that each file has a column listing the patent application number. For full credit, perform the following joins in R without changing any column titles in either dataset:
train <- read.csv("C:/Users/justt/Desktop/School/621/Exams/Exam 1/Training Data.csv")
sc <- read.csv("C:/Users/justt/Desktop/School/621/Exams/Exam 1/Sequence Counts.csv")
str(train)## 'data.frame': 626 obs. of 3 variables:
## $ Patent_Number : chr "PL 3341367 T3" "HR P20210871 T1" "CR 20210284 A" "US 2021/0205309 A1" ...
## $ Cites_Patent_Count : int 0 0 0 0 3 0 0 1 0 0 ...
## $ Cited_by_Patent_Count: int 0 0 0 0 0 0 0 0 0 0 ...str(sc)## 'data.frame': 222 obs. of 2 variables:
## $ Patent_No. : chr "CA 189065 S" "NI 202000072 A" "KR 20210032013 A" "PH 12020550461 A1" ...
## $ Sequence_Count: int 0 0 80 0 0 0 0 0 0 0 ...- Perform an inner join
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.1## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(dplyr)
train %>% inner_join(sc, by = c("Patent_Number" = "Patent_No.")) -> joined_data
head(joined_data)## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Sequence_Count
## 1 CA 189065 S 0 0 0
## 2 NI 202000072 A 0 0 0
## 3 KR 20210032013 A 0 0 80
## 4 PH 12020550461 A1 0 0 0
## 5 TW I722568 B 0 0 0
## 6 CN 112533674 A 0 2 0- Perform a full join
train %>% full_join(sc, by = c("Patent_Number" = "Patent_No.")) -> joined_data1
head(joined_data1)## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Sequence_Count
## 1 PL 3341367 T3 0 0 NA
## 2 HR P20210871 T1 0 0 NA
## 3 CR 20210284 A 0 0 NA
## 4 US 2021/0205309 A1 0 0 NA
## 5 JP 2021100972 A 3 0 NA
## 6 AU 2021/203768 A1 0 0 NA- Join the two datasets so that only the patents in the Sequence Counts dataset are included
train %>% right_join(sc, by = c("Patent_Number" = "Patent_No.")) -> joined_data2
head(joined_data2)## Patent_Number Cites_Patent_Count Cited_by_Patent_Count Sequence_Count
## 1 CA 189065 S 0 0 0
## 2 NI 202000072 A 0 0 0
## 3 KR 20210032013 A 0 0 80
## 4 PH 12020550461 A1 0 0 0
## 5 TW I722568 B 0 0 0
## 6 CN 112533674 A 0 2 0- Read the Training and Testing Data csv file into R. This file contains a binary column called Partition which is 0 when the patent was part of the training set used to develop the regression model in #2, and is equal to 1 otherwise. Use R to perform the following on this dataset:
tandt <- read.csv("C:/Users/justt/Desktop/School/621/Exams/Exam 1/Training and Testing Data.csv")
str(tandt)## 'data.frame': 725 obs. of 4 variables:
## $ Patent_Number : chr "PL 3341367 T3" "HR P20210871 T1" "CR 20210284 A" "US 2021/0205309 A1" ...
## $ Cites_Patent_Count : int 0 0 0 0 3 0 0 1 0 0 ...
## $ Cited_by_Patent_Count: int 0 0 0 0 0 0 0 0 0 0 ...
## $ Partition : int 0 0 0 0 0 0 0 0 0 0 ...- Group the dataset by Partition. Summarize each grouping by calculating the standard deviation of Cites_Patent_Count for each group.:::
by_part <- group_by(tandt, Partition)
Stan_dev <- summarise(by_part, cpc = sd(Cites_Patent_Count, na.rm = TRUE))
Stan_dev## # A tibble: 2 × 2
## Partition cpc
## <int> <dbl>
## 1 0 24.2
## 2 1 16.7arrange(Stan_dev, desc(cpc))## # A tibble: 2 × 2
## Partition cpc
## <int> <dbl>
## 1 0 24.2
## 2 1 16.7- Identify the top 3 patents with the highest number of citations in the Cited_by_Patent_Count column.
- Filter the dataset to display only those rows for which the Cites_Patent_Count is greater than zero.