Differences in students’ academic achievement is an essential phenomenon for sociological research to understand social stratification. This blog is going to analyze if there is any relationship between students’ academic achievement and their inherent characteristics such as parents’ economic condition, education, social capital, and race. To understand the pattern, randomly selected kindergarten student’s academic performance in math and reading and their parents’ socio-economical standing and race is going to be analyzed through one way ANOVA.
setwd("C:/Users/malia/OneDrive/Desktop/710 Memos")
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(foreign) # reads older version of Stata dataset
library(ggplot2) #creating plots and graphs
library(descr) # descriptive stats
library(lattice) # histograms for frequncy distribution
library(magrittr) # pipe operator
library(tidyverse) #importing, cleaning, re-coding, and analyzing data
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble 3.1.4 v purrr 0.3.4
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()
##
## Attaching package: 'sjmisc'
## The following object is masked from 'package:purrr':
##
## is_empty
## The following object is masked from 'package:tidyr':
##
## replace_na
## The following object is masked from 'package:tibble':
##
## add_case
## The following object is masked from 'package:descr':
##
## descr
library(haven) # Allows read.dta, which does not translate STATA data when uploading to R
library(expss) # Allows for more creative ways to make tables
##
## Use 'expss_output_rnotebook()' to display tables inside R Notebooks.
## To return to the console output, use 'expss_output_default()'.
##
## Attaching package: 'expss'
## The following objects are masked from 'package:haven':
##
## is.labelled, read_spss
## The following objects are masked from 'package:sjmisc':
##
## add_columns, add_rows, rec
## The following objects are masked from 'package:stringr':
##
## fixed, regex
## The following objects are masked from 'package:purrr':
##
## keep, modify, modify_if, transpose, when
## The following objects are masked from 'package:tidyr':
##
## contains, nest
## The following objects are masked from 'package:magrittr':
##
## and, equals, not, or
## The following object is masked from 'package:ggplot2':
##
## vars
## The following objects are masked from 'package:dplyr':
##
## between, compute, contains, first, last, na_if, recode, vars
library(Hmisc) # Calculates p-values for correlation coefficients
## Loading required package: survival
## Loading required package: Formula
## Registered S3 methods overwritten by 'Hmisc':
## method from
## [.labelled expss
## print.labelled expss
## as.data.frame.labelled expss
##
## Attaching package: 'Hmisc'
## The following object is masked from 'package:sjmisc':
##
## %nin%
## The following objects are masked from 'package:dplyr':
##
## src, summarize
## The following objects are masked from 'package:base':
##
## format.pval, units
school1<-read_dta("C:\\Users\\malia\\Downloads\\school1.dta")
school2<-read_dta("C:\\Users\\malia\\Downloads\\school2.dta")
childoutcomes1<-read_dta("C:\\Users\\malia\\Downloads\\childoutcomes1.dta")
schools<-rbind(school1,school2)
schools.data<- merge(schools,childoutcomes1 ,by="CHILDID", all= TRUE)
one.way <- aov(reading ~ race, data = schools.data)
summary(one.way)
## Df Sum Sq Mean Sq F value Pr(>F)
## race 1 262 262.36 2.846 0.092 .
## Residuals 740 68212 92.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 250 observations deleted due to missingness
## Aggregate function: calculating means by group
aggregate(x = schools.data$reading,
by = list(schools.data$race),
FUN = mean,
na.rm=TRUE)
## Group.1 x
## 1 1 36.49492
## 2 2 34.02132
## 3 3 32.03534
## 4 4 41.01810
one.way <- aov(WKSESL ~ math4, data = schools.data)
summary(one.way)
## Df Sum Sq Mean Sq F value Pr(>F)
## math4 1 69.7 69.65 125.9 <2e-16 ***
## Residuals 939 519.7 0.55
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 51 observations deleted due to missingness
## Aggregate function: calculating means by group
aggregate(x = schools.data$WKSESL,
by = list(schools.data$math4),
FUN = mean,
na.rm=TRUE)
## Group.1 x
## 1 1 -0.3881132
## 2 2 -0.0580000
## 3 3 0.2586580
## 4 4 0.3276923
schools.data%>%
select(race,math4)
## race math4
## 1 1 2
## 2 1 1
## 3 1 4
## 4 1 4
## 5 NA 2
## 6 1 4
## 7 4 4
## 8 1 1
## 9 1 2
## 10 1 4
## 11 1 4
## 12 1 3
## 13 1 3
## 14 1 1
## 15 NA 2
## 16 1 1
## 17 1 2
## 18 1 4
## 19 1 4
## 20 1 2
## 21 1 4
## 22 1 1
## 23 1 1
## 24 1 1
## 25 1 1
## 26 1 2
## 27 NA 4
## 28 1 3
## 29 NA 2
## 30 1 1
## 31 NA 3
## 32 NA 4
## 33 1 4
## 34 1 2
## 35 1 3
## 36 1 4
## 37 1 4
## 38 1 2
## 39 1 3
## 40 1 2
## 41 1 1
## 42 1 4
## 43 1 1
## 44 1 2
## 45 2 1
## 46 3 2
## 47 2 4
## 48 1 1
## 49 1 1
## 50 2 3
## 51 2 1
## 52 1 2
## 53 3 1
## 54 2 4
## 55 1 4
## 56 1 4
## 57 1 2
## 58 3 3
## 59 1 3
## 60 1 3
## 61 3 3
## 62 3 4
## 63 3 1
## 64 2 1
## 65 1 4
## 66 1 4
## 67 1 1
## 68 1 2
## 69 3 2
## 70 1 1
## 71 1 3
## 72 1 3
## 73 NA 4
## 74 1 4
## 75 1 1
## 76 4 4
## 77 NA 3
## 78 1 3
## 79 1 4
## 80 1 2
## 81 1 4
## 82 1 4
## 83 1 4
## 84 1 2
## 85 1 4
## 86 1 4
## 87 4 2
## 88 1 4
## 89 1 3
## 90 1 3
## 91 3 1
## 92 1 2
## 93 NA 1
## 94 NA 2
## 95 NA 4
## 96 4 1
## 97 1 1
## 98 1 1
## 99 1 4
## 100 1 4
## 101 1 2
## 102 1 3
## 103 3 2
## 104 1 3
## 105 1 1
## 106 3 1
## 107 2 3
## 108 2 2
## 109 1 3
## 110 1 2
## 111 1 4
## 112 1 2
## 113 1 1
## 114 NA 2
## 115 1 3
## 116 1 4
## 117 1 4
## 118 3 2
## 119 1 4
## 120 2 3
## 121 NA 1
## 122 3 2
## 123 3 1
## 124 1 4
## 125 1 4
## 126 1 4
## 127 1 4
## 128 1 4
## 129 1 3
## 130 1 2
## 131 1 3
## 132 3 1
## 133 2 2
## 134 NA 1
## 135 1 3
## 136 NA 3
## 137 1 3
## 138 1 4
## 139 1 2
## 140 NA 4
## 141 3 1
## 142 2 3
## 143 NA 1
## 144 4 4
## 145 2 1
## 146 2 1
## 147 3 1
## 148 2 1
## 149 3 1
## 150 3 1
## 151 1 1
## 152 NA 1
## 153 NA 4
## 154 2 1
## 155 2 1
## 156 4 3
## 157 3 2
## 158 NA 1
## 159 NA 1
## 160 3 1
## 161 2 2
## 162 1 4
## 163 1 2
## 164 1 2
## 165 1 2
## 166 3 1
## 167 4 2
## 168 2 3
## 169 2 4
## 170 2 2
## 171 2 1
## 172 NA 3
## 173 3 2
## 174 1 4
## 175 3 3
## 176 2 1
## 177 2 2
## 178 NA 1
## 179 NA 1
## 180 4 1
## 181 1 4
## 182 NA 1
## 183 1 2
## 184 2 2
## 185 1 4
## 186 1 4
## 187 1 2
## 188 1 1
## 189 1 4
## 190 1 4
## 191 1 4
## 192 1 1
## 193 1 1
## 194 NA 4
## 195 NA 1
## 196 NA 3
## 197 3 4
## 198 1 3
## 199 NA 2
## 200 NA 3
## 201 NA 3
## 202 1 3
## 203 3 2
## 204 NA 1
## 205 NA 1
## 206 1 3
## 207 NA 2
## 208 1 1
## 209 1 2
## 210 2 2
## 211 3 3
## 212 2 1
## 213 2 1
## 214 4 4
## 215 1 3
## 216 3 2
## 217 1 4
## 218 NA 1
## 219 3 3
## 220 3 2
## 221 1 3
## 222 3 2
## 223 1 4
## 224 1 4
## 225 1 2
## 226 2 4
## 227 2 3
## 228 NA 2
## 229 1 3
## 230 1 3
## 231 1 4
## 232 NA 1
## 233 NA 1
## 234 2 4
## 235 1 4
## 236 1 2
## 237 2 2
## 238 NA 2
## 239 2 1
## 240 1 2
## 241 1 3
## 242 1 2
## 243 1 3
## 244 1 4
## 245 1 4
## 246 1 4
## 247 1 3
## 248 1 3
## 249 1 3
## 250 1 3
## 251 1 4
## 252 2 2
## 253 1 1
## 254 NA 2
## 255 1 4
## 256 1 3
## 257 1 4
## 258 3 1
## 259 1 3
## 260 1 3
## 261 1 4
## 262 1 4
## 263 1 4
## 264 1 2
## 265 1 3
## 266 1 3
## 267 NA 4
## 268 NA 2
## 269 2 2
## 270 1 4
## 271 NA 1
## 272 2 1
## 273 1 2
## 274 1 3
## 275 1 3
## 276 1 4
## 277 1 4
## 278 1 4
## 279 1 3
## 280 1 1
## 281 1 4
## 282 1 4
## 283 1 2
## 284 NA 1
## 285 1 1
## 286 3 1
## 287 1 1
## 288 NA 3
## 289 NA 3
## 290 1 3
## 291 1 3
## 292 1 4
## 293 1 3
## 294 1 2
## 295 1 1
## 296 1 1
## 297 1 3
## 298 2 1
## 299 1 3
## 300 NA 1
## 301 NA 4
## 302 1 3
## 303 1 2
## 304 1 4
## 305 1 4
## 306 1 1
## 307 1 2
## 308 1 4
## 309 1 1
## 310 1 4
## 311 1 2
## 312 1 2
## 313 1 1
## 314 1 2
## 315 1 4
## 316 2 2
## 317 2 1
## 318 2 1
## 319 NA 1
## 320 2 4
## 321 NA 4
## 322 1 1
## 323 1 2
## 324 1 2
## 325 2 3
## 326 1 4
## 327 1 3
## 328 NA 1
## 329 1 3
## 330 2 2
## 331 4 4
## 332 1 3
## 333 1 1
## 334 1 4
## 335 1 3
## 336 1 3
## 337 1 2
## 338 1 4
## 339 3 1
## 340 1 3
## 341 1 4
## 342 1 2
## 343 3 2
## 344 1 4
## 345 1 4
## 346 3 3
## 347 1 3
## 348 1 3
## 349 1 2
## 350 1 2
## 351 1 3
## 352 NA 3
## 353 1 4
## 354 1 4
## 355 1 2
## 356 2 1
## 357 1 2
## 358 1 2
## 359 NA 4
## 360 1 4
## 361 2 4
## 362 2 1
## 363 1 1
## 364 1 2
## 365 2 2
## 366 1 3
## 367 1 4
## 368 1 3
## 369 1 4
## 370 1 3
## 371 1 4
## 372 1 2
## 373 1 2
## 374 1 3
## 375 4 4
## 376 4 4
## 377 1 4
## 378 1 3
## 379 1 2
## 380 1 1
## 381 1 2
## 382 1 3
## 383 1 1
## 384 NA 2
## 385 NA 1
## 386 1 2
## 387 3 3
## 388 1 2
## 389 1 4
## 390 1 2
## 391 1 1
## 392 4 1
## 393 1 4
## 394 1 2
## 395 NA 4
## 396 NA 3
## 397 1 2
## 398 1 1
## 399 NA 1
## 400 1 3
## 401 NA 4
## 402 1 2
## 403 1 2
## 404 1 1
## 405 1 1
## 406 4 1
## 407 1 4
## 408 3 1
## 409 1 2
## 410 NA 2
## 411 NA 1
## 412 1 3
## 413 1 4
## 414 1 4
## 415 1 3
## 416 1 4
## 417 1 2
## 418 NA 1
## 419 1 2
## 420 4 4
## 421 1 3
## 422 1 3
## 423 1 4
## 424 NA 4
## 425 1 1
## 426 1 2
## 427 1 4
## 428 NA 1
## 429 NA 1
## 430 1 3
## 431 1 3
## 432 1 4
## 433 1 4
## 434 1 3
## 435 1 3
## 436 2 1
## 437 NA 1
## 438 1 3
## 439 1 2
## 440 NA 4
## 441 NA 1
## 442 1 1
## 443 1 3
## 444 NA 1
## 445 1 3
## 446 NA 1
## 447 1 2
## 448 1 2
## 449 1 2
## 450 1 4
## 451 1 2
## 452 1 3
## 453 1 4
## 454 1 4
## 455 NA 4
## 456 1 3
## 457 1 4
## 458 3 4
## 459 1 2
## 460 1 3
## 461 1 3
## 462 1 3
## 463 1 3
## 464 2 3
## 465 2 4
## 466 1 2
## 467 1 4
## 468 1 3
## 469 1 3
## 470 1 3
## 471 1 3
## 472 1 3
## 473 2 1
## 474 3 4
## 475 2 4
## 476 3 1
## 477 2 4
## 478 1 4
## 479 1 3
## 480 1 3
## 481 1 4
## 482 1 4
## 483 1 3
## 484 4 2
## 485 1 2
## 486 1 3
## 487 1 3
## 488 4 1
## 489 1 1
## 490 NA 2
## 491 1 3
## 492 4 4
## 493 1 4
## 494 1 4
## 495 1 3
## 496 1 1
## 497 1 1
## 498 1 1
## 499 1 2
## 500 1 3
## 501 3 2
## 502 4 2
## 503 1 2
## 504 1 4
## 505 1 4
## 506 NA 4
## 507 NA 4
## 508 NA 3
## 509 4 4
## 510 1 3
## 511 1 1
## 512 1 1
## 513 NA 3
## 514 NA 4
## 515 NA 3
## 516 1 3
## 517 1 2
## 518 1 4
## 519 1 2
## 520 1 4
## 521 1 2
## 522 1 2
## 523 1 3
## 524 1 3
## 525 1 2
## 526 1 4
## 527 NA 3
## 528 NA 3
## 529 NA 4
## 530 4 4
## 531 3 1
## 532 3 3
## 533 2 1
## 534 2 1
## 535 NA 1
## 536 NA 1
## 537 NA 1
## 538 2 2
## 539 2 1
## 540 2 1
## 541 1 4
## 542 1 4
## 543 1 4
## 544 NA 1
## 545 2 4
## 546 NA 3
## 547 2 2
## 548 1 1
## 549 3 2
## 550 3 1
## 551 3 1
## 552 1 1
## 553 NA 4
## 554 1 4
## 555 1 4
## 556 2 4
## 557 1 1
## 558 1 1
## 559 2 2
## 560 NA 3
## 561 NA 3
## 562 1 4
## 563 1 3
## 564 1 2
## 565 1 2
## 566 1 3
## 567 1 3
## 568 4 3
## 569 1 4
## 570 1 4
## 571 1 3
## 572 1 1
## 573 1 1
## 574 1 3
## 575 1 1
## 576 NA 2
## 577 1 4
## 578 1 2
## 579 3 4
## 580 1 4
## 581 NA 1
## 582 4 1
## 583 1 3
## 584 NA 1
## 585 NA 2
## 586 1 4
## 587 1 4
## 588 1 2
## 589 1 2
## 590 1 2
## 591 1 2
## 592 NA 2
## 593 1 2
## 594 1 4
## 595 1 1
## 596 1 4
## 597 1 4
## 598 1 2
## 599 1 3
## 600 1 2
## 601 1 1
## 602 1 1
## 603 2 4
## 604 NA 4
## 605 1 2
## 606 4 3
## 607 NA 4
## 608 NA 4
## 609 1 3
## 610 1 4
## 611 NA 4
## 612 1 2
## 613 1 4
## 614 4 3
## 615 2 1
## 616 1 3
## 617 1 2
## 618 1 4
## 619 1 1
## 620 1 1
## 621 4 1
## 622 NA 1
## 623 4 1
## 624 2 3
## 625 1 3
## 626 1 4
## 627 1 2
## 628 NA 3
## 629 1 4
## 630 1 2
## 631 1 3
## 632 NA 2
## 633 1 2
## 634 1 3
## 635 1 4
## 636 1 4
## 637 1 3
## 638 1 1
## 639 1 4
## 640 1 4
## 641 1 2
## 642 1 1
## 643 1 4
## 644 1 3
## 645 1 3
## 646 1 4
## 647 1 2
## 648 1 4
## 649 NA 4
## 650 1 1
## 651 1 4
## 652 1 1
## 653 1 4
## 654 NA 1
## 655 1 4
## 656 1 2
## 657 1 3
## 658 1 2
## 659 1 4
## 660 1 2
## 661 1 3
## 662 NA 2
## 663 1 4
## 664 1 3
## 665 NA 2
## 666 1 3
## 667 1 3
## 668 2 2
## 669 2 2
## 670 1 2
## 671 1 4
## 672 1 4
## 673 1 4
## 674 1 3
## 675 1 2
## 676 1 1
## 677 1 3
## 678 1 2
## 679 2 2
## 680 1 4
## 681 1 3
## 682 1 2
## 683 1 2
## 684 1 4
## 685 1 1
## 686 1 3
## 687 1 4
## 688 1 4
## 689 NA 4
## 690 NA 3
## 691 1 4
## 692 1 2
## 693 1 4
## 694 1 2
## 695 1 4
## 696 1 1
## 697 1 4
## 698 1 4
## 699 1 3
## 700 1 1
## 701 1 4
## 702 1 3
## 703 2 1
## 704 1 4
## 705 1 3
## 706 2 3
## 707 1 4
## 708 1 2
## 709 1 4
## 710 1 4
## 711 1 3
## 712 1 4
## 713 NA 3
## 714 NA 3
## 715 NA 4
## 716 NA 3
## 717 1 4
## 718 1 3
## 719 1 1
## 720 3 3
## 721 NA 4
## 722 NA 3
## 723 NA 1
## 724 NA 4
## 725 1 1
## 726 1 3
## 727 NA 2
## 728 NA 1
## 729 NA 1
## 730 NA 1
## 731 NA 1
## 732 NA 1
## 733 NA 1
## 734 NA 2
## 735 NA 1
## 736 NA 3
## 737 NA 3
## 738 NA 1
## 739 NA 3
## 740 NA 4
## 741 NA 1
## 742 NA 2
## 743 NA 2
## 744 1 2
## 745 NA 3
## 746 NA 2
## 747 1 3
## 748 3 1
## 749 NA 4
## 750 1 2
## 751 1 2
## 752 NA 2
## 753 NA 2
## 754 2 2
## 755 NA 2
## 756 1 3
## 757 NA 4
## 758 1 2
## 759 NA 3
## 760 NA 1
## 761 1 2
## 762 2 2
## 763 NA 2
## 764 3 1
## 765 1 3
## 766 1 2
## 767 1 1
## 768 2 1
## 769 1 3
## 770 1 4
## 771 2 1
## 772 2 1
## 773 1 2
## 774 1 1
## 775 1 4
## 776 1 3
## 777 1 3
## 778 NA 4
## 779 NA 3
## 780 NA 1
## 781 NA 1
## 782 NA 1
## 783 NA 4
## 784 1 3
## 785 1 1
## 786 NA 2
## 787 NA 2
## 788 4 4
## 789 1 4
## 790 3 2
## 791 3 1
## 792 NA 1
## 793 NA 1
## 794 NA 4
## 795 NA 4
## 796 4 4
## 797 1 4
## 798 1 4
## 799 NA 4
## 800 1 4
## 801 2 1
## 802 2 3
## 803 1 3
## 804 3 1
## 805 NA 1
## 806 1 2
## 807 1 2
## 808 1 2
## 809 NA 1
## 810 2 2
## 811 2 4
## 812 1 3
## 813 NA 4
## 814 1 4
## 815 1 4
## 816 1 4
## 817 3 2
## 818 2 1
## 819 4 1
## 820 2 1
## 821 2 2
## 822 2 2
## 823 1 1
## 824 1 2
## 825 1 3
## 826 1 2
## 827 1 3
## 828 1 3
## 829 1 4
## 830 2 2
## 831 1 2
## 832 1 1
## 833 1 2
## 834 NA 2
## 835 NA 4
## 836 1 4
## 837 1 2
## 838 NA 2
## 839 NA 4
## 840 NA 4
## 841 NA 1
## 842 NA 1
## 843 NA 2
## 844 NA 2
## 845 NA 4
## 846 NA 2
## 847 NA 1
## 848 NA 1
## 849 NA 1
## 850 1 4
## 851 1 4
## 852 1 3
## 853 NA 1
## 854 2 4
## 855 2 4
## 856 2 3
## 857 1 4
## 858 1 3
## 859 1 2
## 860 NA 3
## 861 1 4
## 862 1 3
## 863 NA 1
## 864 2 3
## 865 2 3
## 866 2 1
## 867 1 4
## 868 1 4
## 869 2 2
## 870 1 4
## 871 1 4
## 872 2 3
## 873 NA 3
## 874 1 3
## 875 4 1
## 876 1 3
## 877 1 3
## 878 1 2
## 879 1 4
## 880 2 3
## 881 2 4
## 882 1 2
## 883 2 3
## 884 2 2
## 885 2 3
## 886 1 4
## 887 2 4
## 888 2 1
## 889 1 2
## 890 NA 1
## 891 3 1
## 892 1 2
## 893 1 4
## 894 1 4
## 895 NA 4
## 896 1 4
## 897 1 2
## 898 2 2
## 899 1 3
## 900 1 3
## 901 1 3
## 902 3 1
## 903 1 4
## 904 1 3
## 905 1 3
## 906 2 1
## 907 NA 1
## 908 1 3
## 909 1 2
## 910 NA 4
## 911 1 4
## 912 NA 2
## 913 3 1
## 914 1 1
## 915 1 4
## 916 1 2
## 917 1 2
## 918 3 1
## 919 2 2
## 920 3 4
## 921 1 3
## 922 1 4
## 923 2 4
## 924 1 2
## 925 NA 3
## 926 1 2
## 927 3 2
## 928 1 3
## 929 3 3
## 930 2 2
## 931 2 1
## 932 1 3
## 933 4 3
## 934 NA 3
## 935 1 4
## 936 1 4
## 937 1 3
## 938 NA 4
## 939 2 2
## 940 1 2
## 941 1 1
## 942 4 4
## 943 NA 1
## 944 1 4
## 945 NA 3
## 946 NA 3
## 947 NA 4
## 948 NA 1
## 949 4 2
## 950 2 4
## 951 NA 2
## 952 NA 2
## 953 3 1
## 954 3 3
## 955 1 4
## 956 3 3
## 957 3 2
## 958 3 2
## 959 3 1
## 960 3 2
## 961 2 1
## 962 2 1
## 963 2 4
## 964 3 3
## 965 NA 2
## 966 3 4
## 967 3 3
## 968 3 4
## 969 3 1
## 970 2 4
## 971 NA 4
## 972 3 1
## 973 1 4
## 974 1 4
## 975 1 4
## 976 1 3
## 977 1 3
## 978 NA 2
## 979 1 4
## 980 1 4
## 981 2 3
## 982 1 2
## 983 NA 1
## 984 NA 1
## 985 NA 1
## 986 NA 2
## 987 NA 1
## 988 NA 2
## 989 2 1
## 990 1 4
## 991 NA 3
## 992 NA 3
crosstab(schools.data$race,schools.data$math4,prop.r = T, chisq = T, dnn=c("Race", "Math Score"))
## Cell Contents
## |-------------------------|
## | Count |
## | Row Percent |
## |-------------------------|
##
## ==============================================
## Math Score
## Race 1 2 3 4 Total
## ----------------------------------------------
## 1 80 136 157 197 570
## 14.0% 23.9% 27.5% 34.6% 72.2%
## ----------------------------------------------
## 2 40 31 19 22 112
## 35.7% 27.7% 17.0% 19.6% 14.2%
## ----------------------------------------------
## 3 32 20 14 8 74
## 43.2% 27.0% 18.9% 10.8% 9.4%
## ----------------------------------------------
## 4 10 5 5 14 34
## 29.4% 14.7% 14.7% 41.2% 4.3%
## ----------------------------------------------
## Total 162 192 195 241 790
## ==============================================
##
## Statistics for All Table Factors
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 71.37265 d.f. = 9 p = 8.19e-12
##
## Minimum expected frequency: 6.972152
correlate<-data.frame(schools.data$WKSESL,schools.data$reading)
correlate2<-rcorr(as.matrix(correlate))
correlate2
## schools.data.WKSESL schools.data.reading
## schools.data.WKSESL 1.0 0.4
## schools.data.reading 0.4 1.0
##
## n
## schools.data.WKSESL schools.data.reading
## schools.data.WKSESL 941 809
## schools.data.reading 809 842
##
## P
## schools.data.WKSESL schools.data.reading
## schools.data.WKSESL 0
## schools.data.reading 0