Project 2
Daniela Ngassiki
2025-12-17
This project investigates whether there is a statistically significant difference in average body weight between male and female respondents. Understanding differences in body weight across sexes is important in fields such as public health, medicine, and human biology, as body weight is often associated with health outcomes and risk factors.
The dataset used in this analysis is bdims.csv, which contains 507 observations and 25 variables. Each observation represents an individual respondent, and the variables include demographic information (such as age and sex), body weight, height, and various body measurements. While the dataset includes many body dimension variables, this analysis focuses specifically on sex (coded as 0 = female, 1 = male) and weight (in kilograms), as these variables are directly relevant to the research question.
bdims <- read.csv("../bdims.csv")
str(bdims)## 'data.frame': 507 obs. of 25 variables:
## $ bia_di: num 42.9 43.7 40.1 44.3 42.5 43.3 43.5 44.4 43.5 42 ...
## $ bii_di: num 26 28.5 28.2 29.9 29.9 27 30 29.8 26.5 28 ...
## $ bit_di: num 31.5 33.5 33.3 34 34 31.5 34 33.2 32.1 34 ...
## $ che_de: num 17.7 16.9 20.9 18.4 21.5 19.6 21.9 21.8 15.5 22.5 ...
## $ che_di: num 28 30.8 31.7 28.2 29.4 31.3 31.7 28.8 27.5 28 ...
## $ elb_di: num 13.1 14 13.9 13.9 15.2 14 16.1 15.1 14.1 15.6 ...
## $ wri_di: num 10.4 11.8 10.9 11.2 11.6 11.5 12.5 11.9 11.2 12 ...
## $ kne_di: num 18.8 20.6 19.7 20.9 20.7 18.8 20.8 21 18.9 21.1 ...
## $ ank_di: num 14.1 15.1 14.1 15 14.9 13.9 15.6 14.6 13.2 15 ...
## $ sho_gi: num 106 110 115 104 108 ...
## $ che_gi: num 89.5 97 97.5 97 97.5 ...
## $ wai_gi: num 71.5 79 83.2 77.8 80 82.5 82 76.8 68.5 77.5 ...
## $ nav_gi: num 74.5 86.5 82.9 78.8 82.5 80.1 84 80.5 69 81.5 ...
## $ hip_gi: num 93.5 94.8 95 94 98.5 95.3 101 98 89.5 99.8 ...
## $ thi_gi: num 51.5 51.5 57.3 53 55.4 57.5 60.9 56 50 59.8 ...
## $ bic_gi: num 32.5 34.4 33.4 31 32 33 42.4 34.1 33 36.5 ...
## $ for_gi: num 26 28 28.8 26.2 28.4 28 32.3 28 26 29.2 ...
## $ kne_gi: num 34.5 36.5 37 37 37.7 36.6 40.1 39.2 35.5 38.3 ...
## $ cal_gi: num 36.5 37.5 37.3 34.8 38.6 36.1 40.3 36.7 35 38.6 ...
## $ ank_gi: num 23.5 24.5 21.9 23 24.4 23.5 23.6 22.5 22 22.2 ...
## $ wri_gi: num 16.5 17 16.9 16.6 18 16.9 18.8 18 16.5 16.9 ...
## $ age : int 21 23 28 23 22 21 26 27 23 21 ...
## $ wgt : num 65.6 71.8 80.7 72.6 78.8 74.8 86.4 78.4 62 81.6 ...
## $ hgt : num 174 175 194 186 187 ...
## $ sex : int 1 1 1 1 1 1 1 1 1 1 ...head(bdims)## bia_di bii_di bit_di che_de che_di elb_di wri_di kne_di ank_di sho_gi che_gi
## 1 42.9 26.0 31.5 17.7 28.0 13.1 10.4 18.8 14.1 106.2 89.5
## 2 43.7 28.5 33.5 16.9 30.8 14.0 11.8 20.6 15.1 110.5 97.0
## 3 40.1 28.2 33.3 20.9 31.7 13.9 10.9 19.7 14.1 115.1 97.5
## 4 44.3 29.9 34.0 18.4 28.2 13.9 11.2 20.9 15.0 104.5 97.0
## 5 42.5 29.9 34.0 21.5 29.4 15.2 11.6 20.7 14.9 107.5 97.5
## 6 43.3 27.0 31.5 19.6 31.3 14.0 11.5 18.8 13.9 119.8 99.9
## wai_gi nav_gi hip_gi thi_gi bic_gi for_gi kne_gi cal_gi ank_gi wri_gi age
## 1 71.5 74.5 93.5 51.5 32.5 26.0 34.5 36.5 23.5 16.5 21
## 2 79.0 86.5 94.8 51.5 34.4 28.0 36.5 37.5 24.5 17.0 23
## 3 83.2 82.9 95.0 57.3 33.4 28.8 37.0 37.3 21.9 16.9 28
## 4 77.8 78.8 94.0 53.0 31.0 26.2 37.0 34.8 23.0 16.6 23
## 5 80.0 82.5 98.5 55.4 32.0 28.4 37.7 38.6 24.4 18.0 22
## 6 82.5 80.1 95.3 57.5 33.0 28.0 36.6 36.1 23.5 16.9 21
## wgt hgt sex
## 1 65.6 174.0 1
## 2 71.8 175.3 1
## 3 80.7 193.5 1
## 4 72.6 186.5 1
## 5 78.8 187.2 1
## 6 74.8 181.5 1bdims$sex <- factor(bdims$sex,
levels = c(0, 1),
labels = c("Female", "Male"))
table(bdims$sex)##
## Female Male
## 260 247First I loaded and explored the dataset to see the number of colums, rows, labels, etc… .I also recoded the “sex” column so the categories are male/female. In the original dataset, male is 0 and female is 1.
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, unionbdims_clean <- bdims |>
select(sex, wgt)
bdims_clean## sex wgt
## 1 Male 65.6
## 2 Male 71.8
## 3 Male 80.7
## 4 Male 72.6
## 5 Male 78.8
## 6 Male 74.8
## 7 Male 86.4
## 8 Male 78.4
## 9 Male 62.0
## 10 Male 81.6
## 11 Male 76.6
## 12 Male 83.6
## 13 Male 90.0
## 14 Male 74.6
## 15 Male 71.0
## 16 Male 79.6
## 17 Male 93.8
## 18 Male 70.0
## 19 Male 72.4
## 20 Male 85.9
## 21 Male 78.8
## 22 Male 77.8
## 23 Male 66.2
## 24 Male 86.4
## 25 Male 81.8
## 26 Male 89.6
## 27 Male 82.8
## 28 Male 76.4
## 29 Male 63.2
## 30 Male 60.9
## 31 Male 74.8
## 32 Male 70.0
## 33 Male 72.4
## 34 Male 84.1
## 35 Male 69.1
## 36 Male 59.5
## 37 Male 67.2
## 38 Male 61.3
## 39 Male 68.6
## 40 Male 80.1
## 41 Male 87.8
## 42 Male 84.7
## 43 Male 73.4
## 44 Male 72.1
## 45 Male 82.6
## 46 Male 88.7
## 47 Male 84.1
## 48 Male 94.1
## 49 Male 74.9
## 50 Male 59.1
## 51 Male 75.6
## 52 Male 86.2
## 53 Male 75.3
## 54 Male 87.1
## 55 Male 55.2
## 56 Male 57.0
## 57 Male 61.4
## 58 Male 76.8
## 59 Male 86.8
## 60 Male 72.2
## 61 Male 71.6
## 62 Male 84.8
## 63 Male 68.2
## 64 Male 66.1
## 65 Male 72.0
## 66 Male 64.6
## 67 Male 74.8
## 68 Male 70.0
## 69 Male 101.6
## 70 Male 63.2
## 71 Male 79.1
## 72 Male 78.9
## 73 Male 67.7
## 74 Male 66.0
## 75 Male 68.2
## 76 Male 63.9
## 77 Male 72.0
## 78 Male 56.8
## 79 Male 74.5
## 80 Male 90.9
## 81 Male 93.0
## 82 Male 80.9
## 83 Male 72.7
## 84 Male 68.0
## 85 Male 70.9
## 86 Male 72.5
## 87 Male 72.5
## 88 Male 83.4
## 89 Male 75.5
## 90 Male 73.0
## 91 Male 70.2
## 92 Male 73.4
## 93 Male 70.5
## 94 Male 68.9
## 95 Male 102.3
## 96 Male 68.4
## 97 Male 65.9
## 98 Male 75.7
## 99 Male 84.5
## 100 Male 87.7
## 101 Male 86.4
## 102 Male 73.2
## 103 Male 53.9
## 104 Male 72.0
## 105 Male 55.5
## 106 Male 58.4
## 107 Male 83.2
## 108 Male 72.7
## 109 Male 64.1
## 110 Male 72.3
## 111 Male 65.0
## 112 Male 86.4
## 113 Male 65.0
## 114 Male 88.6
## 115 Male 84.1
## 116 Male 66.8
## 117 Male 75.5
## 118 Male 93.2
## 119 Male 82.7
## 120 Male 58.0
## 121 Male 79.5
## 122 Male 78.6
## 123 Male 71.8
## 124 Male 116.4
## 125 Male 72.2
## 126 Male 83.6
## 127 Male 85.5
## 128 Male 90.9
## 129 Male 85.9
## 130 Male 89.1
## 131 Male 75.0
## 132 Male 77.7
## 133 Male 86.4
## 134 Male 90.9
## 135 Male 73.6
## 136 Male 76.4
## 137 Male 69.1
## 138 Male 84.5
## 139 Male 64.5
## 140 Male 69.1
## 141 Male 108.6
## 142 Male 86.4
## 143 Male 80.9
## 144 Male 87.7
## 145 Male 94.5
## 146 Male 80.2
## 147 Male 72.0
## 148 Male 71.4
## 149 Male 72.7
## 150 Male 84.1
## 151 Male 76.8
## 152 Male 63.6
## 153 Male 80.9
## 154 Male 80.9
## 155 Male 85.5
## 156 Male 68.6
## 157 Male 67.7
## 158 Male 66.4
## 159 Male 102.3
## 160 Male 70.5
## 161 Male 95.9
## 162 Male 84.1
## 163 Male 87.3
## 164 Male 71.8
## 165 Male 65.9
## 166 Male 95.9
## 167 Male 91.4
## 168 Male 81.8
## 169 Male 96.8
## 170 Male 69.1
## 171 Male 82.7
## 172 Male 75.5
## 173 Male 79.5
## 174 Male 73.6
## 175 Male 91.8
## 176 Male 84.1
## 177 Male 85.9
## 178 Male 81.8
## 179 Male 82.5
## 180 Male 80.5
## 181 Male 70.0
## 182 Male 81.8
## 183 Male 84.1
## 184 Male 90.5
## 185 Male 91.4
## 186 Male 89.1
## 187 Male 85.0
## 188 Male 69.1
## 189 Male 73.6
## 190 Male 80.5
## 191 Male 82.7
## 192 Male 86.4
## 193 Male 67.7
## 194 Male 92.7
## 195 Male 93.6
## 196 Male 70.9
## 197 Male 75.0
## 198 Male 93.2
## 199 Male 93.2
## 200 Male 77.7
## 201 Male 61.4
## 202 Male 94.1
## 203 Male 75.0
## 204 Male 83.6
## 205 Male 85.5
## 206 Male 73.9
## 207 Male 66.8
## 208 Male 87.3
## 209 Male 72.3
## 210 Male 88.6
## 211 Male 75.5
## 212 Male 101.4
## 213 Male 91.1
## 214 Male 67.3
## 215 Male 77.7
## 216 Male 81.8
## 217 Male 75.5
## 218 Male 84.5
## 219 Male 76.6
## 220 Male 85.0
## 221 Male 102.5
## 222 Male 77.3
## 223 Male 71.8
## 224 Male 87.9
## 225 Male 94.3
## 226 Male 70.9
## 227 Male 64.5
## 228 Male 77.3
## 229 Male 72.3
## 230 Male 87.3
## 231 Male 80.0
## 232 Male 82.3
## 233 Male 73.6
## 234 Male 74.1
## 235 Male 85.9
## 236 Male 73.2
## 237 Male 76.3
## 238 Male 65.9
## 239 Male 90.9
## 240 Male 89.1
## 241 Male 62.3
## 242 Male 82.7
## 243 Male 79.1
## 244 Male 98.2
## 245 Male 84.1
## 246 Male 83.2
## 247 Male 83.2
## 248 Female 51.6
## 249 Female 59.0
## 250 Female 49.2
## 251 Female 63.0
## 252 Female 53.6
## 253 Female 59.0
## 254 Female 47.6
## 255 Female 69.8
## 256 Female 66.8
## 257 Female 75.2
## 258 Female 55.2
## 259 Female 54.2
## 260 Female 62.5
## 261 Female 42.0
## 262 Female 50.0
## 263 Female 49.8
## 264 Female 49.2
## 265 Female 73.2
## 266 Female 47.8
## 267 Female 68.8
## 268 Female 50.6
## 269 Female 82.5
## 270 Female 57.2
## 271 Female 87.8
## 272 Female 72.8
## 273 Female 54.5
## 274 Female 59.8
## 275 Female 67.3
## 276 Female 67.8
## 277 Female 47.0
## 278 Female 46.2
## 279 Female 55.0
## 280 Female 83.0
## 281 Female 54.4
## 282 Female 45.8
## 283 Female 53.6
## 284 Female 73.2
## 285 Female 52.1
## 286 Female 67.9
## 287 Female 56.6
## 288 Female 62.3
## 289 Female 58.5
## 290 Female 54.5
## 291 Female 50.2
## 292 Female 60.3
## 293 Female 58.3
## 294 Female 56.2
## 295 Female 50.2
## 296 Female 72.9
## 297 Female 59.8
## 298 Female 61.0
## 299 Female 69.1
## 300 Female 55.9
## 301 Female 46.5
## 302 Female 54.3
## 303 Female 54.8
## 304 Female 60.7
## 305 Female 60.0
## 306 Female 62.0
## 307 Female 60.3
## 308 Female 52.7
## 309 Female 74.3
## 310 Female 62.0
## 311 Female 73.1
## 312 Female 80.0
## 313 Female 54.7
## 314 Female 53.2
## 315 Female 75.7
## 316 Female 61.1
## 317 Female 55.7
## 318 Female 48.7
## 319 Female 52.3
## 320 Female 50.0
## 321 Female 59.3
## 322 Female 62.5
## 323 Female 55.7
## 324 Female 54.8
## 325 Female 45.9
## 326 Female 70.6
## 327 Female 67.2
## 328 Female 69.4
## 329 Female 58.2
## 330 Female 64.8
## 331 Female 71.6
## 332 Female 52.8
## 333 Female 59.8
## 334 Female 49.0
## 335 Female 50.0
## 336 Female 69.2
## 337 Female 55.9
## 338 Female 63.4
## 339 Female 58.2
## 340 Female 58.6
## 341 Female 45.7
## 342 Female 52.2
## 343 Female 48.6
## 344 Female 57.8
## 345 Female 55.6
## 346 Female 66.8
## 347 Female 59.4
## 348 Female 53.6
## 349 Female 73.2
## 350 Female 53.4
## 351 Female 69.0
## 352 Female 58.4
## 353 Female 56.2
## 354 Female 70.6
## 355 Female 59.8
## 356 Female 72.0
## 357 Female 65.2
## 358 Female 56.6
## 359 Female 105.2
## 360 Female 51.8
## 361 Female 63.4
## 362 Female 59.0
## 363 Female 47.6
## 364 Female 63.0
## 365 Female 55.2
## 366 Female 45.0
## 367 Female 54.0
## 368 Female 50.2
## 369 Female 60.2
## 370 Female 44.8
## 371 Female 58.8
## 372 Female 56.4
## 373 Female 62.0
## 374 Female 49.2
## 375 Female 67.2
## 376 Female 53.8
## 377 Female 54.4
## 378 Female 58.0
## 379 Female 59.8
## 380 Female 54.8
## 381 Female 43.2
## 382 Female 60.5
## 383 Female 46.4
## 384 Female 64.4
## 385 Female 48.8
## 386 Female 62.2
## 387 Female 55.5
## 388 Female 57.8
## 389 Female 54.6
## 390 Female 59.2
## 391 Female 52.7
## 392 Female 53.2
## 393 Female 64.5
## 394 Female 51.8
## 395 Female 56.0
## 396 Female 63.6
## 397 Female 63.2
## 398 Female 59.5
## 399 Female 56.8
## 400 Female 64.1
## 401 Female 50.0
## 402 Female 72.3
## 403 Female 55.0
## 404 Female 55.9
## 405 Female 60.4
## 406 Female 69.1
## 407 Female 84.5
## 408 Female 55.9
## 409 Female 55.5
## 410 Female 69.5
## 411 Female 76.4
## 412 Female 61.4
## 413 Female 65.9
## 414 Female 58.6
## 415 Female 66.8
## 416 Female 56.6
## 417 Female 58.6
## 418 Female 55.9
## 419 Female 59.1
## 420 Female 81.8
## 421 Female 70.7
## 422 Female 56.8
## 423 Female 60.0
## 424 Female 58.2
## 425 Female 72.7
## 426 Female 54.1
## 427 Female 49.1
## 428 Female 75.9
## 429 Female 55.0
## 430 Female 57.3
## 431 Female 55.0
## 432 Female 65.5
## 433 Female 65.5
## 434 Female 48.6
## 435 Female 58.6
## 436 Female 63.6
## 437 Female 55.2
## 438 Female 62.7
## 439 Female 56.6
## 440 Female 53.9
## 441 Female 63.2
## 442 Female 73.6
## 443 Female 62.0
## 444 Female 63.6
## 445 Female 53.2
## 446 Female 53.4
## 447 Female 55.0
## 448 Female 70.5
## 449 Female 54.5
## 450 Female 54.5
## 451 Female 55.9
## 452 Female 59.0
## 453 Female 63.6
## 454 Female 54.5
## 455 Female 47.3
## 456 Female 67.7
## 457 Female 80.9
## 458 Female 70.5
## 459 Female 60.9
## 460 Female 63.6
## 461 Female 54.5
## 462 Female 59.1
## 463 Female 70.5
## 464 Female 52.7
## 465 Female 62.7
## 466 Female 86.3
## 467 Female 66.4
## 468 Female 67.3
## 469 Female 63.0
## 470 Female 73.6
## 471 Female 62.3
## 472 Female 57.7
## 473 Female 55.4
## 474 Female 104.1
## 475 Female 55.5
## 476 Female 77.3
## 477 Female 80.5
## 478 Female 64.5
## 479 Female 72.3
## 480 Female 61.4
## 481 Female 58.2
## 482 Female 81.8
## 483 Female 63.6
## 484 Female 53.4
## 485 Female 54.5
## 486 Female 53.6
## 487 Female 60.0
## 488 Female 73.6
## 489 Female 61.4
## 490 Female 55.5
## 491 Female 63.6
## 492 Female 60.9
## 493 Female 60.0
## 494 Female 46.8
## 495 Female 57.3
## 496 Female 64.1
## 497 Female 63.6
## 498 Female 67.3
## 499 Female 75.5
## 500 Female 68.2
## 501 Female 61.4
## 502 Female 76.8
## 503 Female 71.8
## 504 Female 55.5
## 505 Female 48.6
## 506 Female 66.4
## 507 Female 67.3bdims_clean |>
group_by(sex) |>
summarise(
mean_weight = mean(wgt),
max_weight = max(wgt),
min_weight = min(wgt)
)## # A tibble: 2 × 4
## sex mean_weight max_weight min_weight
## <fct> <dbl> <dbl> <dbl>
## 1 Female 60.6 105. 42
## 2 Male 78.1 116. 53.9par(mfrow = c(1,2))
hist(bdims_clean$wgt[bdims$sex == "Female"], main = "Female Weight", xlab = "Weight")
hist(bdims$wgt[bdims_clean$sex == "Male"], main = "Male Weight", xlab = "Weight")
par(mfrow = c(1,1))
boxplot(wgt ~ sex,
data = bdims_clean,
main = "Body Weight by Sex",
xlab = "Sex",
ylab = "Weight (kg)")
To address the research question, exploratory data analysis (EDA) was
conducted to understand the distribution and characteristics of body
weight for males and females. The dataset was cleaned by converting the
sex variable into a labeled factor and selecting only the variables
relevant to the analysis. Summary statistics were calculated to compare
average weight by sex. Visualizations, including histograms and
boxplots, were created to examine the distribution of weight within each
group and to identify differences in center, spread, and potential
outliers. These steps help assess assumptions and provide context before
conducting the statistical test.
t.test(wgt ~ sex, data = bdims)##
## Welch Two Sample t-test
##
## data: wgt by sex
## t = -19.577, df = 495.27, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
## 95 percent confidence interval:
## -19.30486 -15.78344
## sample estimates:
## mean in group Female mean in group Male
## 60.60038 78.14453The following hypotheses were tested using a two-sample t-test:
Null hypothesis (H₀): μ₍Female₎ = μ₍Male₎
Alternative hypothesis (H₁): μ₍Female₎ ≠ μ₍Male₎
The significance level was set at α = 0.05.
The results indicate a statistically significant difference in mean body weight between females (M = 60.60 kg) and males (M = 78.14 kg), with a p-value less than 0.001. Because the p-value is smaller than the chosen significance level, the null hypothesis is rejected. This provides strong evidence that average body weight differs between males and females in this sample.
This analysis found a statistically significant difference in average body weight between male and female respondents, with males weighing more on average than females. The results directly answer the research question and are consistent with general biological differences observed between sexes.
These findings are relevant to public health and medical research, where sex-based differences in body weight may influence health risk assessments and treatment decisions. Future research could extend this analysis by examining how body weight interacts with other variables in the dataset, such as height, age, or specific body measurements. Additionally, analyzing body mass index (BMI) rather than weight alone could provide a more nuanced understanding of health-related differences.
list.files()## [1] "project 2.Rmd" "project-2_files" "project-2.Rmd"