Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
20.02 13900 235 1.183 21.40 14050 237 1.188 21.40 14050 237 1.208 21.46 14050 234 1.209 21.46 14150 232 1.209 21.44 14350 237 1.208 21.22 14350 237 1.206 21.46 14400 232 1.209 21.62 14400 232 1.211 21.68 14350 231 1.204 20.98 14350 231 1.211 20.12 14650 236 1.208 19.90 14550 231 1.213 20.98 14500 228 1.207 21.10 14500 229 1.207 20.66 14500 235 1.228 21.38 14500 235 1.227 21.30 14650 243 1.229 21.60 14650 243 1.225 21.50 15050 256 1.217 21.56 14950 253 1.208 21.66 15100 256 1.212 21.60 15150 255 1.209 21.86 15200 256 1.207 21.74 15200 256 1.206 21.74 14650 243 1.198 21.78 14850 248 1.197 21.54 14900 250 1.195 21.70 14650 250 1.197 21.58 14650 244 1.197 21.40 14400 239 1.189 21.52 14650 244 1.19 21.82 14450 241 1.19 23.00 14750 249 1.186 23.40 14800 250 1.186 23.96 14850 253 1.193 24.24 14800 251 1.191 24.50 14800 252 1.188 24.54 15000 257 1.196 24.58 14950 255 1.19 24.38 15000 256 1.185 23.82 15100 256 1.188 24.00 15100 256 1.195 23.96 15150 269 1.192 24.00 14850 263 1.202 24.00 14850 263 1.202 23.30 14800 264 1.191 22.94 14800 264 1.191 23.28 14600 261 1.192 23.38 14500 265 1.192 23.20 14625 267 1.192 23.10 14625 267 1.195 23.14 14600 268 1.203 23.08 14575 269 1.207 23.36 14550 266 1.219 23.76 14600 275 1.217 23.36 14550 273 1.214 23.34 14550 273 1.207 23.78 14850 282 1.206 24.08 15075 286 1.197 23.42 14950 282 1.203 23.36 15075 286 1.208 23.00 14950 282 1.201 22.96 15500 304 1.21 22.92 15425 304 1.21 23.00 15175 298 1.206 23.54 15325 302 1.222 24.18 15600 313 1.226 24.40 15650 322 1.231 24.22 15650 322 1.218 24.40 15875 334 1.21 24.38 15700 331 1.211 23.78 15700 331 1.213 24.38 16025 340 1.209 24.56 15700 331 1.225 24.58 16025 340 1.235 24.18 15900 313 1.235 24.82 16250 331 1.232 25.58 16200 315 1.237 25.12 16275 321 1.243 25.64 16275 321 1.243 25.20 16700 351 1.241 25.28 17000 360 1.254 25.90 16850 343 1.264 26.00 17000 346 1.262 25.88 17150 348 1.259 25.88 17150 346 1.269 25.64 17575 359 1.276 25.86 17850 360 1.278 25.94 17850 375 1.272 25.84 17775 352 1.291 24.98 17050 330 1.283 23.76 17425 337 1.282 23.46 17075 323 1.288 22.58 17050 310 1.277 22.40 16150 299 1.277 22.34 16200 301 1.275 20.76 16650 322 1.284 21.52 16650 322 1.285 21.04 16300 314 1.276 21.68 16325 318 1.276 22.64 16225 315 1.283 23.04 16200 318 1.287 22.00 15900 305 1.274 22.58 15900 305 1.282 22.34 15750 298 1.296 22.88 15750 295 1.285 22.52 15525 289 1.279 21.90 15575 289 1.274 21.60 15500 284 1.266 20.92 15550 281 1.257 20.78 14900 260 1.257 20.58 14400 245 1.256 19.09 14500 250 1.261 19.16 14500 250 1.265 20.20 14450 249 1.259 19.86 14300 246 1.255 19.79 14600 256 1.263 20.10 14950 263 1.258 19.75 14800 254 1.256 19.60 14900 259 1.257 19.96 14950 258 1.257 19.86 14800 257 1.253 19.74 14775 256 1.271 19.84 14775 256 1.279 20.40 15475 274 1.279 20.88 15450 275 1.279 21.20 15650 278 1.274 21.28 15675 278 1.278 20.64 15750 275 1.275 20.96 16025 284 1.274 21.14 16300 286 1.272 21.06 16750 291 1.269 20.94 17050 291 1.266 21.36 16650 279 1.254 20.52 16050 266 1.253 20.16 16175 274 1.248 19.71 15850 274 1.264 19.89 15500 266 1.268 20.46 15675 272 1.263 20.16 15775 274 1.264 19.84 15900 282 1.259 20.08 15975 281 1.274 20.16 15925 282 1.266 20.24 16175 288 1.277 20.24 16275 299 1.276 20.00 16150 298 1.278 19.81 16100 300 1.279 20.08 16000 298 1.285 19.93 16000 298 1.284 19.87 15950 298 1.288 20.06 16050 303 1.286 19.59 15800 292 1.278 19.50 15700 296 1.272 19.41 15650 298 1.273 19.24 15600 301 1.279 19.62 15350 295 1.288 19.83 15375 298 1.292 20.22 15575 302 1.281 20.68 15555 301 1.281 21.38 15600 307 1.283 21.26 15600 306 1.276 21.52 15550 306 1.281 21.64 15350 297 1.282 21.36 15300 299 1.285 22.56 15450 299 1.282 22.28 15550 314 1.285 22.74 15600 317 1.281 22.22 15750 324 1.279 22.48 15650 321 1.273 22.70 15650 321 1.271 22.94 15800 321 1.271 23.02 15050 293 1.271 22.90 14950 279 1.268 22.92 14900 277 1.272 22.42 14900 278 1.268 22.52 14500 266 1.267 22.06 14700 272 1.265 22.34 14700 275 1.268 22.48 14450 267 1.273 22.96 14550 270 1.282 23.00 14750 278 1.277 23.06 14750 278 1.268 22.90 14700 275 1.271 22.98 14950 283 1.266 23.14 15100 288 1.269 22.92 15150 287 1.274 22.96 15200 287 1.268 23.06 14950 284 1.272 23.48 14450 267 1.266 23.50 14250 268 1.262 23.34 14350 274 1.254 23.54 14650 281 1.254 23.16 14650 283 1.253 23.18 14550 280 1.255 23.46 14550 280 1.252 23.48 14650 284 1.252 23.48 15100 292 1.254 24.06 15200 297 1.256 24.06 15050 294 1.262 24.64 15000 296 1.256 24.54 15200 301 1.254 24.72 14950 292 1.258 23.96 15200 301 1.265 24.54 14950 292 1.268 24.66 14950 297 1.272 24.90 14950 299 1.276 25.06 15150 301 1.277 24.70 15100 299 1.276 24.72 15200 303 1.27 24.78 15450 307 1.276 24.64 15600 309 1.278 24.54 15800 312 1.286 24.32 15650 311 1.283 24.60 15650 312 1.282 24.38 15450 308 1.279 24.26 15750 322 1.277 24.34 15500 315 1.284 24.24 15550 315 1.281 24.96 15500 318 1.289 24.94 15450 315 1.295 24.98 15500 315 1.308 24.70 15500 314 1.311 24.34 15600 319 1.315 24.48 15550 318 1.316 24.68 15550 318 1.320 24.04 15600 325 1.324 24.02 15550 324 1.331 24.22 15500 327 1.333 24.12 15450 326 1.327 24.18 15600 332 1.330 24.22 15500 333 1.328 23.46 15450 332 1.318 22.80 15350 324 1.324 23.30 15100 321 1.327 22.70 15250 330 1.319 22.76 15200 329 1.311 23.32 15200 330 1.310 23.44 15150 329 1.316 23.46 15200 329 1.320 23.18 15250 331 1.318 23.32 15050 311 1.319 23.14 15050 303 1.316 24.32 15050 302 1.317 25.40 15050 299 1.317 25.34 15000 297 1.327 25.74 15250 304 1.323 25.94 15300 306 1.311 25.60 15400 308 1.308 25.56 15400 308 1.301 25.54 15400 310 1.302 25.62 15400 313 1.299 25.78 15250 301 1.298 25.52 15200 305 1.289 25.80 14950 293 1.294 25.94 14950 293 1.295 25.90 15050 300 1.291 24.46 15050 300 1.292 23.72 15150 303 1.296 24.00 15405 314 1.294 24.06 15450 312 1.304 24.44 15450 307 1.301 25.34 15600 312 1.298 25.30 15450 307 1.290 25.00 15650 312 1.292 25.18 15650 315 1.297 25.02 15800 319 1.295 25.36 15950 322 1.302 25.44 15950 325 1.302 25.74 16000 326 1.293 25.56 15850 321 1.296 25.94 15850 331 1.299 26.82 16050 328 1.299 26.32 16100 330 1.301 26.76 16000 327 1.296 26.50 16100 329 1.302 26.92 16100 329 1.308 27.12 16150 332 1.314 27.44 16200 335 1.312 27.52 16300 335 1.313 28.34 16300 333 1.315 29.40 16350 337 1.315 28.52 16250 337 1.311 28.52 16200 335 1.313 28.22 16350 337 1.316 28.16 16300 336 1.323 28.26 16050 331 1.321 27.84 16450 340 1.323 27.36 16450 343 1.316 27.92 16600 351 1.308 27.36 16600 351 1.310 27.60 16200 342 1.314 27.81 16200 339 1.315 27.40 16050 327 1.316 27.58 16200 339 1.316 26.36 16050 327 1.322 25.87 16050 327 1.318 25.36 15650 310 1.323 26.11 15800 311 1.333 25.40 15850 311 1.330 25.40 15750 311 1.330 25.67 15500 302 1.329 26.66 15650 309 1.335 26.16 15550 308 1.333 26.01 15700 312 1.327 26.07 15650 312 1.335 24.73 15800 315 1.335 25.50 15800 315 1.335 25.34 15850 317 1.332 25.79 15850 314 1.337 26.17 15850 314 1.336 26.19 15850 316 1.335 26.63 15850 314 1.337 26.49 15850 316 1.343 26.47 15850 315 1.342 26.84 15850 314 1.347 26.66 15900 317 1.353 26.91 16100 320 1.355 26.62 16100 320 1.355 26.92 16050 325 1.358 27.60 16050 326 1.360 27.52 16000 325 1.361 27.58 16000 324 1.356 28.30 16150 328 1.358 28.25 16200 327 1.365 28.15 16150 325 1.360 27.92 16150 325 1.364 28.28 16050 318 1.361 27.97 16100 323 1.359 27.62 16200 323 1.361 27.51 16000 318 1.356 28.05 16000 319 1.362 27.31 16000 319 1.356 27.60 15800 307 1.354 28.07 15850 310 1.353 29.90 15800 308 1.349 29.52 15750 306 1.355 29.71 16050 312 1.354 30.05 16150 313 1.357 30.30 16150 313 1.352 30.80 16150 313 1.348 31.03 16050 310 1.344 30.22 15800 305 1.345 30.82 15850 305 1.349 31.35 15850 308 1.345 30.93 15850 308 1.344 30.84 15750 306 1.345 30.90 15500 298 1.351 30.63 15600 302 1.342 30.78 15700 305 1.345 31.14 15700 303 1.344 30.90 15750 306 1.348 31.81 15550 301 1.353 31.52 15550 301 1.351 31.27 15600 303 1.347 31.18 15650 309 1.335 31.36 15600 310 1.336 31.45 15750 316 1.335 31.24 15900 320 1.329 31.72 15850 320 1.330 31.76 15700 315 1.331 31.58 15850 320 1.340 31.76 15700 315 1.340 30.56 15600 310 1.343 29.90 15650 312 1.340 30.54 15500 308 1.344 31.05 15600 310 1.346 30.94 15650 310 1.346 31.26 15700 313 1.344 31.92 15650 310 1.347 32.70 15700 312 1.351 32.59 15650 310 1.359 32.50 15550 308 1.360 32.63 15500 305 1.362 32.16 15450 301 1.364 31.60 15250 285 1.360 31.43 15300 288 1.362 31.32 15400 292 1.367 31.01 15300 289 1.375 31.44 15450 292 1.379 32.25 15300 291 1.378 32.01 15300 291 1.378 32.90 15300 291 1.377 34.59 15300 294 1.378 34.40 15450 295 1.382 34.71 15400 297 1.380 34.84 15350 295 1.382 34.02 15500 299 1.383 33.79 15400 299 1.374 34.92 15350 296 1.372 35.30 15400 295 1.365 35.25 15500 301 1.366 35.64 15650 303 1.371 35.39 15750 305 1.366 35.99 15700 302 1.366 35.72 15700 302 1.369 36.36 15700 302 1.382 34.62 15700 302 1.379 34.34 15450 293 1.379 33.18 15450 296 1.373 33.05 15450 295 1.365 33.20 15500 298 1.365 33.60 15500 298 1.359 33.33 15550 299 1.348 32.90 15450 296 1.341 32.64 15550 298 1.345 31.49 15550 299 1.348 32.02 15500 291 1.351 32.08 15650 295 1.349 31.20 15700 296 1.357 30.16 15800 294 1.362 30.58 15800 293 1.366 30.60 15800 293 1.366 29.96 15550 276 1.363 28.06 15600 274 1.361 27.96 15550 269 1.371 29.34 15650 274 1.363 30.28 15550 270 1.358 31.62 15550 275 1.359 32.58 15550 271 1.367 32.74 15550 271 1.370 32.95 15550 273 1.380 31.89 15600 274 1.382 32.80 15700 278 1.389 33.22 15700 270 1.390 33.62 15750 279 1.386 34.34 15950 282 1.388 33.84 16200 286 1.387 34.02 16200 286 1.398 33.70 16250 285 1.403 33.05 16450 289 1.405 32.42 16300 285 1.411 32.50 16300 285 1.411 32.15 16350 286 1.413 32.20 16550 289 1.418 31.89 16550 289 1.418 31.23 16550 293 1.423 31.56 16750 305 1.417 32.66 16650 304 1.420 32.30 16500 300 1.411 32.51 16550 300 1.414 33.32 16400 299 1.409 32.26 16600 302 1.404 33.62 16650 306 1.415 34.00 16550 300 1.420 33.52 16550 298 1.417 33.80 16350 295 1.423 34.50 16650 298 1.415 35.18 16700 299 1.420 35.07 16650 297 1.430 34.91 16750 302 1.429 35.57 16700 303 1.417 35.60 16850 306 1.425 35.77 16900 308 1.423 35.78 17100 308 1.431 35.25 17000 302 1.438 36.31 16950 303 1.439 35.28 17200 304 1.441 36.09 16950 296 1.445 35.00 16950 299 1.442 34.75 16950 299 1.448 33.65 17100 300 1.449 34.36 17300 309 1.455 34.20 17300 309 1.472 34.65 17500 313 1.467 35.32 17350 310 1.468 36.53 17600 314 1.458 35.31 17450 308 1.461 34.40 17700 315 1.470 33.62 17950 323 1.464 34.40 18350 343 1.465 34.30 18200 328 1.465 34.87 18100 332 1.479 34.33 17850 327 1.481 34.03 17850 327 1.483 33.05 17650 317 1.481 32.16 17700 323 1.485 31.27 17250 312 1.487 31.41 17200 313 1.475 30.44 17000 306 1.474 30.78 17000 306 1.476 29.64 17300 312 1.467 29.57 17450 309 1.474 28.38 17800 315 1.472 29.51 17450 309 1.455 30.76 17800 315 1.465 30.64 17800 315 1.472 30.32 17250 304 1.467 31.18 17200 301 1.468 31.62 17200 303 1.468 31.99 17200 303 1.451 31.50 17350 307 1.439 30.75 17450 310 1.442 31.07 17400 309 1.439 31.11 17400 309 1.435 31.51 17500 314 1.438 31.51 17600 313 1.440 31.45 17450 305 1.452 30.91 17450 305 1.469 30.52 17550 303 1.472 31.40 17550 303 1.469 30.76 17850 306 1.475 30.74 17800 312 1.473 30.24 17800 312 1.472 32.97 17950 314 1.471 32.88 18100 319 1.468 32.88 18100 319 1.466 33.63 18200 318 1.479 33.72 18200 319 1.490 34.00 18550 326 1.489 33.74 18800 329 1.479 32.85 18600 326 1.469 31.93 18600 325 1.467 31.66 19300 342 1.448 31.40 19050 334 1.449 31.13 19300 344 1.457 30.85 19300 344 1.466 30.71 19300 346 1.471 30.60 19350 340 1.476 29.55 19200 338 1.477 28.29 19100 338 1.481 28.47 19300 346 1.487 27.71 18950 333 1.489 26.30 19400 345 1.483 26.70 19450 344 1.469 26.30 19900 349 1.462 27.90 19950 351 1.457 28.73 20050 354 1.451 29.14 19900 353 1.454 30.65 19850 353 1.454 29.80 19900 356 1.459 30.42 19600 355 1.463 31.48 19400 352 1.467 31.67 19450 350 1.464 30.60 19750 356 1.474 31.59 19750 356 1.466 31.31 20250 350 1.474 30.98 20250 378 1.485 30.70 19800 366 1.482 31.88 19900 375 1.487 32.43 19800 371 1.504 30.31 19700 365 1.512 29.64 19750 367 1.517 30.18 20100 374 1.520 31.21 20450 381 1.521 31.82 20350 378 1.520 32.24 20400 384 1.532 32.01 20050 381 1.542 32.18 20350 402 1.534 33.21 20200 400 1.538 33.57 20450 409 1.548 32.66 20600 416 1.558 33.79 20650 418 1.556 33.81 20650 418 1.577 33.82 20250 404 1.577 34.88 20300 415 1.569 33.45 20200 415 1.542 32.65 20200 401 1.557 31.60 20200 402 1.571 32.02 20250 410 1.579 32.90 20375 416 1.580 32.68 20700 422 1.581 33.09 20250 399 1.566 31.03 20100 397 1.563 31.80 19150 364 1.553 31.36 19150 364 1.572 30.31 19000 351 1.569 31.31 19200 359 1.569 31.84 19250 364 1.573 32.47 19100 363 1.588 32.21 18850 353 1.583 32.96 18600 341 1.587 32.85 18150 339 1.583 32.68 18400 345 1.593 32.01 18350 346 1.587 32.35 18600 357 1.578 33.12 18650 357 1.590 32.61 18500 351 1.593 32.58 18775 358 1.594 33.00 18625 355 1.577 33.30 18600 346 1.560 32.71 18760 353 1.563 33.11 18700 353 1.557 33.44 18900 363 1.554 33.79 18950 363 1.546 33.93 18500 354 1.546 33.84 18450 345 1.553 34.20 18350 347 1.543 31.75 18200 338 1.535 31.12 18075 335 1.546 30.55 18200 339 1.543 31.84 18150 339 1.547 33.55 18150 339 1.544 34.31 17575 327 1.547 35.25 17850 337 1.550 35.09 17950 338 1.558 34.70 18050 341 1.564 34.26 18050 337 1.575 34.75 18050 337 1.576 33.30 18250 343 1.574 34.14 18050 345 1.576 35.06 17900 339 1.576 35.03 17800 335 1.566 33.96 18125 339 1.555 34.83 18550 344 1.551 35.84 18600 344 1.552 35.53 18750 353 1.559 37.10 18850 360 1.547 35.85 18600 357 1.540 34.77 18750 362 1.560 33.95 18750 363 1.578 34.20 18450 348 1.553 34.30 18450 348 1.552 35.09 18400 350 1.542 35.16 18200 338 1.534 34.96 18300 339 1.546 36.02 18200 339 1.548 35.97 18150 340 1.549 36.41 18025 346 1.548 35.84 18200 348 1.561 35.33 18200 348 1.552 35.33 18175 343 1.557 35.05 17950 337 1.560 35.26 18025 336 1.573 34.48 18050 336 1.575 34.96 18150 347 1.576 34.98 18150 345 1.578 33.79 18300 349 1.581 33.14 18475 351 1.589 33.34 18550 351 1.567 33.31 18175 338 1.565 32.27 18200 335 1.569 32.07 18025 335 1.572 31.37 18500 342 1.571 31.17 18750 349 1.584 31.40 18700 346 1.585 29.90 19000 360 1.599 29.90 19000 360 1.589 28.98 18950 362 1.585 28.84 18850 358 1.582 30.04 18850 355 1.586 29.56 18700 355 1.592 30.59 18850 361 1.574 29.71 18850 361 1.568 28.73 19350 371 1.573 29.25 19450 376 1.575 28.12 19500 372 1.571 27.81 19400 373 1.559 29.04 19300 366 1.561 29.00 19300 360 1.557 29.41 19400 364 1.557 29.50 19400 355 1.549 29.49 18800 349 1.548 27.89 18925 350 1.547 28.77 18900 347 1.507 28.70 18900 349 1.501 28.82 18750 347 1.491 28.79 18650 352 1.490 28.94 18625 355 1.491 26.89 18700 353 1.473 24.98 18225 338 1.470 25.52 18100 335 1.468 26.39 18250 336 1.473 26.52 18200 331 1.481 25.00 18350 320 1.481 25.85 17350 297 1.477 25.70 17450 308 1.460 25.70 17825 314 1.477 26.36 17050 263 1.477 26.34 17325 280 1.474 26.22 17125 269 1.462 25.47 17600 280 1.452 25.40 17700 283 1.444 28.41 17875 290 1.449 28.90 17775 282 1.425 28.76 17775 285 1.421 29.15 17950 290 1.414 29.99 17950 290 1.409 29.57 18050 290 1.393 29.97 18100 290 1.407 29.55 17700 282 1.415 29.29 17700 280 1.427 28.68 17650 275 1.422 27.13 17800 279 1.450 25.68 18050 272 1.424 27.04 18050 272 1.457 25.58 17550 247 1.473 25.57 17550 247 1.469 25.60 17150 238 1.470 26.67 17300 237 1.464 25.64 17300 232 1.435 24.96 17550 232 1.430 23.14 19150 257 1.408 22.12 19150 265 1.390 24.59 19100 270 1.383 24.70 19300 282 1.363 23.21 19200 278 1.363 22.68 19300 284 1.373 22.41 19075 281 1.368 21.46 19300 284 1.358 19.71 20050 283 1.364 21.59 19900 269 1.375 20.93 19900 277 1.363 20.32 19400 254 1.351 20.09 19450 252 1.340 18.20 20350 257 1.342 17.52 20850 264 1.318 16.47 20850 265 1.284 16.80 21500 277 1.281 16.03 20350 241 1.260 17.82 19650 247 1.246 17.71 19850 250 1.253 16.77 20100 236 1.277 15.16 19150 225 1.304 14.83 18950 223 1.276 14.57 19000 229 1.282 14.98 18800 239 1.282 13.76 18225 231 1.287 12.87 17650 226 1.277 12.59 18650 227 1.289 11.25 19175 228 1.275 11.60 18700 221 1.253 12.50 18550 239 1.253 13.80 18300 230 1.268 13.96 18200 243 1.266 13.72 18400 242 1.265 15.30 18400 242 1.263 14.69 18700 246 1.254 13.17 18600 247 1.260 13.28 18760 248 1.277 14.76 18650 241 1.281 14.16 18325 232 1.294 13.43 18300 225 1.290 14.14 18850 237 1.273 14.34 18650 229 1.261 13.14 18650 235 1.270 13.27 18875 230 1.262 12.01 19100 225 1.262 10.27 19850 233 1.267 10.35 20250 251 1.285 11.55 20000 245 1.284 11.89 20125 246 1.293 12.79 20100 246 1.322 13.89 20400 250 1.334 14.11 19450 229 1.351 12.55 19600 231 1.369 12.59 19325 232 1.368 12.73 19200 231 1.367 13.23 19150 236 1.406 12.00 19125 241 1.462 13.07 19225 238 1.394 13.55 19850 243 1.397 13.92 19450 237 1.398 13.80 19650 240 1.401 12.92 19450 240 1.427 13.78 19300 246 1.410 14.06 19125 241 1.392 14.18 19050 238 1.387 13.61 19250 244 1.358 13.65 19250 239 1.333 13.00 19250 239 1.360 12.90 19200 228 1.362 13.05 19850 236 1.368 13.53 19900 239 1.339 14.20 19650 240 1.326 14.07 19650 240 1.317 14.88 20075 249 1.309 15.22 20050 255 1.327 15.94 20050 252 1.318 15.34 20350 260 1.293 15.14 19775 250 1.291 14.74 20000 253 1.298 15.13 20300 260 1.280 14.60 19700 249 1.299 14.15 19850 251 1.317 13.21 19675 246 1.326 13.22 19800 249 1.311 12.50 20100 261 1.282 12.42 20350 264 1.276 13.17 21000 268 1.285 13.05 20900 270 1.282 13.26 21150 274 1.283 14.12 22100 287 1.280 14.10 21900 286 1.301 14.55 21550 283 1.297 14.42 21475 280 1.294 14.61 22450 296 1.283 13.90 22750 303 1.282 14.52 22450 300 1.277 15.25 22000 295 1.263 14.91 22550 305 1.260 15.75 22850 315 1.271 15.49 22450 316 1.259 15.55 22250 313 1.280 15.95 22550 318 1.276 14.38 23275 331 1.280 14.68 23250 326 1.278 15.16 23550 333 1.264 14.25 24150 343 1.260 14.23 24650 355 1.262 13.93 24725 357 1.256 13.50 24750 344 1.255 13.10 24400 340 1.256 12.68 24800 348 1.266 12.48 23750 328 1.264 12.60 23750 328 1.260 12.05 23925 331 1.256 11.96 24275 325 1.257 11.89 23250 308 1.278 13.19 23250 311 1.279 12.13 23100 320 1.278 12.12 23600 323 1.291 12.08 23600 319 1.304 12.98 23000 310 1.294 13.14 22600 304 1.313 13.24 22825 308 1.367 12.96 22800 307 1.355 13.13 22800 308 1.356 12.94 22500 301 1.351 12.88 22200 293 1.349 13.27 22100 290 1.361 13.61 22350 304 1.330 14.19 22250 309 1.319 14.30 21950 306 1.331 14.10 21900 302 1.325 14.42 21950 305 1.339 14.25 21950 304 1.343 13.26 22350 308 1.350 13.91 22100 302 1.326 13.43 22200 299 1.323 14.35 22100 298 1.327 14.95 21450 291 1.328 14.09 20700 280 1.317 13.83 20900 278 1.320 14.05 21450 284 1.306 15.55 21450 284 1.297 16.50 21100 280 1.293 16.51 21450 291 1.295 16.40 21450 292 1.305 16.92 21500 295 1.323 15.68 21250 282 1.313 16.51 21300 278 1.299 16.85 21800 285 1.327 16.68 21750 283 1.328 17.07 21875 290 1.322 16.85 22050 296 1.340 15.53 22200 304 1.332 15.68 21950 297 1.336 14.98 21950 297 1.343 16.12 21625 289 1.357 16.72 21200 281 1.368 17.53 21350 287 1.362 17.54 21500 296 1.356 17.37 21600 307 1.352 16.48 21850 318 1.349 16.01 21700 317 1.361 14.98 21400 313 1.369 15.17 21350 311 1.377 15.69 21550 319 1.397 16.18 21650 312 1.401 16.28 21650 315 1.391 16.80 22100 318 1.390 16.15 21650 312 1.386 16.40 21650 312 1.410 16.33 21750 318 1.422 16.09 21750 317 1.424 16.52 21700 319 1.421 16.34 21750 320 1.410 16.88 21650 318 1.418 17.56 21850 326 1.387 17.90 21950 334 1.396 17.33 21950 334 1.410 16.63 21950 339 1.397 17.00 22000 346 1.400 16.95 21675 329 1.385 16.85 22000 346 1.389 17.45 21850 335 1.384 18.08 21800 327 1.392 18.25 21700 332 1.393 17.28 21700 332 1.390 16.51 21600 334 1.386 15.61 21400 320 1.398 15.33 21500 315 1.403 15.89 21500 313 1.394 15.25 21425 314 1.410 15.38 21350 311 1.406 16.09 21400 308 1.413 15.88 21050 301 1.410 15.90 20900 300 1.405 16.15 20900 300 1.401 16.17 21350 307 1.390 16.57 21250 304 1.402 16.10 21300 303 1.390 15.66 21050 295 1.399 15.19 21200 296 1.390 15.64 21200 291 1.398 14.79 21200 291 1.399 15.02 21100 289 1.409 14.60 21150 286 1.413 15.00 20850 275 1.409 15.36 20850 280 1.422 16.19 20800 270 1.422 16.28 20950 279 1.419 16.24 21100 281 1.423 16.62 21200 286 1.423 16.80 21150 286 1.427 16.52 21250 295 1.423 16.93 21250 295 1.410 17.12 21250 290 1.405 17.23 21250 290 1.414 16.76 21300 293 1.430 16.90 21350 299 1.438 17.45 21300 300 1.441 18.32 21100 293 1.437 18.56 21025 288 1.436 18.66 21150 294 1.420 18.52 21350 302 1.417 19.25 21100 300 1.417 18.78 21350 310 1.429 18.50 21300 310 1.429 18.20 21300 310 1.407 18.31 21450 315 1.410 19.50 21350 311 1.412 19.07 21300 309 1.411 18.01 21250 314 1.418 18.26 21325 322 1.424 18.56 21225 312 1.433 18.83 21175 305 1.432 18.96 21125 299 1.432 19.19 21125 300 1.427 19.03 21025 295 1.427 18.52 21225 306 1.436 18.55 21050 302 1.427 18.88 21075 307 1.431 18.89 21125 305 1.422 18.30 21075 305 1.434 17.80 21300 313 1.426 18.02 21150 318 1.433 18.23 21400 321 1.447 18.72 21800 331 1.452 19.29 22150 346 1.455 19.12 22050 346 1.459 19.15 22150 354 1.456 19.57 22000 351 1.461 19.53 21750 346 1.467 19.76 21850 354 1.471 20.33 21900 347 1.471 20.42 21800 348 1.466 20.26 21950 360 1.478 19.73 22050 366 1.476 19.76 21950 360 1.477 19.58 21750 346 1.478 19.47 21800 354 1.477 19.50 21900 356 1.467 20.26 21800 350 1.465 20.36 21550 336 1.455 20.46 21600 333 1.464 20.21 21600 339 1.454 19.51 21700 342 1.454 19.49 21950 348 1.462 20.59 21350 344 1.472 20.40 21850 342 1.469 21.06 22050 351 1.476 21.16 22550 362 1.475 21.72 22800 372 1.477 21.80 22700 368 1.486 22.34 22700 372 1.488 22.23 22725 370 1.486 22.39 22850 371 1.487 22.56 22600 367 1.492 22.40 22350 357 1.497 22.10 22475 360 1.492 21.10 22650 366 1.500 21.95 22550 359 1.502 21.68 22450 361 1.502 21.26 22500 362 1.487 20.80 22325 362 1.479 21.50 22250 354 1.479 20.75 22250 354 1.480 21.28 22350 345 1.477 21.00 22500 344 1.466 21.95 22550 341 1.476 21.72 22875 343 1.487 21.67 23400 358 1.486 23.06 23300 357 1.498 22.85 23375 361 1.497 23.32 23550 364 1.504 23.06 23400 357 1.492 23.02 23550 357 1.487 23.82 23800 361 1.497 24.21 23675 356 1.488 24.32 24000 362 1.496 23.71 24200 374 1.486 23.61 24350 379 1.482 24.09 24375 380 1.497 23.36 24450 382 1.497 23.84 24800 387 1.508 22.89 24900 383 1.507 22.99 25050 384 1.492 22.67 25075 377 1.502 23.73 24550 368 1.507 24.08 24850 370 1.509 23.64 25050 376 1.512 23.58 25650 392 1.507 23.26 25700 389 1.479 23.00 25400 373 1.477 23.38 24750 367 1.477 23.21 24900 367 1.473 23.63 24450 357 1.476 23.88 24400 363 1.465 23.83 24550 352 1.454 24.02 24450 351 1.456 23.39 24400 352 1.434 23.45 24650 356 1.434 23.74 25000 375 1.437 23.68 24500 358 1.428 23.71 24750 361 1.428 23.75 24400 354 1.440 23.83 24250 355 1.441 24.12 24475 374 1.443 23.58 24600 364 1.434 23.40 24450 361 1.441 24.90 24300 362 1.439 25.06 24375 361 1.444 25.10 24375 361 1.435 24.92 24550 356 1.430 25.46 24725 364 1.427 25.89 24825 375 1.453 25.39 25100 391 1.448 25.38 24950 379 1.456 25.25 25325 389 1.449 24.88 25325 387 1.437 25.00 24800 379 1.437 25.00 24975 385 1.428 24.07 25125 399 1.413 23.60 25125 388 1.406 23.18 25125 392 1.414 23.25 25400 395 1.415 23.04 25175 395 1.409 22.77 24650 370 1.407 22.25 24775 376 1.400 22.41 24675 367 1.397 22.50 24825 356 1.391 22.91 24775 362 1.394 22.88 24675 346 1.398 21.69 24750 347 1.385 21.19 24875 350 1.369 21.56 25400 361 1.368 22.00 25400 358 1.376 22.13 24750 334 1.374 22.27 24900 331 1.372 22.30 24825 337 1.357 21.94 24875 337 1.361 22.40 24975 341 1.365 22.77 25375 344 1.373 22.90 25600 342 1.357 23.03 26000 353 1.352 23.05 25900 355 1.363 22.41 25850 354 1.358 22.26 26075 353 1.355 21.90 26275 365 1.349 22.01 26050 366 1.357 22.62 26000 354 1.353 22.76 25825 353 1.355 23.40 26075 363 1.364 23.63 26150 368 1.367 24.05 26275 366 1.358 23.82 26475 376 1.366 23.71 26500 383 1.356 23.95 26575 382 1.361 23.61 26425 386 1.366 23.98 26275 389 1.377 23.56 26375 388 1.371 23.99 25900 382 1.372 24.33 25850 379 1.376 24.48 25625 374 1.366 24.31 25900 380 1.355 24.38 26050 383 1.347 24.63 26150 384 1.352 25.54 26275 385 1.334 25.75 26100 378 1.336 25.73 25975 378 1.335 25.85 25975 378 1.347 25.78 26125 383 1.348 25.86 26175 381 1.348 26.86 26225 382 1.347 27.36 26225 382 1.340 27.38 26200 390 1.334 26.58 26275 401 1.330 27.65 26275 392 1.338 27.73 26275 401 1.359 27.18 26750 406 1.358 27.32 27075 408 1.362 27.30 27475 421 1.354 26.90 27525 412 1.354 26.70 27125 407 1.343 26.75 27000 411 1.349 26.41 26950 405 1.337 26.29 27075 416 1.334 27.51 27150 410 1.331 27.91 26875 397 1.332 27.70 26925 409 1.329 27.28 27150 406 1.325 28.25 27150 406 1.326 27.62 27425 412 1.332 27.30 27625 426 1.324 25.94 27475 423 1.309 24.99 28075 425 1.292 25.50 28075 425 1.273 24.42 28175 423 1.275 26.58 28350 434 1.297 25.84 28350 434 1.270 26.76 28500 440 1.267 26.74 29350 422 1.259 26.68 30225 431 1.249 25.55 29575 442 1.235 26.40 30125 448 1.243 25.19 30125 448 1.227 23.94 31150 475 1.233 24.20 31350 481 1.250 24.20 32175 488 1.236 23.07 31725 476 1.222 24.07 31600 474 1.231 25.02 30800 455 1.226 24.65 30800 455 1.238 24.68 29700 434 1.231 24.63 30875 447 1.216 24.49 31275 455 1.222 25.05 31500 459 1.227 24.31 31375 465 1.206 23.90 31400 464 1.196 23.68 31650 468 1.194 24.50 31975 467 1.201 25.22 31650 468 1.205 25.48 31975 467 1.213 26.00 32575 454 1.225 26.07 32025 470 1.226 26.06 33050 477 1.228 26.22 32300 462 1.236 26.70 32100 467 1.237 27.20 32250 469 1.239 26.77 32050 467 1.226 26.11 31975 469 1.227 25.43 32100 466 1.226 24.99 32025 469 1.229 25.51 32275 482 1.234 24.00 32100 474 1.220 23.86 32275 477 1.227 22.96 31975 468 1.233 23.41 32175 469 1.255 23.17 32375 480 1.253 24.12 32300 474 1.258 23.87 32450 474 1.257 24.27 32425 471 1.266 24.40 30800 443 1.264 24.16 30850 441 1.257 25.15 30750 440 1.257 25.09 30175 436 1.270 24.60 30350 442 1.283 24.33 30125 437 1.300 24.14 30625 444 1.296 24.36 30375 440 1.284 25.40 30425 444 1.282 26.15 30325 440 1.285 26.77 29825 438 1.290 26.94 29450 427 1.293 26.33 29100 421 1.303 26.24 29450 424 1.299 26.23 29550 422 1.307 25.88 29575 436 1.303 27.00 29425 435 1.307 26.91 29050 433 1.322 27.15 28525 420 1.321 27.78 28575 417 1.318 28.73 28500 411 1.318 28.83 28875 430 1.325 28.68 28625 428 1.313 27.56 28625 428 1.302 27.15 28925 426 1.279 27.41 28925 432 1.280 27.47 28950 426 1.282 28.76 28950 426 1.286 28.47 29100 424 1.288 27.94 29700 424 1.284 27.23 30000 431 1.271 27.01 30400 441 1.270 26.15 30375 444 1.261 26.11 30425 447 1.261 27.20 30625 440 1.269 27.36 30700 442 1.271 27.33 30825 439 1.270 27.43 30800 439 1.268 28.92 31100 453 1.280 29.45 31175 459 1.282 29.01 31025 462 1.283 29.25 30975 466 1.287 29.14 31025 464 1.274 29.64 31350 473 1.270 30.40 31075 469 1.272 30.62 31125 474 1.273 31.25 30900 480 1.280 31.75 31150 482 1.285 31.30 31575 486 1.299 30.70 31575 486 1.308 31.03 31375 484 1.306 31.46 31100 484 1.307 31.28 30975 487 1.312 31.03 31200 490 1.336 30.95 31125 491 1.332 31.17 31075 495 1.341 31.29 31275 497 1.348 31.91 31175 496 1.346 32.10 30950 489 1.361 31.71 30725 485 1.365 31.90 30900 490 1.373 32.02 30700 493 1.371 32.65 30625 487 1.378 33.77 30700 495 1.386 33.51 30650 506 1.397 34.26 30525 492 1.387 34.21 30850 500 1.394 34.13 30725 499 1.383 34.73 31025 518 1.396 34.73 30975 517 1.410 34.57 30550 505 1.409 34.80 30900 522 1.390 33.98 31000 519 1.386 34.40 31000 519 1.386 34.21 31000 522 1.402 34.61 31000 519 1.393 35.25 31325 543 1.403 35.23 31000 537 1.391 35.00 31000 537 1.380 34.52 30300 520 1.386 33.82 30575 523 1.386 34.35 30575 526 1.393 34.81 30775 537 1.402 34.96 30550 534 1.401 36.69 30750 536 1.424 36.42 31025 557 1.408 36.44 31000 554 1.392 37.41 30850 550 1.395 36.40 30600 549 1.377 36.15 31150 575 1.370 35.78 31800 595 1.371 36.95 32500 636 1.363 36.14 32325 628 1.361 36.36 31800 594 1.348 37.31 31850 590 1.365 37.58 31625 590 1.367 38.00 31750 605 1.365 37.23 31650 618 1.350 37.00 31525 630 1.334 37.87 32075 638 1.332 37.70 32725 645 1.323 36.17 32900 640 1.315 36.56 32775 640 1.300 37.70 32825 634 1.312 38.77 33200 647 1.316 39.02 34100 682 1.325 39.88 33800 679 1.328 39.56 33525 677 1.336 38.52 33775 697 1.320 37.20 34000 702 1.321 38.58 33425 679 1.324 39.41 33550 685 1.327 39.08 33400 695 1.344 38.81 33300 685 1.336 38.73 33400 688 1.324 38.70 33000 685 1.326 39.23 33500 694 1.315 39.82 33550 694 1.316 39.97 33725 697 1.311 40.37 33700 700 1.306 39.54 33600 695 1.310 39.21 33550 693 1.314 39.07 33500 693 1.320 39.78 34200 722 1.314 39.40 34000 725 1.328 38.92 33600 715 1.336
Names of X columns:
Umi Goud Zilver Eur-Dol
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation