crece<-read.table("growth.csv", sep = ",", header=T)
crece
## horas masa
## 1 1416 0.1481
## 2 1416 0.1954
## 3 1416 0.2204
## 4 1416 0.1207
## 5 1419 0.2920
## 6 1419 0.3892
## 7 1419 0.3837
## 8 1419 0.2994
## 9 1422 0.2892
## 10 1422 0.1347
## 11 1422 0.2254
## 12 1440 0.7405
## 13 1440 0.4069
## 14 1440 0.1213
## 15 1440 0.2477
## 16 1442 0.3641
## 17 1442 0.4780
## 18 1442 0.3243
## 19 1442 0.7489
## 20 1444 0.8562
## 21 1444 0.5270
## 22 1444 0.4817
## 23 1444 0.3918
## 24 1446 0.3449
## 25 1446 0.3349
## 26 1446 0.6067
## 27 1446 0.5888
## 28 1464 1.0516
## 29 1464 0.4802
## 30 1466 1.7754
## 31 1466 1.4030
## 32 1468 0.9107
## 33 1468 0.5893
## 34 1470 1.2156
## 35 1470 1.3592
## 36 1488 1.8306
## 37 1488 1.6102
## 38 1490 1.6628
## 39 1490 1.3939
## 40 1492 1.8325
## 41 1492 1.7633
## 42 1494 1.2809
## 43 1494 1.7984
## 44 1512 2.0020
## 45 1512 1.4276
## 46 1514 1.9924
## 47 1514 1.8784
## 48 1516 1.2773
## 49 1516 1.2808
## 50 1518 1.7115
## 51 1518 1.9192
## 52 1536 1.6359
## 53 1536 2.8007
## 54 1538 2.7597
## 55 1538 2.2568
## 56 1540 1.9971
## 57 1540 1.4329
## 58 1542 2.8216
## 59 1542 2.4322
## 60 1562 2.8792
## 61 1562 1.7876
## 62 1566 1.8798
## 63 1566 2.0045
## 64 1586 2.6742
## 65 1586 2.9516
## 66 1590 2.4495
## 67 1590 2.9953
## 68 1610 2.3608
## 69 1614 3.1235
## 70 1638 3.1728
## 71 1662 3.0080
## 72 1686 3.9043
## 73 1710 3.8536
## 74 1734 4.9471
## 75 1758 3.3347
## 76 1782 4.9931
## 77 1464 0.9834
## 78 1464 1.2531
## 79 1466 0.8680
## 80 1466 1.1197
## 81 1468 1.0167
## 82 1468 1.3540
## 83 1470 1.8440
## 84 1470 1.1583
## 85 1488 1.4967
## 86 1488 1.4209
## 87 1490 1.5471
## 88 1490 0.7599
## 89 1492 2.4725
## 90 1492 1.0097
## 91 1494 1.7194
## 92 1494 1.9900
## 93 1512 1.7484
## 94 1512 0.8221
## 95 1514 2.1259
## 96 1514 1.5582
## 97 1516 1.1170
## 98 1516 1.3397
## 99 1518 1.6475
## 100 1518 1.3037
## 101 1536 1.6586
## 102 1536 1.6086
## 103 1538 2.1021
## 104 1538 2.2497
## 105 1540 1.6863
## 106 1540 2.4327
## 107 1542 1.5602
## 108 1542 1.8178
## 109 1562 1.9473
## 110 1562 2.1227
## 111 1566 2.5310
## 112 1566 2.1990
## 113 1586 2.7956
## 114 1586 1.3617
## 115 1590 2.5855
## 116 1590 1.8011
## 117 1610 1.8224
## 118 1614 2.2646
## 119 1638 3.1698
## 120 1662 3.0866
## 121 1686 2.3654
## 122 1710 2.1446
## 123 1734 3.5218
## 124 1758 4.2132
## 125 1782 3.4072
attach(crece)
plot (horas, masa)
fit1<-lm(masa~horas)
fit1
##
## Call:
## lm(formula = masa ~ horas)
##
## Coefficients:
## (Intercept) horas
## -14.87072 0.01085
fit1$coefficients
## (Intercept) horas
## -14.87071687 0.01085168
fit1$residuals
## 1 2 3 4 5
## -0.3471596724 -0.2998596724 -0.2748596724 -0.3745596724 -0.2358147074
## 6 7 8 9 10
## -0.1386147074 -0.1441147074 -0.2284147074 -0.2711697425 -0.4256697425
## 11 12 13 14 15
## -0.3349697425 -0.0151999528 -0.3487999528 -0.6343999528 -0.5079999528
## 16 17 18 19 20
## -0.4133033095 -0.2994033095 -0.4531033095 -0.0285033095 0.0570933338
## 21 22 23 24 25
## -0.2721066662 -0.3174066662 -0.4073066662 -0.4759100229 -0.4859100229
## 26 27 28 29 30
## -0.2141100229 -0.2320100229 0.0354597668 -0.5359402332 0.7375564101
## 31 32 33 34 35
## 0.3651564101 -0.1488469466 -0.4702469466 0.1343496967 0.2779496967
## 36 37 38 39 40
## 0.5540194864 0.3336194864 0.3645161297 0.0956161297 0.5125127730
## 41 42 43 44 45
## 0.4433127730 -0.0607905837 0.4567094163 0.4649792059 -0.1094207941
## 46 47 48 49 50
## 0.4336758492 0.3196758492 -0.3031275075 -0.2996275075 0.1093691358
## 51 52 53 54 55
## 0.3170691358 -0.1615610745 1.0032389255 0.9405355688 0.4376355688
## 56 57 58 59 60
## 0.1562322121 -0.4079677879 0.9590288554 0.5696288554 0.7995952884
## 61 62 63 64 65
## -0.2920047116 -0.2432114250 -0.1185114250 0.3341550080 0.6115550080
## 66 67 68 69 70
## 0.0660482946 0.6118482946 -0.2396852725 0.4796080141 0.2684677337
## 71 72 73 74 75
## -0.1567725467 0.4790871729 0.1679468924 1.0010066120 -0.8718336684
## 76 77 78 79 80
## 0.5261260512 -0.0327402332 0.2369597668 -0.1698435899 0.0818564101
## 81 82 83 84 85
## -0.0428469466 0.2944530534 0.7627496967 0.0770496967 0.2201194864
## 86 87 88 89 90
## 0.1443194864 0.2488161297 -0.5383838703 1.1525127730 -0.3102872270
## 91 92 93 94 95
## 0.3777094163 0.6483094163 0.2113792059 -0.7149207941 0.5671758492
## 96 97 98 99 100
## -0.0005241508 -0.4634275075 -0.2407275075 0.0453691358 -0.2984308642
## 101 102 103 104 105
## -0.1388610745 -0.1888610745 0.2829355688 0.4305355688 -0.1545677879
## 106 107 108 109 110
## 0.5918322121 -0.3023711446 -0.0447711446 -0.1323047116 0.0430952884
## 111 112 113 114 115
## 0.4079885750 0.0759885750 0.4555550080 -0.9783449920 0.2020482946
## 116 117 118 119 120
## -0.5823517054 -0.7780852725 -0.3792919859 0.2654677337 -0.0781725467
## 121 122 123 124 125
## -1.0598128271 -1.5410531076 -0.4242933880 0.0066663316 -1.0597739488
fit1$fitted.values
## 1 2 3 4 5 6 7
## 0.4952597 0.4952597 0.4952597 0.4952597 0.5278147 0.5278147 0.5278147
## 8 9 10 11 12 13 14
## 0.5278147 0.5603697 0.5603697 0.5603697 0.7557000 0.7557000 0.7557000
## 15 16 17 18 19 20 21
## 0.7557000 0.7774033 0.7774033 0.7774033 0.7774033 0.7991067 0.7991067
## 22 23 24 25 26 27 28
## 0.7991067 0.7991067 0.8208100 0.8208100 0.8208100 0.8208100 1.0161402
## 29 30 31 32 33 34 35
## 1.0161402 1.0378436 1.0378436 1.0595469 1.0595469 1.0812503 1.0812503
## 36 37 38 39 40 41 42
## 1.2765805 1.2765805 1.2982839 1.2982839 1.3199872 1.3199872 1.3416906
## 43 44 45 46 47 48 49
## 1.3416906 1.5370208 1.5370208 1.5587242 1.5587242 1.5804275 1.5804275
## 50 51 52 53 54 55 56
## 1.6021309 1.6021309 1.7974611 1.7974611 1.8191644 1.8191644 1.8408678
## 57 58 59 60 61 62 63
## 1.8408678 1.8625711 1.8625711 2.0796047 2.0796047 2.1230114 2.1230114
## 64 65 66 67 68 69 70
## 2.3400450 2.3400450 2.3834517 2.3834517 2.6004853 2.6438920 2.9043323
## 71 72 73 74 75 76 77
## 3.1647725 3.4252128 3.6856531 3.9460934 4.2065337 4.4669739 1.0161402
## 78 79 80 81 82 83 84
## 1.0161402 1.0378436 1.0378436 1.0595469 1.0595469 1.0812503 1.0812503
## 85 86 87 88 89 90 91
## 1.2765805 1.2765805 1.2982839 1.2982839 1.3199872 1.3199872 1.3416906
## 92 93 94 95 96 97 98
## 1.3416906 1.5370208 1.5370208 1.5587242 1.5587242 1.5804275 1.5804275
## 99 100 101 102 103 104 105
## 1.6021309 1.6021309 1.7974611 1.7974611 1.8191644 1.8191644 1.8408678
## 106 107 108 109 110 111 112
## 1.8408678 1.8625711 1.8625711 2.0796047 2.0796047 2.1230114 2.1230114
## 113 114 115 116 117 118 119
## 2.3400450 2.3400450 2.3834517 2.3834517 2.6004853 2.6438920 2.9043323
## 120 121 122 123 124 125
## 3.1647725 3.4252128 3.6856531 3.9460934 4.2065337 4.4669739
summary(fit1)
##
## Call:
## lm(formula = masa ~ horas)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.54105 -0.29986 -0.03274 0.33362 1.15251
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.487e+01 7.546e-01 -19.71 <2e-16 ***
## horas 1.085e-02 4.944e-04 21.95 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4677 on 123 degrees of freedom
## Multiple R-squared: 0.7966, Adjusted R-squared: 0.795
## F-statistic: 481.8 on 1 and 123 DF, p-value: < 2.2e-16
anova(fit1)
## Analysis of Variance Table
##
## Response: masa
## Df Sum Sq Mean Sq F value Pr(>F)
## horas 1 105.379 105.379 481.81 < 2.2e-16 ***
## Residuals 123 26.902 0.219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
layout(matrix(1:4,2,2))
plot(fit1)
layout(1)
shapiro.test(fit1$residuals)
##
## Shapiro-Wilk normality test
##
## data: fit1$residuals
## W = 0.98757, p-value = 0.3157
abline(fit1)
segments(horas, fitted(fit1), horas, masa)
