SA2
praisons
June 18, 2016
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library(gdata)## Warning: package 'gdata' was built under R version 3.2.3## gdata: Unable to locate valid perl interpreter
## gdata:
## gdata: read.xls() will be unable to read Excel XLS and XLSX files
## gdata: unless the 'perl=' argument is used to specify the location
## gdata: of a valid perl intrpreter.
## gdata:
## gdata: (To avoid display of this message in the future, please
## gdata: ensure perl is installed and available on the executable
## gdata: search path.)## gdata: Unable to load perl libaries needed by read.xls()
## gdata: to support 'XLX' (Excel 97-2004) files.## ## gdata: Unable to load perl libaries needed by read.xls()
## gdata: to support 'XLSX' (Excel 2007+) files.## ## gdata: Run the function 'installXLSXsupport()'
## gdata: to automatically download and install the perl
## gdata: libaries needed to support Excel XLS and XLSX formats.##
## Attaching package: 'gdata'## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.sizerequire(gdata)
setwd('C:/Users/praisons/Documents/CBA/Term3/SA2')
wcdata <- read.csv('wc-at.csv', header = TRUE, sep=",")
plot(wcdata$Waist,wcdata$AT,xlab='Waist Circumference', ylab='Adipose Tissue', main='Scatterplot: Waist Circumference Vs Adipose Tissue')
linreg <- lm(wcdata$AT~wcdata$Waist)
abline(linreg, col='red')
linreg$residuals## 1 2 3 4 5
## -16.8482516 -9.2417039 -24.3532103 -31.5897580 -12.3823657
## 6 7 8 9 10
## -10.8575594 -34.7602369 -39.5073853 7.7839166 -4.9870195
## 11 12 13 14 15
## -4.3905023 0.4270336 1.3488664 -14.7884040 -19.3906852
## 16 17 18 19 20
## 33.6190188 -2.9388829 -7.0829360 4.0676648 -17.2359399
## 21 22 23 24 25
## -13.2318563 -23.5902369 -24.9513775 -32.4587698 -21.8049822
## 26 27 28 29 30
## -31.8335761 -0.1057354 21.8433069 -2.5904198 -4.6244119
## 31 32 33 34 35
## -0.2732713 0.5659755 -19.1429448 -9.6905417 -9.8221700
## 36 37 38 39 40
## 10.1192754 8.2252833 -11.2810294 13.2927366 -1.7884040
## 41 42 43 44 45
## 0.2967287 3.6081742 -11.5766931 56.7901506 25.4361584
## 46 47 48 49 50
## -36.4623529 -3.1889527 12.1192754 6.2303944 22.9839382
## 51 52 53 54 55
## 16.3759146 16.8590709 54.2866678 -3.2623961 -13.4830884
## 56 57 58 59 60
## 2.2751397 1.1436333 72.4819224 -8.9809901 1.5549568
## 61 62 63 64 65
## 4.6239991 22.0543256 -32.0754915 -35.6129842 -107.2880953
## 66 67 68 69 70
## -92.7357531 26.2015566 -27.9281779 -50.0687482 11.8835330
## 71 72 73 74 75
## -66.1264670 20.6366894 -19.5278731 -6.9044512 59.4246736
## 76 77 78 79 80
## 10.0955488 -31.2810294 -16.7398888 -38.6576076 30.9658142
## 81 82 83 84 85
## -10.0104591 -38.4930452 -11.4930452 -13.4107639 23.6952439
## 86 87 88 89 90
## 40.2601112 -46.9519046 -0.3047561 42.4595015 -24.0341858
## 91 92 93 94 95
## 43.7426972 3.0718221 58.0132675 -29.9307246 17.4721269
## 96 97 98 99 100
## -22.1987482 90.3423924 7.5544082 51.2252833 59.4484003
## 101 102 103 104 105
## 0.3423924 67.6199283 71.2126579 54.6366894 -6.2503372
## 106 107 108 109
## -44.5300933 -3.1303981 50.7705596 48.6952439mean(linreg$residuals)## [1] 1.40624e-16summary((linreg))##
## Call:
## lm(formula = wcdata$AT ~ wcdata$Waist)
##
## Residuals:
## Min 1Q Median 3Q Max
## -107.288 -19.143 -2.939 16.376 90.342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -215.9815 21.7963 -9.909 <2e-16 ***
## wcdata$Waist 3.4589 0.2347 14.740 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 33.06 on 107 degrees of freedom
## Multiple R-squared: 0.67, Adjusted R-squared: 0.667
## F-statistic: 217.3 on 1 and 107 DF, p-value: < 2.2e-16#Log
plot(wcdata$Waist, log10(wcdata$AT),xlab='Waist Circumference', ylab='Adipose Tissue', main='Scatterplot: Waist Circumference Vs Adipose Tissue')
linreg <- lm(log10(wcdata$AT)~wcdata$Waist)
abline(linreg, col='red')
linreg$residuals## 1 2 3 4 5
## -0.218285117 -0.177839014 -0.122390376 -0.157941224 -0.152009122
## 6 7 8 9 10
## -0.241800734 -0.272472310 -0.261519570 -0.373464268 -0.093334370
## 11 12 13 14 15
## -0.126640833 0.009142195 -0.017534728 -0.074667538 -0.177088287
## 16 17 18 19 20
## 0.221062709 -0.008258008 -0.094190764 0.025040354 -0.181643877
## 21 22 23 24 25
## -0.101785943 -0.131300828 -0.169214157 -0.102360157 -0.094051647
## 26 27 28 29 30
## -0.082537631 0.085715629 0.196753424 0.071800086 0.047202815
## 31 32 33 34 35
## 0.079178913 0.088967272 -0.011835484 0.037287813 -0.001120604
## 36 37 38 39 40
## 0.108971571 0.116754891 -0.011816018 0.149015631 0.041284407
## 41 42 43 44 45
## 0.082577212 0.052883543 -0.022527048 0.312165464 0.211929635
## 46 47 48 49 50
## -0.122197475 0.059066259 0.116091313 0.103928170 0.201940657
## 51 52 53 54 55
## 0.153671776 0.170988129 0.312614894 -0.019453111 -0.072123014
## 56 57 58 59 60
## 0.068764913 0.091969036 0.359865407 0.033368028 0.090509622
## 61 62 63 64 65
## 0.077877568 0.192833493 -0.270300002 -0.125152055 -0.456380556
## 66 67 68 69 70
## -0.392535581 0.149585886 -0.083323866 -0.170015146 0.027776746
## 71 72 73 74 75
## -0.247898024 0.100077800 -0.017517307 0.019943594 0.126646989
## 76 77 78 79 80
## 0.076166518 -0.084979230 -0.036083410 -0.125631916 0.056007080
## 81 82 83 84 85
## -0.010572498 -0.144405007 -0.060084121 -0.081081272 0.043897643
## 86 87 88 89 90
## 0.124930692 -0.179471585 -0.017156322 -0.047904905 -0.090573581
## 91 92 93 94 95
## 0.191225935 0.015734351 0.222679864 -0.065596946 0.119203260
## 96 97 98 99 100
## -0.060458522 0.207167104 0.074624684 0.263462372 0.183000164
## 101 102 103 104 105
## 0.004127009 0.130793193 0.070937952 0.186714928 0.021712027
## 106 107 108 109
## -0.159045730 0.022423597 0.109998730 0.099509888mean(linreg$residuals)## [1] 7.291757e-18summary((linreg))##
## Call:
## lm(formula = log10(wcdata$AT) ~ wcdata$Waist)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45638 -0.09419 0.01573 0.10008 0.35987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.321821 0.101029 3.185 0.00189 **
## wcdata$Waist 0.017481 0.001088 16.073 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1533 on 107 degrees of freedom
## Multiple R-squared: 0.7071, Adjusted R-squared: 0.7044
## F-statistic: 258.3 on 1 and 107 DF, p-value: < 2.2e-16#Sqrt
#Log
plot(wcdata$Waist, sqrt(wcdata$AT), xlab='Waist Circumference', ylab='Adipose Tissue', main='Scatterplot: Waist Circumference Vs Adipose Tissue')
linreg <- lm(sqrt(wcdata$AT)~wcdata$Waist)
abline(linreg, col='red')
linreg$residuals## 1 2 3 4 5
## -1.507975642 -1.103585286 -1.323753328 -1.696107579 -1.098833876
## 6 7 8 9 10
## -1.400398895 -2.295758099 -2.396288644 -1.168716763 -0.623724116
## 11 12 13 14 15
## -0.743939000 -0.001852764 -0.100536124 -0.828096179 -1.420981659
## 16 17 18 19 20
## 1.952940930 -0.179185929 -0.691142653 0.175897679 -1.372945239
## 21 22 23 24 25
## -0.908313493 -1.344056834 -1.555729270 -1.433110239 -1.116157697
## 26 27 28 29 30
## -1.299269847 0.419008156 1.565279884 0.274098796 0.069903492
## 31 32 33 34 35
## 0.361074939 0.463849032 -0.588555090 -0.080376491 -0.223371547
## 36 37 38 39 40
## 0.859494721 0.834645658 -0.315288384 1.077417333 0.102725307
## 41 42 43 44 45
## 0.394467200 0.301488304 -0.483201461 3.134101155 1.758489181
## 46 47 48 49 50
## -1.549578604 0.239636326 0.950031228 0.647134678 1.616240486
## 51 52 53 54 55
## 1.242586009 1.313997062 3.068901799 -0.241673036 -0.779253001
## 56 57 58 59 60
## 0.352628256 0.493571768 3.787902358 -0.105261840 0.471669601
## 61 62 63 64 65
## 0.459808769 1.576921837 -2.202287533 -1.538189619 -5.157765002
## 66 67 68 69 70
## -4.424643942 1.498883004 -1.123869722 -2.178261450 0.489770218
## 71 72 73 74 75
## -3.014030006 1.097180287 -0.591269925 -0.041650994 2.156285495
## 76 77 78 79 80
## 0.699972066 -1.232798567 -0.583983264 -1.631017406 1.101466235
## 81 82 83 84 85
## -0.274132712 -1.710273924 -0.565929208 -0.726052711 0.862961962
## 86 87 88 89 90
## 1.711249032 -2.115551943 -0.055267084 0.733865315 -1.050240315
## 91 92 93 94 95
## 2.163809725 0.204958284 2.682048264 -1.143283485 1.113429986
## 96 97 98 99 100
## -0.853376905 3.310152046 0.623955261 2.738013940 2.488295133
## 101 102 103 104 105
## 0.074183077 2.359775139 2.069198344 2.398815842 -0.014689360
## 106 107 108 109
## -2.037440587 0.075824549 1.865601320 1.757417805mean(linreg$residuals)## [1] -9.771745e-18summary((linreg))##
## Call:
## lm(formula = sqrt(wcdata$AT) ~ wcdata$Waist)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1578 -1.1162 -0.0147 0.9500 3.7879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.89840 1.03576 -6.66 1.22e-09 ***
## wcdata$Waist 0.18031 0.01115 16.17 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.571 on 107 degrees of freedom
## Multiple R-squared: 0.7096, Adjusted R-squared: 0.7069
## F-statistic: 261.5 on 1 and 107 DF, p-value: < 2.2e-16newspdata <- read.csv('NewspaperData.csv', header= TRUE, sep=",")Including Plots
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