Intro_inf
Grupo 1
2022-09-10
Opções para carregamento do chunk:
eval = FALSE: Exclui o resultado, mas não a formulaecho = FALSE: Exclui a fórmula, mas não o resultadoinclude = FALSE: Exclui o resultado e a formul, mas o chunk ainda rodamessage = FALSE: Exclui as mensagenswarning = FALSE: Exclui os avisos
Dados completos:
| Name | Sex | Age | Start_Day | Month | Year | Time_min | Direction |
|---|---|---|---|---|---|---|---|
| Marilyn Bell | F | 16 | 8 | Sep | 1954 | 1255 | SN |
| Brenda Fisher | F | 28 | 12 | Aug | 1956 | 1131 | SN |
| Cindy Nicholas | F | 16 | 17 | Aug | 1974 | 910 | SN |
| Diana Nyad | F | 24 | 30 | Aug | 1974 | 1095 | NS |
| Debbie Roach | F | 17 | 16 | Aug | 1975 | 1110 | SN |
| Angela Kondrak | F | 17 | 22 | Aug | 1976 | 1428 | SN |
| Kim Lumsdon | F | 19 | 27 | Aug | 1976 | 1287 | SN |
| Loreen Passfield | F | 21 | 1 | Sep | 1979 | 943 | SN |
| Jocelyn Muir | F | 15 | 5 | Sep | 1981 | 955 | SN |
| Marilyn Korzekwa | F | 26 | 1 | Sep | 1983 | 1289 | SN |
| Marilyn Korzekwa | F | 27 | 17 | Aug | 1984 | 1283 | NS |
| Kim Middleton | F | 26 | 16 | Aug | 1985 | 1114 | SN |
| Vicki Keith | F | 25 | 14 | Aug | 1986 | 1619 | NS |
| Vicki Keith | F | 26 | 5 | Aug | 1987 | 3370 | NSN |
| Vicki Keith | F | 27 | 29 | Aug | 1988 | 1413 | SN |
| Vicki Keith | F | 28 | 3 | Sep | 1989 | 1860 | SN |
| Colleen Shields | F | 38 | 10 | Aug | 1990 | 1076 | SN |
| Patty Thompson | F | 45 | 14 | Aug | 1991 | 1158 | SN |
| Shelagh Freedman | F | 17 | 12 | Aug | 1993 | 1563 | SN |
| Kim Middleton | F | 34 | 4 | Sep | 1993 | 1740 | NS |
| Kim Middleton | F | 35 | 30 | July | 1994 | 1574 | NS |
| Ingrid Martin | F | 38 | 11 | Aug | 1996 | 1405 | SN |
| Paula Stephanson | F | 17 | 16 | Aug | 1996 | 1360 | SN |
| Nicole Mallette | F | 31 | 8 | Aug | 1997 | 970 | SN |
| Melissa Brannagan | F | 23 | 9 | Aug | 2005 | 971 | SN |
| Kim Lumsdon | F | 49 | 5 | Aug | 2006 | 1598 | SN |
| Samantha Whiteside | F | 16 | 8 | Aug | 2006 | 912 | SN |
| Colleen Shields | F | 54 | 12 | Aug | 2006 | 991 | SN |
| Stephanie Hermans | F | 21 | 10 | Aug | 2007 | 1085 | SN |
| Jade Scognamillo | F | 15 | 31 | July | 2009 | 1199 | SN |
| Susanne Robinson | F | 36 | 10 | Aug | 2010 | 1468 | SN |
| Rebekah Boscariol | F | 17 | 5 | Aug | 2011 | 933 | SN |
| Christine Arsenault | F | 35 | 8 | Aug | 2011 | 1342 | SN |
| Annaleise Carr | F | 14 | 18 | Aug | 2012 | 1601 | SN |
| Ashleigh Beacham | F | 15 | 18 | Aug | 2013 | 1103 | SN |
| Trinity Arsenault | F | 14 | 3 | Aug | 2014 | 1400 | SN |
| Colleen Shields | F | 62 | 8 | Aug | 2014 | 1294 | SN |
| John Jaremey | M | 36 | 23 | Jul | 1956 | 1273 | SN |
| Bill Sadlo | M | 57 | 23 | Aug | 1957 | 1501 | SN |
| Jim Woods | M | 41 | 26 | Aug | 1957 | 1115 | SN |
| Jim Woods | M | 45 | 2 | Sep | 1961 | 1027 | SN |
| John Kinsella | M | 25 | 16 | Aug | 1978 | 829 | SN |
| Claudio Plit | M | 23 | 16 | Aug | 1978 | 901 | SN |
| Raul Villagomez | M | 27 | 16 | Aug | 1978 | 909 | SN |
| Magdy Mandour | M | 23 | 16 | Aug | 1978 | 919 | SN |
| Bill Heiss | M | 26 | 16 | Aug | 1978 | 957 | SN |
| Cam Kamula | M | 29 | 3 | Aug | 1984 | 1207 | SN |
| Rick Wood | M | 31 | 19 | Aug | 1989 | 1293 | SN |
| Bob Weir | M | 46 | 23 | Aug | 1989 | 1328 | SN |
| Paolo Pinto | M | 52 | 28 | July | 1990 | 1437 | SN |
| John Scott | M | 31 | 7 | Aug | 1992 | 890 | SN |
| Carlos Costa | M | 20 | 22 | July | 1993 | 1963 | SN |
| John Scott | M | 33 | 7 | Aug | 1994 | 882 | SN |
| Rick Goodwin | M | 36 | 27 | Aug | 1994 | 1626 | SN |
| Dan Foster | M | 33 | 15 | Aug | 1998 | 1152 | SN |
| Gregg Taylor | M | 19 | 13 | Aug | 2003 | 1163 | SN |
| Peter Gibbs | M | 56 | 7 | Aug | 2004 | 1120 | SN |
| Jay Serdula | M | 35 | 28 | July | 2008 | 2461 | SN |
| Shaun Chisholm | M | 40 | 10 | Aug | 2008 | 1163 | SN |
| Miguel Vadillo Sanchez | M | 40 | 10 | Aug | 2010 | 1083 | SN |
| Michael McIsaac | M | 32 | 22 | Aug | 2015 | 1343 | SN |
| Loren King | M | 48 | 1 | Aug | 2016 | 1115.13 | SN |
Algumas informações básicas gerais
| Name | Sex | Age | Start_Day | Month | Year | Time_min | Direction | |
|---|---|---|---|---|---|---|---|---|
| Length:62 | Length:62 | Min. :14.00 | Min. : 1.00 | Length:62 | Min. :1954 | Length:62 | Length:62 | |
| Class :character | Class :character | 1st Qu.:20.25 | 1st Qu.: 8.00 | Class :character | 1st Qu.:1978 | Class :character | Class :character | |
| Mode :character | Mode :character | Median :27.50 | Median :13.50 | Mode :character | Median :1992 | Mode :character | Mode :character | |
| NA | NA | Mean :30.13 | Mean :14.02 | NA | Mean :1991 | NA | NA | |
| NA | NA | 3rd Qu.:36.00 | 3rd Qu.:18.75 | NA | 3rd Qu.:2006 | NA | NA | |
| NA | NA | Max. :62.00 | Max. :31.00 | NA | Max. :2016 | NA | NA |
Algumas informações básicas dos dados femininos
| Name | Sex | Age | Start_Day | Month | Year | Time_min | Direction | |
|---|---|---|---|---|---|---|---|---|
| Length:37 | Length:37 | Min. :14.00 | Min. : 1.00 | Length:37 | Min. :1954 | Length:37 | Length:37 | |
| Class :character | Class :character | 1st Qu.:17.00 | 1st Qu.: 8.00 | Class :character | 1st Qu.:1983 | Class :character | Class :character | |
| Mode :character | Mode :character | Median :25.00 | Median :11.00 | Mode :character | Median :1993 | Mode :character | Mode :character | |
| NA | NA | Mean :26.59 | Mean :12.78 | NA | Mean :1992 | NA | NA | |
| NA | NA | 3rd Qu.:34.00 | 3rd Qu.:17.00 | NA | 3rd Qu.:2006 | NA | NA | |
| NA | NA | Max. :62.00 | Max. :31.00 | NA | Max. :2014 | NA | NA |
lgumas informações básicas dos dados masculinos
| Name | Sex | Age | Start_Day | Month | Year | Time_min | Direction | |
|---|---|---|---|---|---|---|---|---|
| Length:25 | Length:25 | Min. :19.00 | Min. : 1.00 | Length:25 | Min. :1956 | Length:25 | Length:25 | |
| Class :character | Class :character | 1st Qu.:27.00 | 1st Qu.:10.00 | Class :character | 1st Qu.:1978 | Class :character | Class :character | |
| Mode :character | Mode :character | Median :33.00 | Median :16.00 | Mode :character | Median :1990 | Mode :character | Mode :character | |
| NA | NA | Mean :35.36 | Mean :15.84 | NA | Mean :1988 | NA | NA | |
| NA | NA | 3rd Qu.:41.00 | 3rd Qu.:23.00 | NA | 3rd Qu.:2003 | NA | NA | |
| NA | NA | Max. :57.00 | Max. :28.00 | NA | Max. :2016 | NA | NA |
Grafico_sparkline
| var | sparkline |
|---|---|
| Age | |
| Time_min |
Boxplot Age/Sex
Time/sex_boxplot
Year/sex_boxplot
Start_day/sex_boxplot
Teste de Hipotese
x <- dados_seminario["Age"]
y <- dados_seminario["Time_min"]
t <- c(16,28,16,24,17,17,19,21,15,26,27,26,25,26,27,28,38,45,17,34,35,38,17,31,23,49,16,54,21,15,36,17,35,14,15,14,62,36,57,41,45,25,23,27,23,26,29,31,46,52,31,20,33,36,33,19,56,35,40,40,32,48)
s <- c(1255, 1131, 910, 1095, 1110, 1428, 1287, 943, 955, 1289, 1283, 1114, 1619, 3370, 1413, 1860, 1076, 1158, 1563, 1740, 1574, 1405, 1360, 970, 971, 1598, 912, 991, 1085, 1199, 1468, 933, 1342, 1601, 1103, 1400, 1294, 1273, 1501, 1115, 1027, 829, 901, 909, 919, 957, 1207, 1293, 1328, 1437, 890, 1963, 882, 1626, 1152, 1163, 1120, 2461, 1163, 1083, 1343, 1115.13)
Age_F <- c(16,28,16,24,17,17,19,21,15,26,27,26,25,26,27,28,38,45,17,34,35,38,17,31,23,49,16,54,21,15,36,17,35,14,15,14,62)
Age_M <- c(36,57,41,45,25,23,27,23,26,29,31,46,52,31,20,33,36,33,19,56,35,40,40,32,48)
mean(Age_M)
mean(Age_F)
t.test(Age_M, Age_F)
t.test(s, t, paired=TRUE, alternative = "greater")
t.test(t, mu= 30.13)
Teste
t
prop.test(x, n, p = NULL, alternative = "two.sided", correct = TRUE) #teste de proporção
Teste
de proporção
Regressão
min <- c(1255, 1131, 910, 1095, 1110, 1428, 1287, 943, 955, 1289, 1283, 1114, 1619, 3370, 1413, 1860, 1076, 1158, 1563, 1740, 1574, 1405, 1360, 970, 971, 1598, 912, 991, 1085, 1199, 1468, 933, 1342, 1601, 1103, 1400, 1294, 1273, 1501, 1115, 1027, 829, 901, 909, 919, 957, 1207, 1293, 1328, 1437, 890, 1963, 882, 1626, 1152, 1163, 1120, 2461, 1163, 1083, 1343, 1115.13)
horas <- c(20.91667,18.85,15.16667,18.25,18.5,23.8,21.45,15.7166,15.91667,21.48333,21.38333,18.56667,26.98333,56.16667,23.55,31,17.93333,19.3,26.05,29,26.23333,23.41667,22.66667,16.16667,16.18333,26.63333,15.2,16.51667,18.08333,19.98333,24.46667,15.55,22.36667,26.68333,18.38333,23.33333,21.56667,21.21667,25.01667,18.58333,17.11667,13.81667,15.01667,15.15,15.31667,15.95,20.11667,21.55,22.13333,23.95,14.83333,32.71667,14.7,27.1,19.2,19.38333,18.66667,41.01667,19.38333,18.05,22.38333,18.5855)
idade <- c(16,28,16,24,17,17,19,21,15,26,27,26,25,26,27,28,38,45,17,34,35,38,17,31,23,49,16,54,21,15,36,17,35,14,15,14,62,36,57,41,45,25,23,27,23,26,29,31,46,52,31,20,33,36,33,19,56,35,40,40,32,48)
#Age
#Time_min
plot(dados_seminario$Time_min, dados_seminario$Age)
#modelo linear
mod <- lm(dados_seminario$Time_min ~ dados_seminario$Age)
#analise grafico
par(mfrow=c(1,1))
plot(mod)
#normalidade nos residuos
shapiro.test(mod$residuals)##
## Shapiro-Wilk normality test
##
## data: mod$residuals
## W = 0.75873, p-value = 9.83e-09#outliers no residuos
summary(rstandard(mod))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.099953 -0.665258 -0.213178 -0.000881 0.328545 5.194825library(car)
#Indepencia dos residuos
durbinWatsonTest(mod)## lag Autocorrelation D-W Statistic p-value
## 1 0.08844374 1.819156 0.408
## Alternative hypothesis: rho != 0library(lmtest)
#homocedasticidade(Breusch-Pagan)
#bptest(mod)
#Analise
summary(mod)##
## Call:
## lm(formula = dados_seminario$Time_min ~ dados_seminario$Age)
##
## Residuals:
## Min 1Q Median 3Q Max
## -443.51 -265.76 -85.73 131.79 2095.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1227.990 139.187 8.823 1.96e-12 ***
## dados_seminario$Age 1.781 4.289 0.415 0.679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 407.1 on 60 degrees of freedom
## Multiple R-squared: 0.002865, Adjusted R-squared: -0.01375
## F-statistic: 0.1724 on 1 and 60 DF, p-value: 0.6795#grafico
library(ggplot2)
library(ggpubr)
ggplot(data = dados_seminario, mapping = aes(x=horas, y=idade)) +
geom_point() +
geom_smooth(method = "lm", col = "red") +
stat_regline_equation(aes(label = paste(..eq.label.., ..adj.rr.label..,
sep = "*plain(\",\")~~")), label.x = 0, label.y = 400) +
theme_classic()