filter(!is_outlier)
sma_M01 <- sma(M01_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M01$groupsummary
summary(sma_M01)
sma_M01_shift <- sma(M01_dx ~ Geometric_mean + Group_Sex, log = "xy", type = "shift", data = cleaned_data)
sma_M01_shift
#M04#
vars <- c("M04_dx", "Geometric_mean", "M04_log", "Group_Sex")
sampleM04_comp <- sample[complete.cases(sample[, vars]), ]
sampleM04_clean <- sampleM04_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M04_log, na.rm = TRUE),
sd_val = sd(M04_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M04_log < lower_bound | M04_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM04_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M04_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM04_clean %>%
filter(!is_outlier)
sma_M04 <- sma(M04_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M04$groupsummary
#M12#
vars <- c("M12_dx", "Geometric_mean", "M12_log", "Group_Sex")
sampleM12_comp <- sample[complete.cases(sample[, vars]), ]
sampleM12_clean <- sampleM12_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M12_log, na.rm = TRUE),
sd_val = sd(M12_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M12_log < lower_bound | M12_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM12_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M12_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM12_clean %>%
filter(!is_outlier)
sma_M12 <- sma(M12_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M12$groupsummary
#M13#
vars <- c("M13_dx", "Geometric_mean", "M13_log", "Group_Sex")
sampleM13_comp <- sample[complete.cases(sample[, vars]), ]
sampleM13_clean <- sampleM13_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M13_log, na.rm = TRUE),
sd_val = sd(M13_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M13_log < lower_bound | M13_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM13_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M13_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM13_clean %>%
filter(!is_outlier)
sma_M13 <- sma(M13_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M13$groupsummary
#M14#
vars <- c("M14_dx", "Geometric_mean", "M14_log", "Group_Sex")
sampleM14_comp <- sample[complete.cases(sample[, vars]), ]
sampleM14_clean <- sampleM14_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M14_log, na.rm = TRUE),
sd_val = sd(M14_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M14_log < lower_bound | M14_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM14_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M14_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM14_clean %>%
filter(!is_outlier)
sma_M14 <- sma(M14_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M14$groupsummary
#M14_1#
vars <- c("M14_1_dx", "Geometric_mean", "M14_1_log", "Group_Sex")
sampleM14_1_comp <- sample[complete.cases(sample[, vars]), ]
sampleM14_1_clean <- sampleM14_1_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M14_1_log, na.rm = TRUE),
sd_val = sd(M14_1_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M14_1_log < lower_bound | M14_1_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM14_1_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M14_1_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM14_1_clean %>%
filter(!is_outlier)
sma_M14_1 <- sma(M14_1_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M14_1$groupsummary
summary(sma_M14_1)
sma_M14_1_shift <- sma(M14_1_dx ~ Geometric_mean + Group_Sex, log = "xy", type = "shift", data = cleaned_data)
sma_M14_1_shift
sample <- read.table(file.choose(),header=T,row.names = 1,sep="\t")
#M14_1#
vars <- c("M14_1_dx", "Geometric_mean", "M14_1_log", "Group_Sex")
sampleM14_1_comp <- sample[complete.cases(sample[, vars]), ]
sampleM14_1_clean <- sampleM14_1_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M14_1_log, na.rm = TRUE),
sd_val = sd(M14_1_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M14_1_log < lower_bound | M14_1_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM14_1_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M14_1_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM14_1_clean %>%
filter(!is_outlier)
sma_M14_1 <- sma(M14_1_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M14_1$groupsummary
sample <- read.table(file.choose(),header=T,row.names = 1,sep="\t")
nrow(BO_F)
table(data$Group_Sex)
table(sample$Group_Sex)
subset(sample, Group_Sex == "BO_F")
all.equal(
sample$M14_1_log,
log10(sample$M14_1_dx),
na.rm = TRUE
)
R.version.string
packageVersion("smatr")
sampleM14_1_comp %>%
count(Group_Sex)
sampleM14_1_clean %>%
count(Group_Sex, is_outlier)
sma_BOF <- sma(
M14_1_dx ~ Geometric_mean,
log = "xy",
data = subset(cleaned_data, Group_Sex == "BO_F"),
slope.test = 1
)
summary(sma_BOF)
#M15_1#
vars <- c("M15_1._dx", "Geometric_mean", "M15_1._log", "Group_Sex")
sampleM15_1._comp <- sample[complete.cases(sample[, vars]), ]
sampleM15_1._clean <- sampleM15_1._comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M15_1._log, na.rm = TRUE),
sd_val = sd(M15_1._log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M15_1._log < lower_bound | M15_1._log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM15_1._clean %>%
filter(is_outlier) %>%
select(Group_Sex, M15_1._log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM15_1._clean %>%
filter(!is_outlier)
sma_M15_1. <- sma(M15_1._dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M15_1.$groupsummary
#M15_a#
vars <- c("M15_a._dx", "Geometric_mean", "M15_a._log", "Group_Sex")
sampleM15_a._comp <- sample[complete.cases(sample[, vars]), ]
sampleM15_a._clean <- sampleM15_a._comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M15_a._log, na.rm = TRUE),
sd_val = sd(M15_a._log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M15_a._log < lower_bound | M15_a._log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM15_a._clean %>%
filter(is_outlier) %>%
select(Group_Sex, M15_a._log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM15_a._clean %>%
filter(!is_outlier)
sma_M15_a. <- sma(M15_a._dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M15_a.$groupsummary
#M17_a#
vars <- c("M17_a_dx", "Geometric_mean", "M17_a_log", "Group_Sex")
sampleM17_a_comp <- sample[complete.cases(sample[, vars]), ]
sampleM17_a_clean <- sampleM17_a_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M17_a_log, na.rm = TRUE),
sd_val = sd(M17_a_log, na.rm = TRUE),
lower_bound = mean_val - 1.96 * sd_val,
upper_bound = mean_val + 1.96 * sd_val,
is_outlier = M17_a_log < lower_bound | M17_a_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM17_a_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M17_a_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM17_a_clean %>%
filter(!is_outlier)
sma_M17_a <- sma(M17_a_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M17_a$groupsummary
#M18#
vars <- c("M18_dx", "Geometric_mean", "M18_log", "Group_Sex")
sampleM18_comp <- sample[complete.cases(sample[, vars]), ]
sampleM18_clean <- sampleM18_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M18_log, na.rm = TRUE),
sd_val = sd(M18_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M18_log < lower_bound | M18_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM18_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M18_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM18_clean %>%
filter(!is_outlier)
sma_M18 <- sma(M18_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M18$groupsummary
#M20#
vars <- c("M20_dx", "Geometric_mean", "M20_log", "Group_Sex")
sampleM20_comp <- sample[complete.cases(sample[, vars]), ]
sampleM20_clean <- sampleM20_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M20_log, na.rm = TRUE),
sd_val = sd(M20_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M20_log < lower_bound | M20_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM20_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M20_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM20_clean %>%
filter(!is_outlier)
sma_M20 <- sma(M20_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M20$groupsummary
#M21#
vars <- c("M21_dx", "Geometric_mean", "M21_log", "Group_Sex")
sampleM21_comp <- sample[complete.cases(sample[, vars]), ]
sampleM21_clean <- sampleM21_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M21_log, na.rm = TRUE),
sd_val = sd(M21_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M21_log < lower_bound | M21_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM21_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M21_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM21_clean %>%
filter(!is_outlier)
sma_M21 <- sma(M21_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M21$groupsummary
#M22#
vars <- c("M22_dx", "Geometric_mean", "M22_log", "Group_Sex")
sampleM22_comp <- sample[complete.cases(sample[, vars]), ]
sampleM22_clean <- sampleM22_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(M22_log, na.rm = TRUE),
sd_val = sd(M22_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = M22_log < lower_bound | M22_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleM22_clean %>%
filter(is_outlier) %>%
select(Group_Sex, M22_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleM22_clean %>%
filter(!is_outlier)
sma_M22 <- sma(M22_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_M22$groupsummary
#PUM#
vars <- c("PUM_dx", "Geometric_mean", "PUM_log", "Group_Sex")
samplePUM_comp <- sample[complete.cases(sample[, vars]), ]
samplePUM_clean <- samplePUM_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(PUM_log, na.rm = TRUE),
sd_val = sd(PUM_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = PUM_log < lower_bound | PUM_log > upper_bound
) %>%
ungroup()
outliers_removed <- samplePUM_clean %>%
filter(is_outlier) %>%
select(Group_Sex, PUM_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- samplePUM_clean %>%
filter(!is_outlier)
sma_PUM <- sma(PUM_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_PUM$groupsummary
#ISM#
vars <- c("ISM_dx", "Geometric_mean", "ISM_log", "Group_Sex")
sampleISM_comp <- sample[complete.cases(sample[, vars]), ]
sampleISM_clean <- sampleISM_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(ISM_log, na.rm = TRUE),
sd_val = sd(ISM_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = ISM_log < lower_bound | ISM_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleISM_clean %>%
filter(is_outlier) %>%
select(Group_Sex, ISM_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleISM_clean %>%
filter(!is_outlier)
sma_ISM <- sma(ISM_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_ISM$groupsummary
#SPU#
vars <- c("SPU_dx", "Geometric_mean", "SPU_log", "Group_Sex")
sampleSPU_comp <- sample[complete.cases(sample[, vars]), ]
sampleSPU_clean <- sampleSPU_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(SPU_log, na.rm = TRUE),
sd_val = sd(SPU_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = SPU_log < lower_bound | SPU_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleSPU_clean %>%
filter(is_outlier) %>%
select(Group_Sex, SPU_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleSPU_clean %>%
filter(!is_outlier)
sma_SPU <- sma(SPU_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_SPU$groupsummary
#ISMM#
vars <- c("ISMM_dx", "Geometric_mean", "ISMM_log", "Group_Sex")
sampleISMM_comp <- sample[complete.cases(sample[, vars]), ]
sampleISMM_clean <- sampleISMM_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(ISMM_log, na.rm = TRUE),
sd_val = sd(ISMM_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = ISMM_log < lower_bound | ISMM_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleISMM_clean %>%
filter(is_outlier) %>%
select(Group_Sex, ISMM_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleISMM_clean %>%
filter(!is_outlier)
sma_ISMM <- sma(ISMM_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_ISMM$groupsummary
#SS#
vars <- c("SS_dx", "Geometric_mean", "SS_log", "Group_Sex")
sampleSS_comp <- sample[complete.cases(sample[, vars]), ]
sampleSS_clean <- sampleSS_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(SS_log, na.rm = TRUE),
sd_val = sd(SS_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = SS_log < lower_bound | SS_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleSS_clean %>%
filter(is_outlier) %>%
select(Group_Sex, SS_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleSS_clean %>%
filter(!is_outlier)
sma_SS <- sma(SS_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_SS$groupsummary
#SA#
vars <- c("SA_dx", "Geometric_mean", "SA_log", "Group_Sex")
sampleSA_comp <- sample[complete.cases(sample[, vars]), ]
sampleSA_clean <- sampleSA_comp %>%
group_by(Group_Sex) %>%
mutate(
mean_val = mean(SA_log, na.rm = TRUE),
sd_val = sd(SA_log, na.rm = TRUE),
lower_bound = mean_val - 2.5 * sd_val,
upper_bound = mean_val + 2.5 * sd_val,
is_outlier = SA_log < lower_bound | SA_log > upper_bound
) %>%
ungroup()
outliers_removed <- sampleSA_clean %>%
filter(is_outlier) %>%
select(Group_Sex, SA_log)
print(outliers_removed)
cat("Total outliers removed:", nrow(outliers_removed), "\n")
cleaned_data <- sampleSA_clean %>%
filter(!is_outlier)
sma_SA <- sma(SA_dx ~ Geometric_mean + Group_Sex, log = "xy", data = cleaned_data, slope.test = 1)
sma_SA$groupsummary
#####################################################################################################
## 3) Descriptive statistics
sample <- read.table(file.choose(),header=T,row.names = 1,sep="\t")
## 3.1) FEM: maximum length of the femur##
#BOLOGNA FEMALES#
summary(sample$Femur_dx[1:31],na.rm = TRUE)
## 3.3) Stature (Trotter_Gleser), Body mass (Ruff) and Geometric_mean for each group
#BOLOGNA FEMALES#
summary(sample$Trotter_Gleser[1:31],na.rm = TRUE)
sd(sample$Trotter_Gleser[1:31],na.rm = TRUE)
## 3.3) Stature (Trotter_Gleser), Body mass (Ruff) and Geometric_mean for each group
#BOLOGNA FEMALES#
summary(sample$Trotter_Gleser[1:31],na.rm = TRUE)
sd(sample$Trotter_Gleser[1:31],na.rm = TRUE)
summary(sample$Ruff[1:31],na.rm = TRUE)
sd(sample$Ruff[1:31],na.rm = TRUE)
summary(sample$Geometric_mean[1:31],na.rm = TRUE)
sd(sample$Geometric_mean[1:31],na.rm = TRUE)
#BOLOGNA MALES#
summary(sample$Trotter_Gleser[32:64],na.rm = TRUE)
sd(sample$Trotter_Gleser[32:64],na.rm = TRUE)
#BOLOGNA MALES#
summary(sample$Trotter_Gleser[32:64],na.rm = TRUE)
sd(sample$Trotter_Gleser[32:64],na.rm = TRUE)
summary(sample$Ruff[32:64],na.rm = TRUE)
sd(sample$Ruff[32:64],na.rm = TRUE)
summary(sample$Geometric_mean[32:64],na.rm = TRUE)
sd(sample$Geometric_mean[32:64],na.rm = TRUE)
#SASSARI FEMALES#
summary(sample$Trotter_Gleser[65:96],na.rm = TRUE)
sd(sample$Trotter_Gleser[65:96],na.rm = TRUE)
#SASSARI FEMALES#
summary(sample$Trotter_Gleser[65:96],na.rm = TRUE)
sd(sample$Trotter_Gleser[65:96],na.rm = TRUE)
#SASSARI FEMALES#
summary(sample$Trotter_Gleser[65:96],na.rm = TRUE)
sd(sample$Trotter_Gleser[65:96],na.rm = TRUE)
summary(sample$Ruff[65:96],na.rm = TRUE)
sd(sample$Ruff[65:96],na.rm = TRUE)
summary(sample$Geometric_mean[65:96],na.rm = TRUE)
sd(sample$Geometric_mean[65:96],na.rm = TRUE)
#SASSARI MALES#
summary(sample$Trotter_Gleser[97:132],na.rm = TRUE)
sd(sample$Trotter_Gleser[97:132],na.rm = TRUE)
summary(sample$Ruff[97:132],na.rm = TRUE)
sd(sample$Ruff[97:132],na.rm = TRUE)
#MASCHI SASSARI
mean(sample$Geometric_mean[97:132],na.rm = TRUE) #139.1868
min(sample$Geometric_mean[97:132],na.rm = TRUE) #129
max(sample$Geometric_mean[97:132],na.rm = TRUE) #152.6434
sd(sample$Geometric_mean[97:132],na.rm = TRUE) #6.753899
#MASCHI BOLOGNA
mean(sample$Geometric_mean[32:64],na.rm = TRUE) #147.4009
min(sample$Geometric_mean[32:64],na.rm = TRUE) #136.4918
max(sample$Geometric_mean[32:64],na.rm = TRUE) #158.4045
sd(sample$Geometric_mean[32:64],na.rm = TRUE) #5.624549
#MASCHI SASSARI
mean(sample$Geometric_mean[97:132],na.rm = TRUE) #139.1868
min(sample$Geometric_mean[97:132],na.rm = TRUE) #129
max(sample$Geometric_mean[97:132],na.rm = TRUE) #152.6434
sd(sample$Geometric_mean[97:132],na.rm = TRUE) #6.753899
