Fahad Hussain Free Computer Education : Statistics & Probability

Hi, welcome to the course of Statistics and Probability for DS, ML and DL. The whole playlist here you can download the code including the PPT Slides that was used in the video session.

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Tutorial 01:

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Tutorial 02:

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Tutorial 03:

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Tutorial 04:

Here is the code of R, excel will be upload in the above link [Soon] if applicable in this tutorial, For better understanding watch the video.

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sample(1:10,3)

sample(1:10, replace=T)

sample(c("red","green"),10,replace=T,prob=c(0.6,0.4))

# Systematic sampling

install.packages("TeachingSampling")

library(TeachingSampling)

P <- c("Mon-8", "Tues-4", "Wed-4", "Thurs-6", "Fri-7","Sat-45","Sun-34","Mon-21", "Tues-11","Wed-34","Thurs-16","Fri-10","Sat-17","Sun-19")

systematic_sample <- S.SY(14,2)

systematic_sample

P[systematic_sample]

# Stratified Samplinglibrary(dplyr)

set.seed(1)

iris %>%

group_by (Species) %>%

sample_n(., 3)

install.packages("sampling")

library(sampling)

stratas = strata(iris, c("Species"),size = c(3,3,3), method = "srswor")

stratas

# Cluster Sampling

set.seed(1)

df <- data.frame(tour = rep(1:10, each=20),

experience = rnorm(200, mean=7, sd=1))

head(df)

#randomly choose 4 tour groups out of the 10

clusters <- sample(unique(df$tour), size=4, replace=F)

#define sample as all members who belong to one of the 4 tour groups

cluster_sample <- df[df$tour %in% clusters, ]

table(cluster_sample$tour)

Tutorial 05:

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Tutorial 06:

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Tutorial 07:

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Tutorial 08:

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Tutorial 09:

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Tutorial 10:

Here is the code of R, excel will be upload in the above link [Soon] if applicable in this tutorial, For better understanding watch the video.

# Arithmetic Mean

x <- c(3, 7, 5, 13, 20, 23, 39, 23, 40, 23, 14, 12, 56, 23)

print(mean(x))

# Geometric Mean

x <- c(1, 5, 9, 19, 25)

print(prod(x)^(1 / length(x)))

# Harmonic Mean

x <- c(1, 5, 8, 10)

print(1 / mean(1 / x))

# Median

x <- c(3, 7, 5, 13, 20, 23, 39,

23, 40, 23, 14, 12, 56, 23)

median(x)

# Mode

x <- c(3, 7, 5, 13, 20, 23, 39,

23, 40, 23, 14, 12, 56,

23, 29, 56, 37, 45, 1, 8)

# Generate frequency table

y <- table(x)

print(y)

# Mode of x

m <- names(y)[which(y == max(y))]

# Print mode

print(m)

#Range

x <- c(1,2,3,4,5)

print(max(x)-min(x))

#weighted mean

x1 <- c(9, 5, 2, 7, 3, 6, 4, 5)

w1 <- c(2, 3, 1, 5, 7, 1, 3, 7)

print(weighted.mean(x1, w1))

# RMS

x <- 1:10

print(sqrt(sum(x^2)/length(x)))

Tutorial 11:

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Tutorial 12:

Here is the code of R, excel will be upload in the above link [Soon] if applicable in this tutorial, For better understanding watch the video.

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data <- c(1,2,3,4,5)

var(data)

# Variance for Sample

#determine length of data

n <- length(data)

#calculate population variance

var(data) * (n-1)/n

# SD for Population

data <- c(1,2,3,4,5)

sd(data)

# SD for Sample

#determine length of data

n <- length(data)

sqrt((n-1)/n) * sd(data) # pop

# COV (Sample and Pop)

data <- c(88, 85, 82, 97, 67, 77, 74, 86)

#calculate CV

cv <- sd(data) / mean(data) * 100

#display CV

Tutorial 13:

Excel Sheet will be upload in the above link [Soon], For better understanding watch the video.

Fahad Hussain CS

Statistics & Probability

For Tutorial 36 to 88 [Final video]

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