An array is a group of similar elements or data items of the same type collected at contiguous memory locations. In simple words, we can say that in computer programming, arrays are generally used to organize the same type of data.
Array for Integral value:
Array for Integral value
Array for Character value:
Array for Character value
Representation of an Array:
Arrays can be represented in several ways, depending on the different languages. To make you understand, we can take one example of the C language. The picture below shows the representation of the array.
Representation of the array
Arrays always store the same type of values. In the above example:
int is a type of data value.
Data items stored in an array are known as elements.
The location or placing of each element has an index value.
Important: Array can store only the same type of data items. From the below example you can see how it works:
Array work image
In the array a, we have stored all integral values (same type)
In the array b, we have stored all char values (same type)
In the array c, there is integral, float, char all types of values and this is not something an array can store so, option 3 is wrong because an array cannot store different types of values.
Declaration Syntax of Array:
VariableType VariableName[Sequence of Elements];
Example 1: For integral value
int A[10];
Here 10 means, this array A can have 10 integer elements.
2 5 8 44 21 11 7 9 3 1
Example 2: For character value
char B[10];
This array B can have 10 character elements.
f d a b n j l s e y
Initialization of an Array:
If an array is described inside a function, the elements will have garbage value. And in case an array is static or global, its elements will be initialized automatically to 0.
We can say that we can simply initialize elements of an array at the time of declaration and for that, we have to use the proper syntax:
Syntax: datatype Array_Name[size] = { value1, value2, value3, ….. valueN };
Types of Arrays:
There are two types of arrays:
One-Dimensional Arrays
Multi-Dimensional Arrays
One -Dimensional Arrays
A one-dimensional array is a kind of linear array. It involves single sub-scripting. The [] (brackets) is used for the subscript of the array and to declare and access the elements from the array.
Syntax: DataType ArrayName [size];
For example: int a[10];
Multi-Dimensional Arrays
In multi-dimensional arrays, we have two categories:
Two-Dimensional Arrays
Three-Dimensional Arrays
1. Two-Dimensional Arrays
An array involving two subscripts [] [] is known as a two-dimensional array. They are also known as the array of the array. Two-dimensional arrays are divided into rows and columns and are able to handle the data of the table.
Syntax: DataType ArrayName[row_size][column_size];
For Example: int arr[5][5];
2. Three-Dimensional Arrays
When we require to create two or more tables of the elements to declare the array elements, then in such a situation we use three-di
An array is a group of similar elements or data items of the same type collected at contiguous memory locations. In simple words, we can say that in computer programming, arrays are generally used to organize the same type of data.
Array for Integral value:
Array for Integral value
Array for Character value:
Array for Character value
Representation of an Array:
Arrays can be represented in several ways, depending on the different languages. To make you understand, we can take one example of the C language. The picture below shows the representation of the array.
Representation of the array
Arrays always store the same type of values. In the above example:
int is a type of data value.
Data items stored in an array are known as elements.
The location or placing of each element has an index value.
Important: Array can store only the same type of data items. From the below example you can see how it works:
Array work image
In the array a, we have stored all integral values (same type)
In the array b, we have stored all char values (same type)
In the array c, there is integral, float, char all types of values and this is not something an array can store so, option 3 is wrong because an array cannot store different types of values.
Declaration Syntax of Array:
VariableType VariableName[Sequence of Elements];
Example 1: For integral value
int A[10];
Here 10 means, this array A can have 10 integer elements.
2 5 8 44 21 11 7 9 3 1
Example 2: For character value
char B[10];
This array B can have 10 character elements.
f d a b n j l s e y
Initialization of an Array:
If an array is described inside a function, the elements will have garbage value. And in case an array is static or global, its elements will be initialized automatically to 0.
We can say that we can simply initialize elements of an array at the time of declaration and for that, we have to use the proper syntax:
Syntax: datatype Array_Name[size] = { value1, value2, value3, ….. valueN };
Types of Arrays:
There are two types of arrays:
One-Dimensional Arrays
Multi-Dimensional Arrays
One -Dimensional Arrays
A one-dimensional array is a kind of linear array. It involves single sub-scripting. The [] (brackets) is used for the subscript of the array and to declare and access the elements from the array.
Syntax: DataType ArrayName [size];
For example: int a[10];
Multi-Dimensional Arrays
In multi-dimensional arrays, we have two categories:
Two-Dimensional Arrays
Three-Dimensional Arrays
1. Two-Dimensional Arrays
An array involving two subscripts [] [] is known as a two-dimensional array. They are also known as the array of the array. Two-dimensional arrays are divided into rows and columns and are able to handle the data of the table.
Syntax: DataType ArrayName[row_size][column_size];
For Example: int arr[5][5];
2. Three-Dimensional Arrays
When we require to create two or more tables of the elements to declare the array elements, then in such a situation we use three-di
Solution Manual for Introduction to Programming Using Python 1st Edition by S...romanelins
Link full download: https://bit.ly/2SRdYnZ
Language: English
ISBN-10: 0134058224
ISBN-13: 978-0134058221
ISBN-13: 9780134058221
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Very quick introduction to the language R. It talks about basic data structures, data manipulation steps, plots, control structures etc. Enough material to get you started in R.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
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Solution Manual for Introduction to Programming Using Python 1st Edition by S...romanelins
Link full download: https://bit.ly/2SRdYnZ
Language: English
ISBN-10: 0134058224
ISBN-13: 978-0134058221
ISBN-13: 9780134058221
Programming Using Python 1st Edition by Schneider pdf free
1st Edition by Schneider Introduction to Programming Using Python
instant download Introduction to Programming Using Python 1st Schneider
I am Simon M. I am a Data Analysis Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, Nottingham Trent University, UK
I have been helping students with their homework for the past 8 years. I solve assignments related to Data Analysis.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Data Analysis Assignments.
I am Stacy W. I am a Probability Assignment Expert at statisticsassignmenthelp.com. I hold a Masters in Statistics from, University of McGill, Canada
I have been helping students with their homework for the past 8 years. I solve assignments related to Probability.
Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Probability Assignments.
If you are worried about completing your R homework, you can connect with us at Statisticshomeworkhelper.com. We have a team of experts who are professionals in R programming homework help and have years of experience in working on any problem related to R. Visit statisticshomeworkhelper.com or email info@statisticshomeworkhelper.com. You can also call +1 (315) 557-6473 for assistance with Statistics Homework.
Very quick introduction to the language R. It talks about basic data structures, data manipulation steps, plots, control structures etc. Enough material to get you started in R.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
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This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
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3. R Studio is that all the information you
need to write code is available in a
single window.
Additionally, with many shortcuts, auto
completion, and highlighting for the
major file types you use while developing
in R, R Studio will make typing easier
and less error-prone.
R offers a wide variety of statistics-
related libraries and provides a
favorable environment for statistical
computing and design.
ADD A FOOTER 3
13. Data frame
13
A DataFrame is a data structure that organizes data into a 2-
dimensional table of rows and columns, much like a spreadsheet.
DataFrames are one of the most common data structures used in
modern data analytics because they are a flexible and intuitive way of
storing and working with data.
Numerical=c(1,2,3,4,5)
Character=c("one","two","three","four","five")
logical=c(TRUE,FALSE,FALSE,TRUE,TRUE)
data.frame(Character,Numerical,logical) Character Numerical
logical) 1 one 1 TRUE 2 two 2 FALSE 3
three 3 FALSE 4 four 4 TRUE 5 five 5
TRUE
15. 15
A histogram is a graph used to represent
the frequency distribution of a few data
points of one variable. Which is equal to
class interval.
hist(iris$Sepal.Length)
hist(iris$Petal.Width)
hist(faithful$eruptions)
18. It is basically a table where each column is a variable and each row has one
set of values for each of those variables (much like a single sheet in a program
like LibreOffice Calc or Microsoft Excel).
18
Basic
data("iris")
names(iris)
Result "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
dim(iris)
Result = 150 5
str(iris3)
num [1:50, 1:4, 1:3] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
- attr(*, "dimnames")=List of 3
..$ : NULL
..$ : chr [1:4] "Sepal L." "Sepal W." "Petal L." "Petal W."
..$ : chr [1:3] "Setosa" "Versicolor" "Virginica"
19. 19
sum(iris$Sepal.Length)
Result = 876.5
sum(iris$Sepal.Width)
result = 458.6
sum(iris$Petal.Length)
result = 563.7
sum(iris$Petal.Width)
result = 179.9
IQR(iris$Sepal.Length)
Result= 1.3
sort(iris3)
sort(iris$Sepal.Length)
round(iris$Sepal.Length)
20. 20
summary(iris)
• Result
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 setosa :50
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
versicolor:50
Median :5.800 Median :3.000 Median :4.350 Median :1.300
virginica :50
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
summary(iris$Sepal.Length)
• Result
Min. 1st Qu. Median Mean 3rd Qu. Max. 4.300 5.100 5.800
5.843 6.400 7.900
21. 21
sum(iris$Sepal.Length)
Result = 876.5
sum(iris$Sepal.Width)
result = 458.6
sum(iris$Petal.Length)
result = 563.7
sum(iris$Petal.Width)
result = 179.9
IQR(iris$Sepal.Length)
Result= 1.3
22. 22
mean(x, na.rm = T)
Result 30.33333
median(x,na.rm=T)
Result 28.5
summary(x)
result Min. 1st Qu. Median Mean 3rd Qu. Max.
10.00 22.00 28.50 30.33 37.25 55.00 >
sd(x,na.rm=T)
result 15.68014
var(x,na.rm=T)
Result 245.8667
23. 23
A quantile defines a particular part of a data set, i.e. a quantile determines how many values in
a distribution are above or below a certain limit
quantile(x, probs = seq(0,1,.2), na.rm=T)
0% 20% 40% 60% 80% 100%
10 20 28 29 40 55
quantile(x, probs = seq(0,1,.3), na.rm=T)
0% 30% 60% 90%
10.0 24.0 29.0 47.5
quantile(x, probs = seq(0,1,.4), na.rm=T)
0% 40% 80%
10 28 40
quantile(x, probs = seq(0,1,.6), na.rm=T)
0% 60%
10 29
quantile(x, probs = seq(0,1,.9), na.rm=T)
0 0% 90% 10.0 47.5
24. 24
An integer (pronounced IN-tuh-jer) is a whole number (not a fractional number) that can be
positive, negative, or zero. Examples of integers are: -5, 1, 5, 8, 97,
firstTwentyIntegers = 1:30
sum(firstTwentyIntegers)
Result = 465
36. dbinom
dbinom(0, 5, .5) #probabilty of 0 heads in 5 flips
Result 0.03125
dbinom(0:5, 5, .5) #full probability dist. for 5 flips
Result 0.03125 0.15625 0.31250 0.31250 0.15625 0.03125
sum(dbinom(0:2, 5, .5)) #probability of 2 or fewer heads in 5
flips
Result 0.5
sum(dbinom(0:8, 9, .10)) #probability of 6 or fewer heads in 8
flips
Result 1
37. rbinom, binom.test, prop.test
pbinom(2, 5, .5) #same as last line
Result 0.5
table(rbinom(10000, 5, .5)) / 10000
Result 0 1 2 3 4 5
0.0335 0.1544 0.3131 0.3182 0.1532 0.0276
binom.test(29,200, .21)
Result Exact binomial test
data: 29 and 200
number of successes = 29, number of trials = 200, p-value = 0.02374
alternative hypothesis: true probability of success is not equal to 0.21
95 percent confidence interval:
0.09930862 0.20156150
sample estimates:
probability of success
0.145
prop.test(29, 200, .21)
39. dpois(2:7, 4.2) #probabilities of 2,3,4,5,6,or7
result 0.13226099 0.18516538 0.19442365 0.16331587 0.11432111 0.06859266
ppois(1, 9.2) #probabilities of 1 or fewer successes in pois(4.2); sameas sum (0:1,4.2
Result 0.001030602
1-ppois(7,4.2) #probability of 8 or more successes in pois(4.2)
0.001030602
dpois(), ppois()
41. t.test(extra ~ group, data=sleep) # 2-sample t with group id column
Result
Welch Two Sample t-test
data: extra by group
t = -1.8608, df = 17.776, p-value = 0.07939
alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
95 percent confidence interval:
-3.3654832 0.2054832
sample estimates:
mean in group 1 mean in group 2
0.75 2.33
data(sleep)
42. t.test(sleepGrp1, sleepGrp2, conf.level=.99)
Welch Two Sample t-test
data: sleepGrp1 and sleepGrp2
t = -1.8608, df = 17.776, p-value = 0.07939
alternative hypothesis: true difference in means is not equal to 0
99 percent confidence interval:
-4.0276329 0.8676329
sample estimates:
mean of x mean of y
0.75 2.33
data(sleep)
43. Two sample test
Two-sample t test power calculation
n = 40
delta = 0.5
sd = 0.4
sig.level = 0.01
power = 0.998096
alternative = two.sided
NOTE: n is number in *each* group