What is the need of the t Distribution According to the central.pdf
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What is the need of the t Distribution? According to the central limit theorem, the sampling distribution of a statistic (like a sample mean) will follow a normal distribution, as long as the sample size is sufficiently large. Therefore, when we know the standard deviation of the population, we can compute a z-score, and use the normal distribution to evaluate probabilities with the sample mean. But sample sizes are sometimes small, and often we do not know the standard deviation of the population. When either of these problems occur, statisticians rely on the distribution of the t statistic (also known as the t score), whose values are given by: t = [ x - ? ] / [ s / sqrt( n ) ] where x is the sample mean, ? is the population mean, s is the standard deviation of the sample, and n is the sample size. The distribution of the t statistic is called the t distribution or the Student t distribution. The t distribution allows us to conduct statistical analyses on certain data sets that are not appropriate for analysis, using the normal distribution. Degrees of Freedom There are actually many different t distributions. The particular form of the t distribution is determined by its degrees of freedom. The degrees of freedom refers to the number of independent observations in a set of data. When estimating a mean score or a proportion from a single sample, the number of independent observations is equal to the sample size minus one. Hence, the distribution of the t statistic from samples of size 8 would be described by a t distribution having 8 - 1 or 7 degrees of freedom. Similarly, a t distribution having 15 degrees of freedom would be used with a sample of size 16. Solution What is the need of the t Distribution? According to the central limit theorem, the sampling distribution of a statistic (like a sample mean) will follow a normal distribution, as long as the sample size is sufficiently large. Therefore, when we know the standard deviation of the population, we can compute a z-score, and use the normal distribution to evaluate probabilities with the sample mean. But sample sizes are sometimes small, and often we do not know the standard deviation of the population. When either of these problems occur, statisticians rely on the distribution of the t statistic (also known as the t score), whose values are given by: t = [ x - ? ] / [ s / sqrt( n ) ] where x is the sample mean, ? is the population mean, s is the standard deviation of the sample, and n is the sample size. The distribution of the t statistic is called the t distribution or the Student t distribution. The t distribution allows us to conduct statistical analyses on certain data sets that are not appropriate for analysis, using the normal distribution. Degrees of Freedom There are actually many different t distributions. The particular form of the t distribution is determined by its degrees of freedom. The degrees of freedom refers to the number of independent observations in a set of da.
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![What is the need of the t Distribution?
According to the central limit theorem, the sampling distribution of a statistic (like a sample
mean) will follow a normal distribution, as long as the sample size is sufficiently large.
Therefore, when we know the standard deviation of the population, we can compute a z-score,
and use the normal distribution to evaluate probabilities with the sample mean.
But sample sizes are sometimes small, and often we do not know the standard deviation of the
population. When either of these problems occur, statisticians rely on the distribution of the t
statistic (also known as the t score), whose values are given by:
t = [ x - ? ] / [ s / sqrt( n ) ]
where x is the sample mean, ? is the population mean, s is the standard deviation of the sample,
and n is the sample size. The distribution of the t statistic is called the t distribution or the
Student t distribution.
The t distribution allows us to conduct statistical analyses on certain data sets that are not
appropriate for analysis, using the normal distribution.
Degrees of Freedom
There are actually many different t distributions. The particular form of the t distribution is
determined by its degrees of freedom. The degrees of freedom refers to the number of
independent observations in a set of data.
When estimating a mean score or a proportion from a single sample, the number of independent
observations is equal to the sample size minus one. Hence, the distribution of the t statistic from
samples of size 8 would be described by a t distribution having 8 - 1 or 7 degrees of freedom.
Similarly, a t distribution having 15 degrees of freedom would be used with a sample of size 16.
Solution
What is the need of the t Distribution?
According to the central limit theorem, the sampling distribution of a statistic (like a sample
mean) will follow a normal distribution, as long as the sample size is sufficiently large.
Therefore, when we know the standard deviation of the population, we can compute a z-score,
and use the normal distribution to evaluate probabilities with the sample mean.
But sample sizes are sometimes small, and often we do not know the standard deviation of the
population. When either of these problems occur, statisticians rely on the distribution of the t](https://image.slidesharecdn.com/whatistheneedofthetdistributionaccordingtothecentral-230630165127-4c0e11c2/85/What-is-the-need-of-the-t-Distribution-According-to-the-central-pdf-1-320.jpg)
![statistic (also known as the t score), whose values are given by:
t = [ x - ? ] / [ s / sqrt( n ) ]
where x is the sample mean, ? is the population mean, s is the standard deviation of the sample,
and n is the sample size. The distribution of the t statistic is called the t distribution or the
Student t distribution.
The t distribution allows us to conduct statistical analyses on certain data sets that are not
appropriate for analysis, using the normal distribution.
Degrees of Freedom
There are actually many different t distributions. The particular form of the t distribution is
determined by its degrees of freedom. The degrees of freedom refers to the number of
independent observations in a set of data.
When estimating a mean score or a proportion from a single sample, the number of independent
observations is equal to the sample size minus one. Hence, the distribution of the t statistic from
samples of size 8 would be described by a t distribution having 8 - 1 or 7 degrees of freedom.
Similarly, a t distribution having 15 degrees of freedom would be used with a sample of size 16.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Solution
Sequence.h
#ifndef MAIN
#define MAIN
#include
class Sequence
{
public:
//TYPEDEFS and MEMBER CONSTANTS
typedef double value;
typedef std::size_t size_type;
static const size_type DEFAULT_CAPACITY = 30;
// CONSTRUCTORS and DESTRUCTOR
Sequence(size_type initial_capacity = DEFAULT_CAPACITY); //constructor
Sequence(const Sequence& start); // COPY CONSTRUCTOR
~Sequence( ); // DESTRUCTOR
// MODIFICATION MEMBER FUNCTIONS
void resize(size_type new_capacity);
void start( );
void advance( );
void inser.
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1)here it will divide the problem into small subparts or subproblems
2)it is also known as top-down approach
3)it will do step by step refinement and do basic programming
4)here the problems are divided in to smaller pieces and find the solution for each problem and
combine
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object oriented programming:
1)here each component is detrmined as an object
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3)each objects which change it state while doing operations
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2. How are these approaches similar? How do they differ?
sol:
one of the similar thing is both will divide problem in to small thing whiel structured desing will
divided into sub problems and oop is divided in to objects
1)sturtured programming will save time instead of writing complex function in oop
2)as they are small program they are very easy to maintain by oop are not
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sol:i feel obejct oriented desing is more comfartable and benificial than structed desing ,because
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become easier to solve the problem in natural way
Solution
1. Briefly describe the structured design approach and the object-oriented design.
structured desing:
1)here it will divide the problem into small subparts or subproblems
2)it is also known as top-down approach
3)it will do step by step refinement and do basic programming
4)here the problems are divided in to smaller pieces and find the solution for each problem and
combine
them together to solve entire problem
object oriented programming:
1)here each component is detrmined as an object
2)the main task is to fiding objects and doing required operations on them
3)each objects which change it state while doing operations
4)by combining and communicating with all required objects get an overall solution to the
problem
2. How are these approaches similar? How do they differ?
sol:
one of the similar thing is both will divide problem in to small thing whiel structured desing will
divided into sub problems and oop is divided in to objects
1)sturtured programming will save time instead of writing complex function in oop
2)as they are small program they are very easy to maintain by oop are not
3)structured programming are easy to understand
3. Which approach do you feel is most beneficial? Why?
sol:i feel obejct oriented desing is more comfartable and benificial than structed desing ,because
in object oriented desing every real world problem in to model of objects and by doing it will
become easier to solve the problem in natural way.
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want to know there will be some measure of interoperability for their hardware and software.
Examples :
IEEE 802.1 (LAN/MAN)
IEEE 802.3 (Ethernet)
IEEE 802.5 (Token Ring)
IEEE 802.11 (Wireless LAN)
4)
IEEE 802 group of standards
https://en.m.wikipedia.org/wiki/IEEE_802
5)
File transfer protocol ( ftp)
https://en.m.wikipedia.org/wiki/File_Transfer_Protocol
Solution
1) Protocols are needed so your computer can interact with other computers for things such as
using the internet and sending and receiving files and emails. Protocols are the \"language\"
computers use to talk to each other. Standards are important so all networks have to follow the
same rules. Standards are a set of guidelines on how different companies need to make their
equipment.
2) a protocol is the special set of rules that end points in a telecommunication connection use
when they communicate. Protocols specify interactions between the communicating entities.
Protocols exist at several levels in a telecommunication connection. For example, there are
protocols for the data interchange at the hardware device level and protocols for data interchange
at t.
argon is inert gas , doesnot chemically reacts wi.pdf
argon is inert gas , doesnot chemically reacts with air . for gases vol% is = to their
mol% vol% of Ar= mol% of Ar= ((no 0f mol of Ar)/(no of mol of Ar+no of mol of air))
Solution
argon is inert gas , doesnot chemically reacts with air . for gases vol% is = to their
mol% vol% of Ar= mol% of Ar= ((no 0f mol of Ar)/(no of mol of Ar+no of mol of air)).
#include sstream #include linkylist.h #include iostream.pdf
#include
#include \"linkylist.h\"
#include
#include
#include
#include
using namespace std;
int main(int argc, char *argv[])
{
//node* head = new node;
LinkedList list(1);
ifstream infile1 (argv[1]);
//make sure the file opens properly and read the contents of the file in to a list
if(!infile1)
{
cout<<\"Your Testfile could not be opened!\"<>users;
if (users.find(\"I\")!=string::npos)
{
//int spot = atoi((users[3]).c_str());
string pot = string(users.substr(2));
stringstream spot(pot);
string data;
cin>>data;
insertAfter(list.getNode(spot),data);
}
/*else if (users.find(\"D\")!=string::npos)
{
int spoty = atoi((users[3]).c_str());
deleteNode(spoty);
}*/
else if (users==\"L\")
{
list.traverse_and_print();
}
}
return 0;
#include
using namespace std;
class Node
{
friend class LinkedList;
private:
int value; /* data, can be any data type, but use integer for easiness */
Node *Next; /* pointer to the next node */
public:
/* Constructors with No Arguments */
Node(void)
: Next(NULL)
{ }
/* Constructors with a given value */
Node(int val)
: value(val), Next(NULL)
{ }
/* Constructors with a given value and a link of the next node */
Node(int val, Node* next)
: value(val), Next(next)
{}
/* Getters */
int getValue(void)
{ return value; }
Node* getNext(void)
{ return Next; }
};
/* definition of the linked list class */
class LinkedList
{
private:
/* pointer of head node */
Node *Head;
/* pointer of tail node */
Node *Tail;
public:
/* Constructors with a given value of a list node */
LinkedList(int val);
/* Destructor */
~LinkedList(void);
/* Function to append a node to the end of a linked list */
void tailAppend(string val);
/* Remove a node with a specific value if it exists */
void remove(int val);
void insertAfter(Node* afterMe, string val);
Node* getNode(int pos);
/* Traversing the list and printing the value of each node */
void traverse_and_print();
};
#include
#include \"linkylist.h\"
#include
using namespace std;
LinkedList::LinkedList(int val)
{
/* Create a new node, acting as both the head and tail node */
Head = new Node(val);
Tail = Head;
}
Node* LinkedList::getNode(int pos)
{
Node* p = Head;
/* Counter */
while (pos--) {
if (p != NULL)
p = p->Next;
else
return NULL;
}
return p;
}
void LinkedList::insertAfter(Node* afterMe, string val)
{
if(afterme != NULL)
{
afterMe->Next = new Node(val, afterMe->Next);
}
}
LinkedList::~LinkedList()
{
}
void LinkedList::tailAppend(string val)
{
/* The list is empty? */
if (Head == NULL) {
/* the same to create a new list with a given value */
Tail = Head = new Node(val);
}
else
{
/* Append the new node to the tail */
Tail->Next = new Node(val);
/* Update the tail pointer */
Tail = Tail->Next;
}
}
void LinkedList::remove(int val)
{
Node *pPre = NULL, *pDel = NULL;
/* Check whether it is the head node?
if it is, delete and update the head node */
if (Head->value == val) {
/* point to the node to be deleted */
pDel = Head;
/* update */
Head = pDel->Next;
delete pDel;
return;
}
pPre = Head;
pDel = Head->Next;
/*.
Function header If a program contain a function definition, that .pdf
Function header :
If a program contain a function definition, that function defnition consist of a
Function header write by taking pointers or in the absense of pointers.
Example :
A function header look like that includes pointers.
void (*current_menu1)(int)
// here current_menu1 is a pointer
void (*payment_details)(int);
// here payment_details is a pointer
void (*payments_receipts)(double );
/ /payments_receipts is a pointer
Solution
Function header :
If a program contain a function definition, that function defnition consist of a
Function header write by taking pointers or in the absense of pointers.
Example :
A function header look like that includes pointers.
void (*current_menu1)(int)
// here current_menu1 is a pointer
void (*payment_details)(int);
// here payment_details is a pointer
void (*payments_receipts)(double );
/ /payments_receipts is a pointer.
GameOfLife.cs using System; using System.Collections.Generic;.pdf
GameOfLife.cs
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading;
namespace A03LeeJoe
{
class GameOfLife
{
private int[, ,] grid;
private int displayedGrid = 0;
private int tempGrid = 1;
private const int DEAD = 0;
private const int ALIVE = 1;
private const int TOP = 5;
private const int LEFT = 20;
//constructor
public GameOfLife()
{
grid = CreateGrid();
}
//methods
private int[, ,] CreateGrid()
{
int[, ,] newGrid = new int[25, 40, 2];
for (int col = 14; col < 27; col++)
{
newGrid[13, col, 0] = ALIVE;
}
for (int row = 8; row < 11; row++)
{
newGrid[row, 12, 0] = ALIVE;
newGrid[row, 20, 0] = ALIVE;
newGrid[row, 28, 0] = ALIVE;
}
return newGrid;
}
public void Start()
{
//Display Grid
Console.SetWindowSize(80, 40);
Setup();
//loop until user presses a key
while (!Console.KeyAvailable)
{
Thread.Sleep(1000);
//perform next move
NextMove();
DisplayGrid();
SetColor(1);
Console.WriteLine();
Console.CursorLeft = LEFT;
Console.WriteLine(\" Press any key to end \", displayedGrid);
}
}
private void Setup()
{
CreateHeader();
DisplayGrid();
//prompt user to press enter
Console.WriteLine();
Console.CursorLeft = LEFT;
SetColor(1);
Console.WriteLine(\" Please press enter to begin \");
Console.ReadLine();
}
private void CreateHeader()
{
Console.SetCursorPosition(LEFT, TOP);
SetColor(1);
Console.WriteLine(\"{0,40}\", \" \");
Console.CursorLeft = LEFT;
Console.WriteLine(\" G A M E O F L I F E \");
Console.CursorLeft = LEFT;
Console.WriteLine(\"{0,40}\", \" \");
}
private void NextMove()
{
//iterate through grid
for (int row = 0; row <= grid.GetUpperBound(0); row++)
{
for (int col = 0; col <= grid.GetUpperBound(1); col++)
{
//each iteration check neighbor count
int neighborCount = GetNeighborCount(row, col);
//determine new life or death and update on temp grid\'
grid[row, col, tempGrid] = UpdateCell(grid[row, col, displayedGrid],
neighborCount);
}
}
//switch the grid\'s 3rd dimension to display new grid
SwitchGrid();
}
private void SwitchGrid()
{
if (displayedGrid == 0)
{
displayedGrid = 1;
tempGrid = 0;
}
else
{
displayedGrid = 0;
tempGrid = 1;
}
}
private int UpdateCell(int stateOfCell, int neighborCount)
{
if (stateOfCell == ALIVE)
{
switch (neighborCount)
{
case 0:
case 1:
return DEAD;
case 2:
case 3:
return ALIVE;
default:
return DEAD;
}
}
else if (neighborCount == 3)
{
return ALIVE;
}
return stateOfCell;
}
private void DisplayGrid()
{
Console.SetCursorPosition(LEFT, TOP + 4);
for (int row = 0; row <= grid.GetUpperBound(0); row++)
{
Console.CursorLeft = LEFT;
for (int col = 0; col <= grid.GetUpperBound(1); col++)
{
SetColor(grid[row, col, displayedGrid]);
Console.Write(\".\");
}
Console.WriteLine();
}
}
private void SetColor(int state)
{
if (state == 1)
{
Console.BackgroundColor = ConsoleColor.Red;
Console.ForegroundColor = ConsoleColor.Yellow;
}
else
{
Console.BackgroundColor = ConsoleColor.Blue;
Console.ForegroundColor = ConsoleColor.Black;
}
}
private int GetNeighborCount(int row, int col)
{
int ne.
With the vast increase in technology, the number of ways that the us.pdf
With the vast increase in technology, the number of ways that the use of cybertechnology impact
the personalities are also increasing. Many of the social scientists has proofs for this.
One incident, in the 1980s, that quickly caught their attention involved a male psychologist who
joined an online forum for disabled persons, where he identified himself as a woman who had
become crippled as a result of an automobile accident.
Mitch Parsell (2008) describes the Internet as a \"powerful new force\" for what he calls \"the
manufacture of identity.\" He believes that this technological medium offers an unparalleled
ability to create ourselves in our own image. It gives users an unprecedented capacity to
determine their initial presentations to others.In short, it enables users to be masters of their
identity.
Now when we talk about Ethics and Technology: Controversies, Questions, and Strategies for
Ethical Computing:
Cybertechnology as a \"Medium of Self-Expression\" Sherry Turkle (2005) notes that by the
mid-1980s, computers had become an \"evocative object\"—i.e., an object that \"provoked self-
reflection.\" Turkle suggested that a computer could be viewed as a model for analyzing and
constructing one\'s identity. She also noted that the computer has become a medium through
which people could discover their personal identity; for example, computers provide a context in
which individuals can try out different cognitive styles and different methods of problem solving
to ultimately discover which style or method they prefer. Because people develop their own
unique style of computing, Turkle argues that the computational environment becomes an
extension of themselves, in much the same way that their manner of dress is an extension of their
personality. So, in her view, a computer can function as a \"medium for self-expression\" as well
as for \"self-discovery.
Impact of cybertechnology on Sense of Self
We have examined some effects that one\'s interactions in virtual or computer-mediated
environments, including MOOs and MUDs, can have for one\'s personal identity.
2 main factors are:
our relation to nature,
our relation to (and sense of place in) the universe.
Williams notes that in the aftermath of these milestones, we have a much humbler view of our
place in the universe than our ancestors did.
How, exactly, has cybertechnology also has influenced the way that we see ourselves in relation
to both the factors—i.e., how has this relatively recent technology already begun to define us as
human beings?
To support Bolter\'s thesis that we have come to see ourselves more and more in computer-like
ways, we have only to reflect for a moment on some of the expressions that we now use to
describe ourselves. For example, Bolter points out that when psychologists speak of \"input and
output states of the brain,\" or of the brain\'s hardware and software, they exemplify Turing\'s
men. And when cognitive psychologists study the \"mind\'s algorithm for searchi.
Let n = size of the hash table hashTable = array of size n, w.pdf
//Let n = size of the hash table
// hashTable = array of size n, which represents the hash table
// hashIndex = represents the index of array
// If the list is EMPTY, we add the element into the list
/*If the list is NOT EMPTY then we compare the newNode to the node already in the list in such
a way to get sorted.*/
struct node
{
int key;
T value;
struct node *next;
};
struct node *hashTable[n] = {NULL};
struct node * createNode(int key, char *name, int age)
{
struct node *newnode;
newnode = (struct node *)malloc(sizeof(struct node));
newnode->key = key;
newnode->value = value;
newnode->next = NULL;
return newnode;
}
add(int key, T value)
{
int hashIndex = key % n;
struct node *newNode = creteNode(key,value);
/*Adding new node to the list with No element */
if (hashTable[hashIndex] == NULL)
{
hashTable[hashIndex] = newnode;
return;
}
/*Adding new node to the sorted list */
struct node *temp1;
struct node *temp2 = hashTable[hashIndex];
temp1 = temp2;
while(temp2 -> key < newNode -> key)
{
temp1 = temp2;
temp2 = temp2 ->next;
}
newnode->next = temp2;
temp1->next = newNode;
return;
}
Solution
//Let n = size of the hash table
// hashTable = array of size n, which represents the hash table
// hashIndex = represents the index of array
// If the list is EMPTY, we add the element into the list
/*If the list is NOT EMPTY then we compare the newNode to the node already in the list in such
a way to get sorted.*/
struct node
{
int key;
T value;
struct node *next;
};
struct node *hashTable[n] = {NULL};
struct node * createNode(int key, char *name, int age)
{
struct node *newnode;
newnode = (struct node *)malloc(sizeof(struct node));
newnode->key = key;
newnode->value = value;
newnode->next = NULL;
return newnode;
}
add(int key, T value)
{
int hashIndex = key % n;
struct node *newNode = creteNode(key,value);
/*Adding new node to the list with No element */
if (hashTable[hashIndex] == NULL)
{
hashTable[hashIndex] = newnode;
return;
}
/*Adding new node to the sorted list */
struct node *temp1;
struct node *temp2 = hashTable[hashIndex];
temp1 = temp2;
while(temp2 -> key < newNode -> key)
{
temp1 = temp2;
temp2 = temp2 ->next;
}
newnode->next = temp2;
temp1->next = newNode;
return;
}.
We can see in course of evolution of plant there was a transition fr.pdf
We can see in course of evolution of plant there was a transition from aquatic to terrestrial life
has taken place.They have adapted lots of features to servive on land.
1.Thay became so large comparative ao aquatic plants.This feature evolve to colonise the land.
2.They have developed well organised vascular system allowing plant to absorb water from soil.
3 .They have evolved strong roots to adhere by the substratum.They can go few meters inside the
soil.
Solution
We can see in course of evolution of plant there was a transition from aquatic to terrestrial life
has taken place.They have adapted lots of features to servive on land.
1.Thay became so large comparative ao aquatic plants.This feature evolve to colonise the land.
2.They have developed well organised vascular system allowing plant to absorb water from soil.
3 .They have evolved strong roots to adhere by the substratum.They can go few meters inside the
soil..
Wave CharacterMany of the things that light does are only expla.pdf
>> Wave Character
Many of the things that light does are only explained sufficiently by thinking of light as a wave.
Refraction and diffraction are two examples. Light refracts when it travels from one medium to
another, because waves travel at different speeds through different media. In a similar way, light
diffracts when it travels between or around objects, because obstacles make the light waves bend.
So, obviously we\'re not wrong about light behaving like a wave. We even use the wave
diffraction of light by reading interference patterns in X-ray crystallography.
>> Particle Behaviour
photoelectric effect, which describes the way electrons are excited and emitted from matter when
they absorb the energy from light. In 1887, Heinrich Hertz observed that a charged object would
create a bigger, faster spark if it was treated with ultraviolet light because the light was actually
exciting the electrons. Further studies by other scientists showed that electrons really could be
knocked out of a metal in response to a beam of light. For a while, scientists thought that the
electrons were just absorbing the energy in the light wave, and then using that energy to jump out
of the metal. The more energy the electrons could absorb, the more energy they could use to
jump out.
A photon is a nearly massless particle carrying a small amount of energy. We use photons to
quantify, or measure the amount of, the energy in light and other electromagnetic waves. Each
photon can excite only one electron at a time. When the intensity of light is increased, then the
number of photons is increased, so a higher number of electrons are knocked loose from the
metal. The energy of electrons does not change because the energy of each photon is still the
same.
Solution
>> Wave Character
Many of the things that light does are only explained sufficiently by thinking of light as a wave.
Refraction and diffraction are two examples. Light refracts when it travels from one medium to
another, because waves travel at different speeds through different media. In a similar way, light
diffracts when it travels between or around objects, because obstacles make the light waves bend.
So, obviously we\'re not wrong about light behaving like a wave. We even use the wave
diffraction of light by reading interference patterns in X-ray crystallography.
>> Particle Behaviour
photoelectric effect, which describes the way electrons are excited and emitted from matter when
they absorb the energy from light. In 1887, Heinrich Hertz observed that a charged object would
create a bigger, faster spark if it was treated with ultraviolet light because the light was actually
exciting the electrons. Further studies by other scientists showed that electrons really could be
knocked out of a metal in response to a beam of light. For a while, scientists thought that the
electrons were just absorbing the energy in the light wave, and then using that energy to jump out
of the metal. The more energy the electr.
The solvent doesnt interfere with the purification processOil Bath.pdf
The solvent doesnt interfere with the purification process
Oil Bath Assembly
Mineral oil or silicone oil is typically used for oil baths in research labs for reactions that require
heating/reflux temperatures up to 200 °C. Oil baths provide more uniform heat in comparison to
other heating devices. A laboratory oil bath is made of an aluminum or stainless steel pan, a
heavy porcelain dish or thick walled Pyrex® glass to withstand breakage and accidental spill.
The electric heating coil (or oil bath on a hot plate) is controlled using a variable voltage
controller (i.e., Variac). The voltage controller is adjusted to increase or decrease the temperature
setting of the oil bath. Heating mantles should NEVER be plugged directly into an outlet.
Bath Material
Useful Range
Flash Point
Advantage/Disadvantage
Risk
Silicone Oil
25 °C to 230 °C
150 °C to
350 °C
Acceptable
MODERATE
Water (rotary evaporator)
0 °C to 70 °C
Acceptable.
LOW
Sand
25°C to 500+ °C
NA
Acceptable.
LOW
Hazards
Hazards associated with the use of oil baths include hot temperatures and fire.
Medical Attention and Emergency Treatment
Use cold water and rinse the affected skin immediately for at least for 15 minutes; remove any
hot oil residue without rubbing the skin. If the affected area is on the hand, wrap with a clean
bandage to prevent further irritation and injury. DO NOT apply any ointments and avoid
breaking blisters. Seek medical attention as necessary. Medical Attention and Emergency
Treatment information is provided on the EHS website.
Personal Protective Equipment (PPE)
Slip-resistant insulated thermal gloves.
Safety glasses with side shields or a face shield.
Lab coat (and wear pants that cover the legs).
Prudent Safety Practices
Storage
Spill Cleanup
Granular clay absorbents, universal polypropylene sorbent pads, or other oil-specific absorbent
pads can be used for cleanup of small incidental spills of both bath oil and vacuum pump oil.
Solid waste from the cleanup can be scooped, bagged, and placed in a metal container or other
compatible container. Oil-specific absorbents absorb only oil and not aqueous materials. For
questions, contact EHS for proper cleanup and disposal procedures.
Disposal of Used Oil
advantages
techniques are understandably used only for samples containing a significant amount of
combustible or organic material as the matrix. With this in mind, let\'s look at the major
advantages
· The ability to decompose large sample sizes.
· The need for little or no reagents.
· The technique is relatively safe.
· The ability to prepare samples containing volatile combustion elements such as sulfur,
fluorine and chlorine (the Schöniger oxygen flask combustion technique is very popular in this
case).
· The technique lends itself to mass production.
The technique of graphite furnace atomic absorption spectrometry (GFAA) incorporates sample
ashing as part of an automatic measurement cycle. Trace analysts have learned a great deal about
the loss of volatile componen.
An object of class StatCalc can be used to compute several simp.pdf
/*
An object of class StatCalc can be used to compute several simple statistics
for a set of numbers. Numbers are entered into the dataset using
the enter(double) method. Methods are provided to return the following
statistics for the set of numbers that have been entered: The number
of items, the sum of the items, the average, the standard deviation,
the maximum, and the minimum.
*/
public class Stats {
private int count; // Number of numbers that have been entered.
private double sum; // The sum of all the items that have been entered.
private double squareSum; // The sum of the squares of all the items.
private double max = Double.NEGATIVE_INFINITY; // Largest item seen.
private double min = Double.POSITIVE_INFINITY; // Smallest item seen.
int[] myList;
public int getCount() {
System.out.println(myList.size());
count = myList.size().
// Return number of items that have been entered.
return count;
}
public double getSum() {
int sum = 0;
for (int i = 0; i < myList.length; i++)
{
sum +=mylist[i];
}
return sum;
}
public double getMean() {
int sum = 0;
for (int i = 0; i < myList.length; i++)
{
sum +=mylist[i];
}
if(count > 0)
return sum / count;
else
return -1;
}
public double getMin() {
int smallest = myList[0];
int largetst = myList[0];
for(int i=1; i< mylist.length; i++)
{
if(mylist[i] > largetst)
largetst = numbers[i];
else if (mylist[i] < smallest)
smallest = numbers[i];
}
return smallest;
}
public double getMax() {
int smallest = myList[0];
int largetst = myList[0];
for(int i=1; i< mylist.length; i++)
{
if(mylist[i] > largetst)
largetst = numbers[i];
else if (mylist[i] < smallest)
smallest = numbers[i];
}
return largetst;
}
stats(int [] data)
{
if(data.count <= count)
{
if(myList.size() > 0)
System.arraycopy( myList, 0, data, 0, count );
}
}
stats()
{
Scanner s = new Scanner(System.in);
int count = s.nextInt();
s.nextLine(); // throw away the newline.
int [] numbers = new int[count];
Scanner numScanner = new Scanner(s.nextLine());
for (int i = 0; i < count; i++) {
if (numScanner.hasNextInt()) {
numbers[i] = numScanner.nextInt();
} else {
System.out.println(\"You didn\'t provide enough numbers\");
break;
}
}
System.arraycopy( myList, 0, numbers, 0, count );
}
stats(int size)
{
setValue(size);
}
private void setValue(int size)
{
count = size;
myList = new int[size];
}
} // end class StatCalc
public class SimpleStats {
public static void main(String[] args) {
Stat calc; // Computes stats for numbers entered by user.
calc = new Stat(100);
int arry[] = {20.20.40,30.80,,70,4,5,6,7,10}
calc.setvalue(array);
double item; // One number entered by the user.
TextIO.putln(\"Enter your numbers. Enter 0 to end.\");
TextIO.putln();
do {
TextIO.put(\"? \");
item = TextIO.getlnDouble();
if (item != 0)
calc.enter(item);
} while ( item != 0 );
TextIO.putln(\"\ Statistics about your calc:\ \");
TextIO.putln(\" Count: \" + calc.getCount());
TextIO.putln(\" Sum: \" + calc.getSum());
TextIO.putln(\" Minimum: \" + calc.getMin());
TextIO.putln(\" Maximum: \" + ca.
The answers can be found below as discussedPart 1)The human gen.pdf
The answers can be found below as discussed:
Part 1)
The human genome is highly vast in nature and hence there are some complicating factors in the
study of human genetics:
1: Size and packing: The human genome is enoromously large in linear seqeunce which makes it
highly difficult to be analysed as a whole. Moreover, the DNA is wounded in complex primary,
secondary and tertiary structures which makes is difficult to isolate intact i.e. formation of
nucleosomes over histones, solenoid formation, high writhe and twist etc. The human genome is
highly compact and along with it, chemical modifications such as
acetylation/methylation/sumoylation/phosphorylation of the DNA-associated proteins also occur.
Similarly, post-transcriptional modifications of mRNA also occur only in eukaryotes.
Compensation: These structural features of the human genome have been well exploited for their
analysis. The size cannot be reduced directly but the complexity of packing can be used by
action of relaxation enzymes such as topoisomerases.
Part 2)
The coding sequences: The human genome is not continuously coding in nature, but it has been
differentiated into stretches of coding euchromatin exons and non-coding intervening
heterochromatin intron sequences. This makes the linearity of the coding sequence highly
compromised and a single mechanism of linear transcription or translation cannot take place.
Compensation: The human genome contains the intron sequences only in the DNA. These
sequences are effectively removed out of the DNA after transcription by mRNA splicing and
thus only exons are remained in the mRNA. Thus, this ensures that only exons can reach the
translation process.
3. Polymorphism: The human genome shows very high degree of single nucleotide
polymorphism (SNP) which shows pattern of inheritance and can be transitted form one person
to another genetically. These SNPs are crucial for providing discrete phenotypic features to
individuals which can be difficult to assess and identify due to large size of human genome.
Compensation: Although there occur SNPs in human genome, the modern techniques such as
next-generation seqeuncing have made it possible to effectively, efficiently and quickly screen
the whole genome for such targets.
Answer part 2)
Variations in Mendelian genetics in humans can be described as below:
1. Co-dominance: Unlike Mendelian genetics, the blood group system of humans show co-
dominance between the allele A and allele B and both of these alleles are completely dominant
over the allele O. This makes occurance of various phenotypes in blood groups i.e. A, B, AB and
O.
2. Incomplete dominance: On contrary to Mendel\'s pattern of inheritance, the texture of hair in
human is inherited genetically following a pattern of incomplete dominance. This deciphers that
an offspring of parents having straight and curly hair will have softly curled hair.
3. Quantitative traits: Unlike Mendel\'s patterns of inheritance, some genes show quantitative.
Solution (3)The smallest respiratory bronchioles subdivide into t.pdf
Solution
:
(3)The smallest respiratory bronchioles subdivide into thin airways called alveolar
ducts.Alveolar sacs are connected to respiratory bronchioles by alveolar ducts.It helps alveoli in
process of gas exchange.Alveolar duct transport inhaled air filled in it to the alveoli and after the
process of gas exchange completed alveolar duct exhibit carbon dioxide gas..
An asset manager of a hedge fund or a mutual funds is the one who di.pdf
An asset manager of a hedge fund or a mutual funds is the one who directly handles the client
money and is responsible for acting in an ethical manner while discharging their duties.
Some of the major ways in which asset managers breach their fiduciary duties are as follows :
(i) Asset Managers generally have access to private information of companies\' profits, next
business move, major public announcements which have direct effect on the stock price. In order
to make their judgement about stocks right, fund managers use manupulative techniques to
inflate stock prices with the help of insider information.
(ii) Fund managers in normal practice met out prefrential treatment to clients whose asset volume
being managed is higher than the rest of the group. In this way only a group of clients gets all the
attention of the fund manager.
(iii) Fund managers usually undertake complex financial transactions and disclose less of these
to the clients and regulators.
(iv) Fund managers engage in activities which more oftem result in conflict of interest. Conflicts
of interest can be in the form of raise larger funds when insufficient opportunities exist, taking
over undisclosed and inappropriate risk in the investment etc.
Solution
An asset manager of a hedge fund or a mutual funds is the one who directly handles the client
money and is responsible for acting in an ethical manner while discharging their duties.
Some of the major ways in which asset managers breach their fiduciary duties are as follows :
(i) Asset Managers generally have access to private information of companies\' profits, next
business move, major public announcements which have direct effect on the stock price. In order
to make their judgement about stocks right, fund managers use manupulative techniques to
inflate stock prices with the help of insider information.
(ii) Fund managers in normal practice met out prefrential treatment to clients whose asset volume
being managed is higher than the rest of the group. In this way only a group of clients gets all the
attention of the fund manager.
(iii) Fund managers usually undertake complex financial transactions and disclose less of these
to the clients and regulators.
(iv) Fund managers engage in activities which more oftem result in conflict of interest. Conflicts
of interest can be in the form of raise larger funds when insufficient opportunities exist, taking
over undisclosed and inappropriate risk in the investment etc..
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Event Link:-
https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-exploring-gemini-ai-and-integration-with-mulesoft/
Agenda
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YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
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Priya Shaw - https://www.linkedin.com/in/priya-shaw
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- 1. What is the need of the t Distribution? According to the central limit theorem, the sampling distribution of a statistic (like a sample mean) will follow a normal distribution, as long as the sample size is sufficiently large. Therefore, when we know the standard deviation of the population, we can compute a z-score, and use the normal distribution to evaluate probabilities with the sample mean. But sample sizes are sometimes small, and often we do not know the standard deviation of the population. When either of these problems occur, statisticians rely on the distribution of the t statistic (also known as the t score), whose values are given by: t = [ x - ? ] / [ s / sqrt( n ) ] where x is the sample mean, ? is the population mean, s is the standard deviation of the sample, and n is the sample size. The distribution of the t statistic is called the t distribution or the Student t distribution. The t distribution allows us to conduct statistical analyses on certain data sets that are not appropriate for analysis, using the normal distribution. Degrees of Freedom There are actually many different t distributions. The particular form of the t distribution is determined by its degrees of freedom. The degrees of freedom refers to the number of independent observations in a set of data. When estimating a mean score or a proportion from a single sample, the number of independent observations is equal to the sample size minus one. Hence, the distribution of the t statistic from samples of size 8 would be described by a t distribution having 8 - 1 or 7 degrees of freedom. Similarly, a t distribution having 15 degrees of freedom would be used with a sample of size 16. Solution What is the need of the t Distribution? According to the central limit theorem, the sampling distribution of a statistic (like a sample mean) will follow a normal distribution, as long as the sample size is sufficiently large. Therefore, when we know the standard deviation of the population, we can compute a z-score, and use the normal distribution to evaluate probabilities with the sample mean. But sample sizes are sometimes small, and often we do not know the standard deviation of the population. When either of these problems occur, statisticians rely on the distribution of the t
- 2. statistic (also known as the t score), whose values are given by: t = [ x - ? ] / [ s / sqrt( n ) ] where x is the sample mean, ? is the population mean, s is the standard deviation of the sample, and n is the sample size. The distribution of the t statistic is called the t distribution or the Student t distribution. The t distribution allows us to conduct statistical analyses on certain data sets that are not appropriate for analysis, using the normal distribution. Degrees of Freedom There are actually many different t distributions. The particular form of the t distribution is determined by its degrees of freedom. The degrees of freedom refers to the number of independent observations in a set of data. When estimating a mean score or a proportion from a single sample, the number of independent observations is equal to the sample size minus one. Hence, the distribution of the t statistic from samples of size 8 would be described by a t distribution having 8 - 1 or 7 degrees of freedom. Similarly, a t distribution having 15 degrees of freedom would be used with a sample of size 16.