1
MF
Mwamili Foundation
Grant Proposal
Grant Proposal
Mwamili Foundation is a non-for-profit private organization that aims to empower the children from
slums to attain a formal education. The foundation runs two programmes that support this objective.
Fo
TITLE: IMPACT OF PUBLIC SECTOR REFORMS ON SERVICE DELIVERY IN
KENYA: CASE STUDY OF HUDUMA CENTRES
Background to the Study
In the last 20 years, Kenyas civil service has undergone a number of changes. Some of these
changes include employee rationalization
Benedict Curriculum Vitae
BENEDICT KOSKEI
Age: 28 Years | Marital Status: |Nationality: Kenyan
P.O. Box,
Nairobi, Kenya
Telephone: 0721203380
Email: benedictkoskei@gmail.com
PERSONAL PROFILE
A solutions orientated Procurement Professional who can formulat
MECHANICAL PROBLEM ASSIGNMENT
Paper Title Capitalized and Centered
Name of Student
Institution affiliation
1
MECHANICAL PROBLEM ASSIGNMENT
Question 1
The allowable stress can be calculated from;
Safety factor=yield stress/ allowable stress
Allowable stres
CHAPTER ONE
INTRODUCTION
1.1 Background to the study.
Education remains the only major avenue for upward social mobility (Amutabi, 2003). In
developing countries, Kenya included, most of the people still live below the poverty line.
For such persons, the
DE MONTFORT UNIVERSITY
FACULTY OF ART, DESIGN & HUMANITIES
LEICESTER SCHOOL OF ARCHITECTURE
ARCH XXXX
Dissertation
THE ARCHITECTURE OF DENYS LASDUN
RECONSIDERED IN THE 21 ST CENTURY
[Full name of author]
[PNumber]
Session 2014-15
STATEMENT OF ORIGINALITY
Running head: ADAPTIVE TECHNOLOGY FOR VISUALLY IMPAIRED STUDENTS
1
Adaptive Technology for Visually Impaired Students
Students Name
Institutions Affiliation
Adaptive technology for visually impaired
I.
2
Adaptive technology for visually impaired students
1
The Use and Misuse of Computers in Education
Students Name
Institution Affiliation
Professors Name
Date
2
Question 1
I.
Critical Review
Computer programs have been on the move to be integrated in the current education
curriculum especially for the low g
Short-termism (myopia) 1
Short-termism (myopia)
By
Course Code and Name
Professors Name
Date of Submission
Short-termism (myopia) 2
Executive summary
Short- termism is where the management in an organization excessively concentrates on
the short-term goal
A. OBJECTIVES
i.
To learn how a normal squirrel cage induction motor can be started using a
ii.
variable resistor
To study the variation of motor current, speed, slip, efficiency, power factor and
torque of a normal squirrel cage Induction motor as the lo
Lecture Notes on Asymptotic Statistics
Changliang Zou
Prologue
Why asymptotic statistics? The use of asymptotic approximation is two-fold. First, they
enable us to find approximate tests and confidence regions. Second, approximations can be
used theoretic
Random Variables and Their Probability
Distributions, chapter 4.1
Grethe Hystad
February 16, 2011
Grethe Hystad
Random Variables and Their Probability Distributions, chapter 4.1
Random Variables
Numerical outcomes such as
the number of students who receiv
Numerical Control (NC) Defined
Form of programmable automation in which the mechanical
actions of a machine tool or other equipment are
controlled by a program containing coded alphanumeric
data
The alphanumeric data represent relative positions
between
3.2: Averages
How would you describe an average Kenyan youth? An average Kenyan is
one who has characteristics that can be considered to represent Kenya. In
statistics average measures give us values that may be considered to be
typical of the data being
.2.2.2 Disadvantages of Secondary Data
i. Definitions of data used may be different from those of the current study
ii. Measurement error may not be easily approximated from secondary data
iii. Secondary data may suffer from the bias of the source
iv. Rel
1.3: Nature of Statistical Data
Data are pieces of information relating to objects that have been measured
(for example organizations, people, dogs, mosquitos etc) and attributes that
were recorded (for example profits, number of employees, salary, age, s
1.3: Nature of Statistical Data
Data are pieces of information relating to objects that have been measured
(for example organizations, people, dogs, mosquitos etc) and attributes that
were recorded (for example profits, number of employees, salary, age, s
1.4: Variables
When we measure the attributes of an object, we obtain a value that varies
between objects. For example consider the students in a class as objects
and their height as the attribute. The attribute height varies between
objects, hence attrib
3.5: Mode
It originates from the French word La Mode meaning fashionable. It
represents the value that occurs the most often. It is the value with the
highest frequency. In grouped data the mode way be said to be the interval
with the highest frequency. T
2.2.1: Primary data
Primary data may be collected through observation, personal (face to face)
interviews, telephone interviews, self-administered mail questionnaire etc.
2.2.1.1 Advantages of Primary Data
i. Data collected answers a specific study questi
The United States employed a statistician to examine damaged planes returning from bombing
missions over Germany in World War II. He found that the number of returned planes that had
damage to the fuselage was far greater than those that had damage to the
Calculating R 2 3.4
37
Correlation
Goal: Find cause and effect links between variables.
What can we conclude when two variables are highly correlated?
Positive Correlation
High values of x
are associated with
high values of y .
Negative Correlation
High v
Abstract Algebra
Question 1. Evaluation for all c
The first step involves finding the values that make x3+x2+c a maximal ideal. Taking the
value of c=0, The polynomial becomes x3+x2 which can be factorized to x(x2+x). This is a
reducible polynomial hence
Simple linear regression
Yi
= 0 + 1 xi + i ; i = 1, . . . , n
E(Y |X)
= 0 + 1 x
Systematic components: 0 + 1 xi
Stochastic component : error term
the central parameter is the slope parameter 1
If X rises by an unit, than Y rises by 1 units on average
Chapter 9 Correlation and
Regression
Sections 9.1 and 9.2
In sections 9.1 and 9.2 we show how
the least square method can be
used to develop a linear equation
relating two variables. The variable
that is being predicted is called the
dependent variable an