STAT 415: STATISTICAL COMPUTING
MAIN INFORMATION PAGE
COURSE PRELIMINARIES
Statistical analysis and research is becoming more and more complex
because of the use of more advanced methods and models. This
course aims at introducing students to methods and techniques for
performing such analysis and research, in particular this course
focuses the development and implementation of statistical
algorithms.
Course Content
There are Five (5) topics in this course, namely:
Topic One: Review of R
Topic Two: Simulation
Topic Three: Optimization and solutions to nonlinear Equations
Topic Four: Monte Carlo Integration
Topic Five: Basic Bayesian Statistics
Course Leaning Outcomes
“Transforming Lives through Quality Education”
Egerton University is ISO 9001:2008 Certified Page
1 of 110
, STAT 415: STATISTICAL COMPUTING
Upon successful completion of this course, a learner should be able
to:
Develop algorithms and write R functions to simulate data from
discrete and continuous distributions
Solve nonlinear equations numerically (Univariate functions)
Optimize univariate functions (perform unconstrained optimization)
Approximate definite integrals using Monte Carlo Methods
Perform basic Bayesian statistical analysis
Need Help?
This course was developed in June 2020 by Obwoge Okenye, Phone:
+254705081120 Email: . (Lecturer of Statistics
in the Department of Mathematics at Egerton University). For any help
do no hesitate to contact me.
For technical support e.g. lost passwords, broken links etc. please
contact tech-support via e-mail . You can
also reach learner support through
.
Assignments/Activities
Assignments/Activities are provided at the end of each topic. Some
assignments/activities will require submission while others will be
self-assessments that do not require submission. Ensure you carefully
check which assignment require submission and those that do not.
Course Learning Requirements
Timely submission of the assignments
2 CATs (30%) – CAT 1 marks are derived from assignments.
Final Examination (70% of total score)
“Transforming Lives through Quality Education”
Egerton University is ISO 9001:2008 Certified Page
2 of 110
, STAT 415: STATISTICAL COMPUTING
A working Laptop
Self-assessment
Self-assessments are provided in order to aid your understanding of
the topic and course content. While they may not be graded, you are
strongly advised to attempt them whenever they are available in a
topic.
“Transforming Lives through Quality Education”
Egerton University is ISO 9001:2008 Certified Page
3 of 110
, STAT 415: STATISTICAL COMPUTING
EGERTON UNIVERSITY
1 female 14 235
5 male 13 111
4 male 34 292
6 female 23 111
5 female 12 211
7 male 34 133
6 female 25 156
8 male 43 79
(f) Arrays , , Error! Bookmark not defined.
[,1] [,2] [1,] 1 3 [2,] 2 4 Error! Bookmark not defined.
, , [,1] [,2] 4
[1,] 5 13
[2,] 6 13
Example
ART=array(1:16,dim=c(2,2,4))
> ART
,,3
[,1] [,2]
[1,] 9 11
[2,] 10 12
,,4
[,1] [,2]
[1,] 13 15
[2,] 14 16
(g) Lists
Example
“Transforming Lives through Quality Education”
Egerton University is ISO 9001:2008 Certified Page
4 of 110
MAIN INFORMATION PAGE
COURSE PRELIMINARIES
Statistical analysis and research is becoming more and more complex
because of the use of more advanced methods and models. This
course aims at introducing students to methods and techniques for
performing such analysis and research, in particular this course
focuses the development and implementation of statistical
algorithms.
Course Content
There are Five (5) topics in this course, namely:
Topic One: Review of R
Topic Two: Simulation
Topic Three: Optimization and solutions to nonlinear Equations
Topic Four: Monte Carlo Integration
Topic Five: Basic Bayesian Statistics
Course Leaning Outcomes
“Transforming Lives through Quality Education”
Egerton University is ISO 9001:2008 Certified Page
1 of 110
, STAT 415: STATISTICAL COMPUTING
Upon successful completion of this course, a learner should be able
to:
Develop algorithms and write R functions to simulate data from
discrete and continuous distributions
Solve nonlinear equations numerically (Univariate functions)
Optimize univariate functions (perform unconstrained optimization)
Approximate definite integrals using Monte Carlo Methods
Perform basic Bayesian statistical analysis
Need Help?
This course was developed in June 2020 by Obwoge Okenye, Phone:
+254705081120 Email: . (Lecturer of Statistics
in the Department of Mathematics at Egerton University). For any help
do no hesitate to contact me.
For technical support e.g. lost passwords, broken links etc. please
contact tech-support via e-mail . You can
also reach learner support through
.
Assignments/Activities
Assignments/Activities are provided at the end of each topic. Some
assignments/activities will require submission while others will be
self-assessments that do not require submission. Ensure you carefully
check which assignment require submission and those that do not.
Course Learning Requirements
Timely submission of the assignments
2 CATs (30%) – CAT 1 marks are derived from assignments.
Final Examination (70% of total score)
“Transforming Lives through Quality Education”
Egerton University is ISO 9001:2008 Certified Page
2 of 110
, STAT 415: STATISTICAL COMPUTING
A working Laptop
Self-assessment
Self-assessments are provided in order to aid your understanding of
the topic and course content. While they may not be graded, you are
strongly advised to attempt them whenever they are available in a
topic.
“Transforming Lives through Quality Education”
Egerton University is ISO 9001:2008 Certified Page
3 of 110
, STAT 415: STATISTICAL COMPUTING
EGERTON UNIVERSITY
1 female 14 235
5 male 13 111
4 male 34 292
6 female 23 111
5 female 12 211
7 male 34 133
6 female 25 156
8 male 43 79
(f) Arrays , , Error! Bookmark not defined.
[,1] [,2] [1,] 1 3 [2,] 2 4 Error! Bookmark not defined.
, , [,1] [,2] 4
[1,] 5 13
[2,] 6 13
Example
ART=array(1:16,dim=c(2,2,4))
> ART
,,3
[,1] [,2]
[1,] 9 11
[2,] 10 12
,,4
[,1] [,2]
[1,] 13 15
[2,] 14 16
(g) Lists
Example
“Transforming Lives through Quality Education”
Egerton University is ISO 9001:2008 Certified Page
4 of 110
