Download statistics-syllabus

Course Nr.: 0403111
STATISTICS
Course The course introduces students to the discipline
Description: of Statistics as a science of understanding and
analyzing data. The goals of this course are as
follows:
 Recognize the importance of data collection
and determine how they affect the scope of
inference.
 Use statistical packages in R-Studio to
summarize data numerically and visually,
and to perform data analysis.
 Have a conceptual understanding of the
unified nature of statistical inference.
 Apply estimation and testing methods
(confidence intervals and hypothesis tests) to
analyze single variables and the relationship
between two variables in order to
understand natural phenomena and make
data-based decisions.
 Model and investigate relationships between
two or more variables within a regression
framework.
 Complete practical assignments that employ
simple statistical inference and modelling
techniques.
Text Book: Introductory Statistics with Randomization
and Simulation
David M. Diez, Christopher D. Barr, Mine
Catinkaya-Rundel, 1st Ed (2014)
Lecture summaries and presentations:
www.alveronika.wordpress.com
Page Statistics
Tafila Technical
University
College of Business
Dept. of Economics
2nd Semester
2016/2017
Lecturer:
Dr. Veronika Alhanaqtah
Office Hours:
Monday, Wednesday
9:30 – 11:00
(office 347)
Software: R/R Studio (free resource)
Evaluation
Official website: https://cran.r-project.org
Nr.
1st Exam (20 Marks): March 9
2nd Exam (20 Marks): April 20
Final Exam (50 Marks)
Laboratory Assignments (10 Marks)
Main Topic
0
Introduction
1
Introduction to data
Sub-Topic
What Statistics is about
Course requirements
1.1. Introductory concepts and vocabulary
1.1.1. Data Set. Unit of observation
1.1.2. Variable. Variable types
1.1.3. Online data libraries
2
Descriptive statistics:
Univariate analysis
2.1.
One variable graphics and number summaries
2.1.1. Histogram. Symmetric and asymmetric distribution
2.1.2. Box plot. Number summaries: minimum, 25th, 50th, 75th
percentiles, maximum. Outlier. Boundary fence
2.2. Measures of central tendency and variability
2.2.1. Center: median, mean, mode
2.2.2. Spread: range, inter-quartile range (IQR), standard
deviation (SD), skewness, kurtosis
2.3. Transformation and standardizing
2.3.1. Mathematical transformations
2.3.2. Standardizing (Z-score)
2.4. Normal distribution
2.4.1. Bell-shaped distribution
2.4.2. Empirical rule
2.4.3. Standard normal probabilities
2.4.4. Chebyshev’s theorem
First Exam
3
Descriptive statistics:
Bivariate analyses
3.1. Relationship between two categorical variables - Mosaic Plots
and Contingency Tables
3.2. Relationship between one categorical and one numeric
variables – Side-by-side boxplots
3.3. Relationship between two numeric variables – Correlation
and Regression
4
Inferential statistics: foundations
4.1.
4.2.
4.3.
4.4.
4.5.
4.6.
4.7.
Simple linear regression
The linear correlation coefficient
Modeling linear relationships with randomness present
The least squares regression line
Statistical inferences about β2
The coefficient of determination
Estimation and prediction
Second Exam
5
Theory of probability
5.1. Numerical characteristics of random variables
5.1.1. Types of averages
Simple arithmetical average
Weighted arithmetical average
Geometric average
Chronological average
Harmonic average
5.1.2. Expected value
5.1.3. Variance
5.1.4. Standard deviation
5.1.5. Covariance
5.1.6. Covariance matrix
5.1.7. Correlation coefficient
5.2. Events and probabilities (additional)
5.3. Probability distributions (additional)
Laboratory Assignments in computer classes (R/R-Studio): in the course of a semester
Final Exam