Embedded Files
About Course
About Course
This course introduces the basic statistical concepts in general applications. The topics include basic concepts of sampling methods, method of data collection, introductory methods in descriptive and inferential statistics.
Specific topics include methods of describing data, correlation & regression analysis, time series analysis, index numbers and probability. Students will be exposed to statistical project which involve analyzing primary or secondary data in enhancing their understanding in solving real problems.
Course Learning Outcomes
Course Learning Outcomes
At the end of the course, students should be able to:
1. Explain the concept of descriptive statistics.
2. Interpret the appropriate statistical solution in a given problem.
3. Demonstrate leadership, autonomy and responsibility skills in handling group project.
Course Syllabus
Course Syllabus
CHAPTER 1: Introduction to Statistics
CHAPTER 1: Introduction to Statistics
Definition of statistics
Terms in statistics
Types of statistics (Descriptive and Inferential)
Sources of data (Primary and Secondary)
Types of variables (Qualitative, Quantitative Continuous and Quantitative Discrete)
Scale of measurement (Nominal, Ordinal, Interval and Ratio)
Sampling techniques
Data Collection Methods
Designing a Questionnaire
CHAPTER 2: Describing Data
Data organization and presentation (Frequency distribution table, pie chart, bar chart, contingency table, stem and leaf, box and whisker plot, histogram, cumulative frequency distribution and ogives)
Numerical Measures (Grouped and Ungrouped data)
Measures of Central Tendency
Measures of Location
Measures of Skewness
Measures of Dispersion (Range, standard deviation, variance, coefficient of variation)
CHAPTER 3: Correlation and Regression
Scatter diagram
Correlation Analysis (Pearson's Product Moment Correlation)
Simple linear regression
Coefficient of determination
CHAPTER 4: Index Number
CHAPTER 4: Index Number
Types of index numbers
Unweighted index numbers (Simple relative, simple average relative, simple aggregate index)
Weighted index number (weighted aggregate index, Laspeyres' and Paasche's index)
CHAPTER 5: Time Series Data Analysis
Component of time series
Multiplicative time series model
Trend Analysis (Moving Average Method)
Seasonal Variations
Forecasting (using multiplicative model)
CHAPTER 6: Introduction to Probability
Set theory
Addition rules
Multiplication rules and conditional probability
Counting rules (Permutations and Combinations)
Tree Diagram
Bayes' Theorem
Lecturers
Lecturers
SITI HASMA HAJAR BINTI MAT ZIN
SITI HASMA HAJAR BINTI MAT ZIN
Email: hasmahajar@uitm.edu.my
Position: Statistics Lecturer at Universiti Teknologi MARA Cawangan Segamat
Qualification:
Master of Science Applied Statistics
Bachelor of Science (Hons) Statistics
*Role: As a main lecturer who teaching this course and managing classroom website
SUHANAH ROSNAN
SUHANAH ROSNAN
Email: suhan009@uitm.edu.my
Position: Computer Science Lecturer at Universiti Teknologi MARA Cawangan Segamat
Qualification:
Master in Computer Science
Bachelor Degree in Intelligent System
*Role: As a lecturer who managing classroom website
Page updated
Report abuse