This is an introductory course in statistics. The course mainly focuses on descriptive statistics and probability, which includes definition of statistics, type of variables and measurement levels of data, basic sampling techniques, presentation of data using frequency distributions and graphs, measures of central tendency, measures of variation, measures of position, probability, discrete probability distribution, and normal distribution. In addition to mathematics-based explanation, the course teaches how to use Excel/Google sheet to perform the analysis of data.
Credits: 3
Lecture Hours: 45
Self-Study Hours:
Reading: 60 hours
Review: 16 hours
Assignment: 48 hours
Total Study Hours: 169 hours
Statistics subject plays an important role in the students’ BA program. It represents a foundation in statistical science and prepares the students to enhance their skills in “Quantitative methods for business”, which will have an important impact on the comprehension of further subjects like Microeconomics, Macroeconomics, Finance and Management accounting.
Both Statistics and Quantitative Methods for Business are closely supported by the subject “Applied computer science” that provides the students with the familiarity of working with Excel.
Prerequisites
There are no specific prerequisites for this course. However, the students should have a basic understanding of the mathematical concepts learned in high-school, including working with proportions, summations, factorials and simple algebra problem solving.
Learning outcomes for this course cover the area of descriptive statistics and basic probability. On successful completion of this course, students should be able to:
1. Knowledge
| Level of Learning | PLO | CLO | Learning Outcome |
|---|---|---|---|
| Understand | PK1 | CK1 | Describe the understanding of the introduction to statistics. |
| Understand | PK1 | CK2 | Explain data description using frequency distributions, graph presentations, numerical measures such as measures of central tendency, measures of variation, measures of position, and exploratory data analysis. |
| Understand | PK2 | CK3 | Explain how to solve certain types of probability-related problems. |
2. Cognitive Skills
| Level of Learning | PLO | CLO | Learning Outcome |
|---|---|---|---|
| Apply | PC1 | CC1 | Perform descriptive statistical analysis of business-related data for decision-making. |
3. Communication, Information Technology, and Numerical Skills
| Level of Learning | PLO | CLO | Learning Outcome |
|---|---|---|---|
| Apply | PCIT1 | CCIT1 | Use Excel and/or Google sheet to perform statistical analysis of data. |
4. Interpersonal Skills and Responsibilities
| Level of Learning | PLO | CLO | Learning Outcome |
|---|---|---|---|
| Apply | PIP1 | CIP1 | Work individually and in a team to perform descriptive statistical analysis of data with a limitation. |
The course targets the 30 lessons in the study plan below. Each lesson is 1.5 class hours each; there are a total of 45 class hours. The study plan below describes the learning outcome for each lesson, described in terms of what the student should be able to do at the end of the lesson. Readings should be done by students as preparation before the start of each class. Implementation of this study plan may vary somewhat depending on the progress and needs of students.
| No | Lesson Learning Outcomes | Teaching and Learning Activities, Assessment |
|---|---|---|
| 1 | Introduction to the course and overview of the course and requirements. | Lecture Discussion |
| 2 | Introduction and branches of statistics 1. Explain the definition of statistics and the reasons why to study it. (CK1) 2. Demonstrate knowledge of statistical terms and differentiate between the two branches of statistics. (CK1) | Lecture Demonstration Questioning Reading: Chapter 1 (Bluman, 2018, pp.1-5) |
| 3 | Variables and types of Data 1. Identify types of data. (CK1) 2. Identify the measurement level for each variable. (CK1) | Lecture Questioning Reading: Chapter 1 (Bluman, 2018, pp. 6-10) |
| 4 | Data collection and sampling techniques Use basic sampling techniques in a survey. (CK1, CC1) | Lecture Demonstration Questioning Discussion Reading: Chapter 1 (Bluman, 2018, pp. 11-18, 33-40) |
| 5 | Organizing data Organize data into frequency distribution. (CK2, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 2 (Bluman, 2018, pp. 41-56) |
| 6 | Graphic presentation 1. Represent data in frequency distributions graphically using histograms, frequency polygons, and ogives. (CK2, CCIT1) 2. Describe the shapes of the distribution. (CK2) | Lecture Demonstration Questioning Reading: Chapter 2 (Bluman, 2018, pp. 57-73) |
| 7 | Other types of graphs Represent data using bar graphs, Pareto charts. (CK2, CCIT1) | Lecture Questioning Demonstration Reading: Chapter 2 (Bluman, 2018, pp. 74-78) |
| 8 | Other types of graphs (Finished) 1. Create a time series graph, pie graphs, and stem-and-leaf plots. (CK2, CCIT1) 2. Summarize how data are organized and presented. (CK2, CC1, CCIT1) | Lecture Demonstration Questioning Discussion Reading: Chapter 2 (Bluman, 2018, pp. 78-108) |
| 9 | Measures of central tendency Calculate the mean, median, mode and midrange of a dataset. (CK2, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 3 (Bluman, 2018, pp. 109-119) |
| 10 | Measures of central tendency 1. Estimate the mean of grouped data. (CK2, CCIT1) 2. Compute the weighted mean and geometric mean. (CK2, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 3 (Bluman, 2018, pp. 119-127) |
| Measures of variation Calculate the variance and standard deviation of raw data and grouped data. (CK2, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 3 (Bluman, 2018, pp. 128-138) | |
| 12 | The Use of standard deviation 1. Compute the coefficient of variation and skewness. (CK2, CCIT1) 2. Make use of standard deviation by Chebyshev’s theorem and empirical (normal) rule. (CK2, CC1) | Lecture Demonstration Questioning Reading: Chapter 3 (Bluman, 2018, pp. 138-147) |
| 13 | Measures of position Determine the measures of position such as z-score, percentiles, deciles, and quartiles. (CK2, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 3 (Bluman, 2018, pp. 148-167) |
| 14 | Exploratory data analysis (EDA) 1. Use boxplot and five-number summary to discover various aspects of data. (CK2, CCIT1) 2. Summarize numerical description of data. (CK2, CC1, CCIT1) | Lecture Demonstration Questioning Discussion Reading: Chapter 3 (Bluman, 2018, pp. 168-184) |
| 15 | Sample space and probability 1. Define some key terms for the introduction to probability. (CK3) 2. Compute classical probability and empirical probability. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 4 (Bluman, 2018, pp. 185-200) |
| 16 | Addition Rules for Probability 1. Explain the concept of mutually exclusive events. (CK3) 2. Compute the probability of compound events using the addition rules. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 4 (Bluman, 2018, pp. 201-212) |
| 17 | Multiplication rules and conditional probability 1. Explain the concept of dependent and independent events. (CK3) 2. Compute the probability of compound events using the multiplication rules and the conditional probability. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 4 (Bluman, 2018, pp. 213-226) |
| 18 | Conditional probability and counting rules 1. Compute revised probability using Bayes’ theorem. (CK3, CCIT1) 2. Compute the total number of outcomes using fundamental counting rule, permutation rule, and combination rule. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 4 (Bluman, 2018, pp. 227-241) |
| 19 | Counting rules 1. Solve the probability problems using the counting rules. (CK3, CCIT1) 2. Review probability. (CK3, CC1, CCIT1) | Lecture Demonstration Questioning Discussion Reading: Chapter 4 (Bluman, 2018, pp. 242-254) |
| 20 | Probability distribution Construct a probability distribution for a random variable. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 5 (Bluman, 2018, pp. 255-264) |
| 21 | Mean, variance, Standard Deviation, and Expectations Compute the Mean, variance standard deviation, and expected value for a discrete random variable. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 5 (Bluman, 2018, pp. 265-275) |
| 22 | Binomial distribution Calculate a binomial probability distribution. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 5 (Bluman, 2018, pp. 276-289) |
| 23 | Poisson distribution and hypergeometric distribution 1. Calculate Poisson probability distribution. (CK3, CCIT1) 2. Calculate a hypergeometric distribution. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 5 (Bluman, 2018, pp. 290-302) |
| 24 | Review discrete probability distributions. (CK3, CC1, CCIT1) | Lecture Demonstration Questioning Discussion Reading: Chapter 5 (Bluman, 2018, pp. 303-309) |
| 25 | Normal distributions 1. Explain the properties of a normal distribution. (CK3) 2. Find the area under the standard normal distribution given various z values and vice versa. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 6 (Bluman, 2018, pp. 312-327 ) |
| 26 | Applications of the Normal distribution Calculate probabilities for a normally distributed variable by transforming it into a standard normal variable. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 6 (Bluman, 2018, pp. 228-332) |
| 27 | Applications of the normal distribution Calculate specific data values for given percentages, using the standard normal distribution. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 6 (Bluman, 2018, pp. 332-343) |
| 28 | Central limit theorem 1. Explain the central limit theorem. (CK3) 2. Use the central limit theorem to solve problems involving sample means for large samples. (CK3, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 6 (Bluman, 2018, pp. 344-353) |
| 29 | Normal approximation to the binomial distribution 1. Use the normal approximation to compute probabilities for a binomial variable.(CK3, CCIT1) 2. Review normal distribution (CK3, CC1, CCIT1) | Lecture Demonstration Questioning Discussion Reading: Chapter 6 (Bluman, 2018, pp. 354-368) |
| 30 | General Review (All CK, CC1, CCIT1) | Lecture Questioning Discussion |
| Guest Lecture (If any) The importance of statistics in auditing, finance, or accounting (CC1, CIP1) (This session may vary due to the availability of the guest.) | Lecture Discussion | |
| Total Hours : 45 hours | ||
This course is taught with a variety of teaching methods such as lecture, demonstration, questioning and discussion. Students will be assigned readings, homeworks, projects, and in-class tests.
Grades will be determined based on a grading score, calculated using the following assessments and score allocations:
| Assessment | Weight of each assessment | Learning Outcome Assessed | |
|---|---|---|---|
| CLO | PLO | ||
| Participation | 10% | All CK, CC1, CCIT1, CIP1 | PK1, PCIT1 |
| In-class tests | 20% | All CK, CC1, CCIT1 | PK1, PK2, PC1, PCIT1 |
| Assignments | 20% | CK1, CK2, CC1, CCIT1, CIP1 | PK1, PK2, PC1, PCIT1, PIP1, PIP2 |
| Midterm exam | 25% | CK1, CK2, CC1, CCIT1 | PK1, PK2, PCIT1 |
| Final exam | 25% | CK2, CK3, CC1, CCIT1 | PK1, PK2, PCIT1 |
| Total grading score | 100% | ||
During the course there are two project assignments:
Assignment 1 – Descriptive Data Analysis
Work Group: Individual
Output format: APA Format Report
Language: Khmer (English allowed for non-Khmer speaking students)
Assignment: Students will analyse and present descriptive statistics related to a set of data.
Assignment 2 – Descriptive Statistics Research
Work Group: Group of three to six students
Output format: APA Format Report
Language: English
Assignment: Each group chooses a topic related to service, product, price and the like. They prepare a questionnaire to conduct a survey to obtain data, then apply their skill in descriptive statistics to organize and summarize the data. Finally, they submit a report on their findings.
Textbooks
References