The course covers the main topics in inferential statistics such as estimation, statistical hypothesis tests, correlation and simple linear regression analysis, and analysis of variance. 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 Excel.
Prerequisites
The students should have successfully finished Statistics 1 in order to achieve the goals projected for this course. Students are supposed to be familiar with Excel/Google sheets.
The learning outcomes of this course are centered into four basic areas: population parameter estimation, statistical hypothesis tests, simple linear correlation and regression, and analysis of variance (ANOVA). On successful completion of this course, students should be able to:
1. Knowledge
| Level of Learning | PLO | CLO | Learning Outcome |
|---|---|---|---|
| Understand | PK1 | CK1 | Explain the methods in inferential statistics such as confidence intervals, statistical tests, simple linear correlation and regression, and ANOVA. |
2. Cognitive Skills
| Level of Learning | PLO | CLO | Learning Outcome |
|---|---|---|---|
| Apply | PC1 | CC1 | Apply statistical analysis techniques in business-related research to some extent. |
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 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 | Confidence Intervals for the mean 1. Find the confidence Interval for the mean when σ is known. (CK1, CCIT1) 2. Determine the minimum sample size for estimating population mean. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 7 (Bluman, 2018, pp. ) |
| 3 | Confidence Intervals for the Mean 1. Describe the properties of t-distribution. (CK1, CCIT1) 2. Find the confidence interval for the mean when σ is unknown. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 7 (Bluman, 2018, pp. 383-389) |
| 4 | Confidence Intervals and Sample Size for Proportions 1. Calculate the confidence interval for a proportion. (CK1, CCIT1) 2. Calculate the minimum sample size for estimating the proportion. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 7 (Bluman, 2018, pp. 390-397) |
| 5 | Confidence Interval for Variances and Standard deviations 1. Describe the properties of chi-distribution. (CK1, CCIT1) 2. Calculate the confidence intervals for variances and standard deviations. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 7 (Bluman, 2018, pp. 399-405) |
| 6 | Review confidence interval and sample size Demonstrate the calculations of confidence interval for mean, proportion, and variance/standard deviation and determination of sample size.(CK1, CK2, CC1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 7 (Bluman, 2018, pp. 406-412) |
| 7 | Introduction to hypothesis testing 1. Explain some key definitions used in hypothesis testing. (CK1) 2. State the null and alternative hypotheses. (CK1) | Lecture Demonstration Questioning Reading: Chapter 8 (Bluman, 2018, pp. 413-419) |
| 8 | Introduction to hypothesis testing 1. Determine the critical values for the z test. (CK1, CCIT1) 2. List five steps in the traditional method of hypothesis test. (CK1) | Lecture Demonstration Questioning Reading: Chapter 8 (Bluman, 2018, pp. 420-426) |
| 9 | One-sample z test for the mean Perform z test for the mean when σ is known, using traditional method and P-value method. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 8 (Bluman, 2018, pp. 426-441) |
| 10 | One-sample t test for the mean and z test for the proportion Perform the test for the mean when σ is unknown using t-test. (CK1,CCIT1) Perform test for single proportion, using z test. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 8 (Bluman, 2018, pp. 442-460) |
| 11 | Chi-square test for the variance or standard deviation Perform a test for single variance or standard deviation, using chi-square test. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 8 (Bluman, 2018, pp. 461-472) |
| 12 | Review one-sample test for the mean, proportion, variance/standard deviation. (CK1,CK2, CC1, CCIT1) | Lecture Questioning Discussion Reading: Chapter 8 (Bluman, 2018, pp. 479-486) |
| 13 | Testing the difference between two means 1. Explain the concept of independent samples. (CK1) 2. Perform the test for the difference between two means, using z test. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 9 (Bluman, 2018, pp. 487-499) |
| 14 | Testing the difference between two means Perform the test for the difference between two means for independent samples, using t test. (CK1 ,CCIT1) | Lecture Demonstration Questioning Reading: Chapter 9 (Bluman, 2018, pp. 499-507) |
| 15 | Paired samples t test and testing the difference between proportions 1. Perform a test for the difference between two means for dependent samples. (CK1, CCIT1) 2. Perform the z test for the difference between two proportions. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 9 (Bluman, 2018, pp. 507-519) |
| 16 | Testing the difference between two variances Describe the characteristics of F distribution. (CK1) Perform the F test for the difference between two variances or standard deviations. (CK1,CCIT1) | Lecture Demonstration Questioning Reading: Chapter 9 (Bluman, 2018, pp. 519-538) |
| 17 | Review two-sample tests. (CK1, CK2, CC1, CCIT1) | Lecture Questioning Discussion Reading: Chapter 9 (Bluman, 2018, pp. 539-518) |
| 18 | Scatter plots and Pearson correlation coefficient 1. Create the scatter plot for two quantitative variables. (CK1, CCIT1) 2. Compute Pearson correlation coefficient. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 10 (Bluman, 2018, pp. 550-558) |
| 19 | Equation of the regression line 1. Test the significance of the correlation coefficient. (CK1, CCIT1) 2. Calculate the equation of the regression line. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 9 (Bluman, 2018, pp. 559-581) |
| 20 | Coefficient of determination and standard error of estimate 1. Create a residual plot to evaluate the goodness of the model. (CK1, CCIT1) 2. Calculate the coefficient of determination and the coefficient of non-determination. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 10 (Bluman, 2018, pp. 582-588) |
| 21 | Standard error of estimate and prediction interval 1. Calculate the standard error of the estimate. (CK1, CCIT1) 2. Compute and interpret the prediction interval. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 10 (Bluman, 2018, pp. 588-592) |
| 22 | Review simple linear correlation and regression. (CK1, CK2, CC1, CCIT1) | Lecture Questioning Discussion Reading: Chapter 10 (Bluman, 2018, pp. 602-607) |
| 23 | Goodness-of-fit test 1. Recall the characteristic of the chi-square distribution. (CK1) 2. Perform a chi-square goodness-of-fit test. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 11 (Bluman, 2018, pp. 609-616, 619-623) |
| 24 | Tests using contingency tables 1. Test two categorical variables for independence, using chi-square. (CK1, CCIT1) 2. Test the proportions for homogeneity, using chi-square. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 11 (Bluman, 2018, pp. 624-639) |
| 25 | Review chi-square test. (CK1, CK2, CC1, CCIT1) | Lecture Questioning Discussion Reading: Chapter 11 (Bluman, 2018, pp. |
| 26 | One-way ANOVA 1. Explain the basic concept of ANOVA. (CK1) 2. Perform one-way ANOVA. (CK1, CCIT1) | Lecture Questioning Discussion Reading: Chapter 12 (Bluman, 2018, pp. 648-660) |
| 27 | Scheffé test and Tukey’s HSD test Perform post hoc tests: Scheffé and Tukey’s HSD test to do pairwise comparisons of the means. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 12 (Bluman, 2018, pp. 660-665) |
| 28 | Two-way ANOVA with replication 1. State the hypotheses in two-way ANOVA with replication.2 (CK1) 2. Perform two-way ANOVA. (CK1, CCIT1) | Lecture Demonstration Questioning Reading: Chapter 12 (Bluman, 2018, pp. 665-678) |
| 29 | Review ANOVA. (CK1, CK2, CC1, CCIT1) | Lecture Questioning Discussion Reading: Chapter 12 (Bluman, 2018, pp. 679-687) |
| 30 | Overall Review(CK1, CK2, CC1, CCIT1, CIP1) | 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 | ||
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% | CK1, CC1, CCIT1, CIP1 | PK1, PK2, PCIT1 |
| In-class tests | 20% | CK1, CC1, CCIT1, CIP1 | PK1, PK2, PCIT1 |
| Assignments | 20% | CK1, CK2, CC1, CCIT1,CIP1 | PK1, PK2, PCIT1, PC1, PIP1 |
| Midterm exam | 25% | CK1, CCIT1 | PK1, PK2, PCIT1 |
| Final exam | 25% | CK1, CC1, CCIT1, CIP1 | PK1, PK2, PCIT1 |
| Total grading score | 100% | ||
There will be two assignments; one is the individual project and another is the group Assignment 1 – Confidence Intervals
| Work Group: | Individual |
| Output format: | APA Format Report |
| Language: | Khmer (English allowed for non-Khmer speaking students) |
|
Assignment:
|
Students individually choose a business-related topic, conduct a survey to obtain data, apply data analysis techniques including confidence intervals, and write a report on the findings. |
Assignment 2 – Simple Linear Correlation and Regression
| Work Group: | Group of three to six students |
| Output format: | APA Format Report |
| Language: | English |
| Assignment: | Student groups choose a business- or economic-related topic in simple linear correlation and regression, analyze the data and write a report on the findings. |
Textbooks
References