INTRODUCTION TO DATA ANALYSIS IN BUSINESS (STAT 111)

OVERVIEW

1. COURSE DESCRIPTION

This course introduces the basic concepts and techniques of statistics, which is the study of how to collect, organize, summarize, present, and draw conclusions from data. Statistics is essential for many fields and applications that involve data analysis and interpretation. In this course, you will learn the fundamental principles and methods of statistics, such as descriptive statistics, estimation, hypothesis testing, and regression.

2. REASON FOR THE COURSE

This course can provide valuable skills and knowledge for students who are majoring in accounting and finance. Statistics can help them analyze data, interpret results, and make decisions based on empirical evidence. Statistics can also help them understand the concepts and methods of and assist them in carrying out their tasks of auditing, financial reporting, and risk management. A statistics course can enhance their critical thinking, problem-solving, and communication abilities, which are essential for their future careers. Therefore, taking a statistics course is a worthwhile investment for accounting and finance majors.

3. STUDY HOURS

4. ROLE IN CURRICULUM

Prerequisites:
There is no prerequisite for  Introduction to Data Analysis in Business (STAT 111). However, to take this course students must have a strong foundation in algebra and be able to use a spreadsheet program, particularly Excel and Google Sheets.

SKILLS

  • LEARNING OUTCOMES
  • ASSESSMENT AND GRADING
  • TEACHING METHODS
  • STUDY PLAN
  • TEXTBOOKS AND REFERENCES

5. COURSE LEARNING OUTCOMES (CLO)

On successful completion of this course, students will be able to:

Knowledge Level of
Learning		 Related PLO
Describe statistical methods (CK1)
Describe the methods for organizing, summarizing, interpreting, and drawing conclusions from business-related data		 Understand PCIT1
Cognitive Skills Level of
Learning		 Related PLO
Apply statistical methods (CC1)
Apply statistical methods to solve real-world business problems.		 Apply PCIT1
Write statistical analysis reports (CC2)
Write reports of the statistical analysis results.		 Create PCIT1
Communication, Information Technology, and Numerical Skills Level of
Learning		 Related PLO
Explain statistical results (CCIT1)
Explain statistical results to both technical and non-technical audiences.		 Apply PCIT2
Use spreadsheet software (CCIT2)
Use spreadsheets such as Microsoft Excel/Google Sheets to analyze data.		 Apply PCIT2
Interpersonal Skills and Responsibilities Level of
Learning		 Related PLO
Use statistics ethically (CIP1)
Use statistics in an ethical and responsible way, in order to avoid misleading data and interpretation.		 Apply
 PCIT2

6. ASSESSMENT AND GRADING

   Grades will be determined based on a grading score, calculated using the following assessments and score allocations:

SKILL Assessment Skill Weighting for Grade
Participation Quiz Project final case study
Describe statistical methods (CK1)   60%    40%  10%
Apply statistical methods (CC1)     60%  40% 20%
Write statistical analysis reports (CC2)     60% 40% 30%
Explain statistical results (CCIT1)  100%       10%
Use spreadsheet software (CCIT2)   70%   30% 20%
Use statistics ethically (CIP1)       100%   10%

 

7. TEACHING METHODS

This course utilizes a variety of teaching methods, including lectures, demonstrations, questioning, and problem-solving discussions. Additionally, gamification tools such as Quiz or Kahoot are integrated into the class. Students will be assigned readings, homework, projects, and quizzes.

During the course, there is one project assignment:

Assignment: Data Analysis for Business Research Project
Work Group: Group of 3-5 students
Output Format APA Format Report
Language: English
Description: Each team selects a relevant topic related to service, product, pricing, or a similar aspect. They create a survey to collect data, then use statistical data analysis skills to interpret, summarize, and draw conclusions. Finally, they present their findings in an APA-formatted report.

8. STUDY PLAN

The course targets the 30 lessons in the study plan below. Each lesson is 1.5 class hours; there are a total of 45 class hours.  The study plan below describes the skills to be learned in each lesson. Readings should be completed before the start of each class. Implementation of this study plan may vary depending on the progress and needs of students. References are supporting documents that students may optionally read for deeper understanding or clarification.

 No Lesson Learning Outcomes Teaching (T), and Assessment (A) Methods
 1 Introduction to the course and overview of the course and requirements. Lecture (T)
Discussion (A)
2

Fundamental statistical terms 

  1. Explain the definition of some basic statistical terms. (CK1)
  2. Explain the types of statistical variables (CK1)
  3. Explain levels of Measurement of a Variable (CK1)
 Lecture (T)
Demonstration (T)
Discussion (T, A)
Quizizz/Kahoot (T, A)
Reading: (Bluman, 2023, pp.2-11)
3

Sampling Techniques

  1. Differentiate between probability sampling and non-probability sampling. (CK1, CC1, CCIT1)
  2. Describe the four basic probability sampling methods. (CK1, CC1, CCIT1)
  3. Describe some non-probability sampling methods. (CK1, CC1, CCIT1)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Reading: (Bluman, 2023, pp.11-17)
4

Organizing Data

  1. Organize data into a frequency distribution for categorical data and numerical data. (CK1, CC1, CCIT1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Reading: (Bluman, 2023, pp.42-53)
5

Organizing and presenting data

  1. Present data histogram, stem-and-leaf plot, bar chart, line chart, and Pie chart. (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Quizizz/Kahoot (T, A)
Reading: (Bluman, 2023, pp.59, 76-86)
6

Measures of Central Tendency

  1. Explain the parameters and statistics (CK1)
  2. Calculate and interpret the mean (for raw and grouped data), weighted mean, median, and mode for raw data. (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)Reading: (Bluman, 2023, pp.113-123)
7

Measures of Variation

  1. Explain the concept of variation in a data distribution (CK1)
  2. Calculate and interpret the variance and standard deviation of raw data. (CK1, CCIT2) 
  3. Calculate the coefficient of variation (CK1, CCIT2)
Lecture (T)
Demonstration (T)
Questioning (T, A)Reading: (Bluman, 2023, pp.131-140)
8

Measures of Position

  1. Explain the concept of the measure of position (CK1)
  2. Calculate the percentiles and quartiles using the Google Sheets app. (CK1, CCIT2)
  3. Summarize the data by using a five-number summary and boxplot
 Lecture (T)
Demonstration (T)
Questioning (T, A)Reading: (Bluman, 2023, pp.131-140)
9

A review of descriptive statistics

  1. Review and assess learning outcomes of lectures 2-8
 Discussion (T, A)
Quizizz Game/Kahoot (T, A)
10

Quiz 1

  1. Assess learning outcomes of lectures 2-8 (CK1, CC1, CC2, CCIT2, CIP1)
 Quiz  (A)
11

Estimate the mean

  1. Explain the concept of parameter estimation (CK1)
  2. Discuss the properties of the standard normal distribution ( CK1)
  3. Calculate the confidence interval for the mean when sigma is known and the minimum sample size for estimating the mean from an infinite population. (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)Reading: (Bluman, 2023, pp. 370-382)
12

Estimate the mean

  1. Discuss the properties of Student’s t distribution (CK1)
  2. Calculate the confidence interval for the mean when sigma is unknown. (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)Reading: (Bluman, 2023, pp. 383-389)
13

Estimate the proportion

  1. Calculate a Z confidence interval for the proportion (CK1, CCIT2)
  2. Determine the minimum sample size for estimating the population proportion. (CK1, CC1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)Reading: (Bluman, 2023, pp. 389-397)
14

Estimate the variance and standard deviation

  1. Discuss the properties of the chi-square distribution (CK1)
  2. Calculate the confidence interval for the variance and standard deviation (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Group problem-solving (T, A)
Quizizz/Kahoot (T, A)Reading: (Bluman, 2023, pp. 404-412)
15

A review of parameter estimation

  1. Review and assess learning outcomes of lectures 11-14. (CK1, CC1 CC2, CCIT1, CCIT2)
 Presenting(T)
Group problem-solving (T, A)Quizizz/Kahoot (T, A)
16

Statistical hypothesis test

  1. Describe the overview of a statistical hypothesis test (CK1)
  2. Describe a five-step traditional method and p-value method in statistical tests. (CK2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Quizizz/Kahoot (T, A)
Reading: (Bluman, 2023, pp. 414-427)
17

One Sample Z test for the mean

  1. Demonstrate one sample z-test for a mean(CK1, CC1 CC2, CCIT2)
  2. Explain the relationship between confidence interval and hypothesis test when inferring the mean. (CK1)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp. 428-441)
18

The difference between two means

  1. Demonstrate two-sample Z test (CK1, CC1 CC2, CCIT2)
  2. Calculate a confidence interval for the difference between two means (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp. 490-501)
19

One Sample t-test and independent sample t-test

  1. Demonstrate one sample t-test for a mean(CK1, CC1 CC2, CCIT2)
  2. Demonstrate the independent samples t-test with the assumption of unequal variances (Welch’s t-test) (CK1, CC1 CC2, CCIT2)
  3. Calculate a confidence interval for the difference between two means (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp. 503-505, 507-510)
20

Independent sample t-test and paired sample t-test

  1. Demonstrate the independent samples t-test with the assumption of equal variances (CK1, CC1 CC2, CCIT2)
  2. Calculate a confidence interval for the difference between two means (CK1, CCIT2)
  3. Demonstrate the paired sample t-test. (CK1, CC1 CC2, CCIT2)
  4. Calculate the confidence interval for the difference mean. (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp.509, 511-522)
21

Z test for the proportion(s)

  1. Demonstrate one sample z-test for the proportion (CK1, CC1 CC2, CCIT2)
  2. Demonstrate a two-sample z-test for the difference between proportions  (CK1, CC1 CC2, CCIT2)
  3. Calculate the confidence interval for the difference between the proportions. (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp. 453-461, 523-531)
22

Difference between two variances

  1. Demonstrate the chi-square test for a single variance or standard deviation. (CK1, CC1 CC2, CCIT2) 
  2. Describe the properties of F distribution. (CK1)
  3. Demonstrate the F test for two variances.  (CK1, CC1 CC2, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp. 462-474, 523-531)
23

Non-Parametric Test

  1. Demonstrate the chi-square goodness-of-fit test. (CK1, CC1 CC2, CCIT2) 
  2. Demonstrate the chi-square independence test. (CK1, CC1 CC2, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp.613-645)
24

A review of hypothesis test

  1. Review and assess learning outcomes of lectures 16-23.
 Presenting(T)
Group problem-solving (T, A)
Quizizz/Kahoot (T, A)
25

Scatter Plot and Correlation Analysis

  1. Create a scatter plot and interpret.  (CK1, CC2,  CCIT2)
  2. Calculate the Pearson correlation coefficient and interpret  (CK1, CC2 CCIT2)
  3. Demonstrate a t-test of the Pearson correlation coefficient. (CK1, CC2 CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp.551-567)
26

Least Square Method

  1. Calculate the intercept and slope for the equation of the regression line. (CK1, CCIT2)
  2. Create and interpret the residual plot. (CK1, CCIT2)
  3. Compute and interpret the coefficient of determination, R-squared. (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp.551-567)
27

Confidence Interval and Prediction Interval

  1. Calculate the confidence interval and prediction interval. (CK1, CCIT2)
 Lecture (T)
Demonstration (T)
Questioning (T, A)
Group problem-solving (T, A)Reading: (Bluman, 2023, pp.551-567)
28

A Review of correlation and simple linear regression

  1. Review and assess learning outcomes of lectures 25-27.
 Presenting(T)
Group problem-solving (T, A)
Quizizz/Kahoot (T, A)
29

Quiz 2

  1. Assess learning outcomes of lectures 11-28 (CK1, CC1, CC2, CCIT2, CIP1)
 Quiz (A)
30 Overall review for final exam Presenting(T)
Group problem-solving (T, A)
Quizizz/Kahoot (T, A)

9. TEXTBOOKS AND REFERENCES

Textbooks

Bluman, A. G. (2023). Elementary Statistics: A Step by Step Approach. New York: McGraw Hill Education

References

Lind, D. A., Marchal, W. G., and Wathen, M. (2021). Statistical Techniques in Business and Economics. New York: New York: McGraw Hill Education

Education Resources