ECON G160: Statistics for Business and Economics

Course Outline of Record
Item	Value
Eff Term	Fall 2026
Curriculum Committee Approval Date	05/06/2025
Top Code	170100 - Mathematics, General
Units	3 Total Units (Lecture Units 3)
Hours	54 Total Hours (Lecture Hours 54)
Total Outside of Class Hours	108
Total Student Learning Hours	162
Course Credit Status	Credit: Degree Applicable (D)
Material Fee	No
Basic Skills	Not Basic Skills (N)
Repeatable	No
Open Entry/Open Exit	No
Grading Policy	Standard Letter (S), Pass/No Pass (B)
Local General Education (GE)	Area 2 Mathematics (GB2)
California General Education Transfer Curriculum (Cal-GETC)	Cal-GETC 2A Math Concepts (2A)
Intersegmental General Education Transfer Curriculum (IGETC)	IGETC 2A Math Concepts (2A)
California State University General Education Breadth (CSU GE-Breadth)	CSU B4 Math/Quant.Reasoning (B4)

Course Description

This course introduces probability techniques, hypothesis testing and predictive techniques to facilitate decision-making. Topics include descriptive statistics, probability and sampling distributions, statistical inference, correlation and linear regression, chi-square and t-test, and application of technology for statistical analysis and interpretation of statistical findings. Students will utilize statistical techniques to calculate probabilities, perform hypothesis testing, and interpret the results. Provides an introductory statistics course for business and economics majors and provides applications using data from business, economics, political science, health science, and education. Enrollment Limitation: STAT C1000/STAT C1000E/PSYC G140/SOC G125; students who complete ECON G160 may not enroll in or receive credit for STAT C1000, STAT C1000E, PSYC G140, or SOC G125. PREREQUISITE: Course taught at the level of intermediate algebra or appropriate math placement. Transfer Credit: CSU; UC: Credit Limitations: ECON G160, MATH G103, MATH G160, MATH G160S, PSYC G140 and SOC G125 combined: maximum credit, 1 course. C-ID: MATH 110.C-ID: MATH 110.

Course Level Student Learning Outcome(s)

Interpret levels of statistical significance and confidence intervals.
Calculate probabilities using normal and t-distributions.
Determine the appropriate technique for a hypothesis test and interpret the results.
Interpret levels of statistical significance and confidence intervals.

Course Objectives

Interpret and analyze data presented in tables and graphs, identifying trends, relationships, and potential biases in economic and business applications.
Calculate and evaluate measures of central tendency (mean, median, mode) and measures of variation (range, variance, standard deviation) to assess data distribution and dispersion.
Compare and apply appropriate sampling methods, including simple random, stratified, cluster, and systematic sampling, assessing their advantages, disadvantages, and biases in economic and business research.
Compute and analyze the mean and variance of discrete probability distributions, such as binomial and Poisson, applying them to risk assessment and economic forecasting.
Apply and differentiate between key continuous probability distributions, including normal, t-distribution, f-distribution, and chi-squared, to perform inferential statistical analysis.
Determine and interpret levels of statistical significance, incorporating p-values, confidence intervals, and the margin of error in economic and business decision-making.
Perform hypothesis tests for population means, proportions, and variances, applying one-sample and two-sample hypothesis tests, while identifying and evaluating Type I and Type II errors in statistical inference.
Apply ANOVA and regression analysis to compare means across multiple groups, interpret F-statistics, and assess statistical significance in economic and business research.
Estimate and interpret regression models, including simple and multiple regression analysis, assess model accuracy using R-squared.
Utilize statistical analysis software to manage, analyze, and interpret results from large economic and business data sets.
Apply statistical analysis techniques to analyze data in business, economics, political science, health science, and education applications.
Identify the correct statistical technique to use, including selection of the appropriate distribution and p-value to test for statistical significance.

Lecture Content

Descriptive Techniques
1. Graphical data
  1. Pie and bar charts
  2. Histograms
  3. Scatter plots
  4. Line charts
2. Numerical descriptive techniques
  1. Central tendency
  2. Mean
  3. Median
  4. Mode
  5. Calculate and interpret measures of central tendency
3. Measures of Variability
  1. Range
  2. Variance
  3. Standard deviation
    1. Coefficient of variation
  4. Calculate and interpret measures of variability
4. Relative standing and box plots
5. Linear relationships
Data Collection and Sampling
1. Data sources
2. Sampling methods
3. Sampling and nonsampling errors
Probability
1. Joint, marginal, and conditional probability
  1. Complement rule
  2. Multiplication rule
  3. Addition rule
  4. Conditional probabilities and Bayes's Law
2. Probability trees
3. Random variables: discrete and continuous
4. Expected value
5. Probability distribution: discrete and continuous
  1. Binomial distribution
  2. Normal distribution
  3. t-distribution
  4. Chi-squared distribution
  5. Poisson distribution
Sampling Distributions
1. Mean
2. Proportion
3. Central limit theorem
Population Estimation and Inference
1. Test statistic and confidence interval when standard deviation is unknown
2. Testing and estimating a population variance
3. Inference about a population proportion
  1. Statistic and sampling distribution
  2. Testing and estimating a proportion
  3. Selecting the sample size
4. One population
5. Two populations
  1. Inference about the difference between two means
    1. Independent samples
    2. Matched pairs experiment
  2. Inference about the ratio of two variances
  3. Inference about the difference between two population proportions
Hypothesis Testing and Inference
1. Null and alternative hypothesis
2. Test statistic
3. Type I and Type II errors; power
4. Significance levels
5. p-Value
6. One and two tailed tests
7. Confidence intervals
  1. Single population mean using the z distribution
  2. Single population mean using the t distribution
  3. Population proportion
  4. Selection of the appropriate p-value to construct confidence intervals
  5. Interpretation of confidence intervals
8. Chi-squared tests
  1. Goodness of fit
  2. Independence
  3. Homogeneity
Analysis of Variance (ANOVA)
1. Identify, compute, and interpret one-way ANOVA
2. Randomized block (two-way) ANOVA
  1. Sum of squares
  2. Mean squares
  3. F-test statistic
Regression Analysis
1. Linear regression
2. Correlation
3. Influential points and outliers
4. Goodness of fit
  1. R-square
  2. Adjusted R-square
Statistical Analysis using Technology
1. Graphing calculators
2. Excel or other statistical software
  1. Statistical analysis including descriptive statistics, confidence intervals, and hypothesis tests
  2. Linear regression analysis including ANOVA
3. Interpretation of results
4. Critique of statistical analyses
5. Large data sets
Statistical applications
1. Business
2. Social science (economics, political science, sociology, and psychology)
3. Health science
4. Education
5. Interpretation of statistical results to faciliate decision-making and policy

Method(s) of Instruction

Lecture (02)
DE Live Online Lecture (02S)
DE Online Lecture (02X)

Instructional Techniques

Reading Assignments

Textbook Data files Supplemental readings and case studies

Writing Assignments

Written questions on homework, quizzes, and exams Interpretation and application of calculations and statistical analysis

Out-of-class Assignments

Homework assignments based on lecture and textbook examples Data analysis problems, including the use of statistical software Individual or group projects collecting and analyzing data

Study Non-Contact Hours Recommended

108

Methods of Student Evaluation

Midterm Exam
Final Exam
Short Quizzes
Problem Solving Exercises

Demonstration of Critical Thinking

Problem solving on homework, quizzes, and exams Determine appropriate statistical tests to apply to a given data set Analyze data sets Apply sample statistics to make population conclusions Interpret the results from statistical calculations Critique statistical analyses to evaluate methodology and results

Required Writing, Problem Solving, Skills Demonstration

Complete written solutions to homework, quiz, and exam questions Written reports or projects Analysis and comparison of data Computer lab assignments, such as using Excel, to produce desciptive statistics and perform statistical analyses

Resources Subscreen

Textbook: Keller, G.. Statistics for Management and Economics, 12th ed.. Cengage (2023).
Open Education Resource: Holmes, L., Illowsky, B., Dean, S.. Introductory Business Statistics 2e, ed.. (2023).

Eligible Discipline(s)

Business: Master’s degree in business, business management, business administration, accountancy, finance, marketing, or business education OR bachelor’s degree in any of the above AND master’s degree in economics, personnel management, public administration, or Juris Doctorate (J.D.) or Legum Baccalaureus (LL.B.) degree OR bachelor’s degree in economics with a business emphasis AND master’s degree in personnel management, public administration, or J.D. or LL.B. degree OR the equivalent. Master's degree required.
Economics: Master’s degree in economics OR bachelor’s degree in economics AND master’s degree in business, business administration, business management, business education, finance, or political science OR the equivalent. Master's degree required.

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