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Dec 26, 2024
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ECON 2021 - Statistics for Business and Economics Credits: 3 Hours/Week: Lecture None Lab None Course Description: This course is an introduction to quantitative decision making. It will focus on probabilistic and statistical techniques as applied to business decision-making. Topics include probability, classical statistics, expected value, and sampling. This course includes the use of a statistical software package. MnTC Goals None
Prerequisite(s): None Corequisite(s): None Recommendation: None
Major Content
- Comparisons Involving Means
- Comparisons Involving Proportions and a Test of Independence
- Continuous Probability Distributions.
- Data and Statistics.
- Descriptive Statistics: Numerical Measures
- Descriptive Statistics: Tabular and Graphical Presentations
- Discrete Probability Distributions
- Hypothesis Tests
- Interval Estimation
- Introduction to Probability
- Multiple Regression
- Sampling and Sampling Distributions
- Simple Linear Regression
Learning Outcomes At the end of this course students will be able to:
- Explain the differences between the regression model, the regression equation, and the estimated regression equation
- Perform regression analysis to develop an equation that estimates mathematically how two variables are related
- Explain how the analysis of variance procedure can be used to determine if the means of more than two populations are equal
- Analyze the difference between two population means when the samples are independent and when the samples are matched
- Compute probabilities using a normal probability distribution. Understand the role of the standard normal distribution in this process
- Discuss the role probability information plays in the decision making process
- Formulate and test hypotheses about a population mean and/or a population proportion
- Interpret tabular summarization procedures for quantitative data such as: frequency and relative frequency distributions, cumulative frequency and cumulative relative frequency distributions
- Interpret summarization procedures for qualitative data such as : frequency and relative frequency distributions, bar graphs and pie charts
Competency 1 (1-6) None Competency 2 (7-10) None Courses and Registration
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