Statistics For Decision Making – qnt275 (3 credits)

This course introduces the use of statistics for business decision making. After completion of this course, students will be able to explain how to obtain a suitable sample of business data and evaluate its validity and reliability for statistical inferences, produce tables and charts to organize and display business data, interpret numerical business data using measures of central tendency and variability, apply fundamental concepts probability theory for inferential decision making for business, and perform trend analyses.

This undergraduate-level course is 5 weeks. This course is available to take individually or as part of a degree or certificate program. To enroll, speak with an Enrollment Representative.

Introduction to Statistics

  • Differentiate between quantitative and qualitative data and levels of data measurement.
  • Evaluate tables and charts which organize and display quantitative and qualitative business data.
  • Explain the role of statistics in decision making.
  • Identify the difference between population and sample.
  • Explain the notion of sampling.

Describing Data

  • Interpret numerical business data using measures of central tendency (mean, median, and mode) and variability.
  • Define quartiles and box-and-whisker plots to summarize key characteristics of data.

Basic Probability and Random Variables

  • Differentiate between discrete, continuous, and conditional probability.
  • Apply probabilities to support business decision making.
  • Describe a random variable and its characteristics.
  • Create and interpret probability distributions.

The Normal Curve and Introduction to the Central Limit Theorem

  • Describe the characteristics of the normal curve.
  • Use a normal table to compute basic probabilities.
  • Explain that the random X variable is normal.
  • Define what is meant by sampling error.
  • Develop an intuitive understanding of the Central Limit Theorem.

Business Decision-Making

  • Create and interpret confidence intervals for a population mean.
  • Summarize the elements of hypothesis testing.
  • Interpret data analysis results to reach conclusions.

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