Statistics For Decision Making – qnt275 (3 credits)

This course introduces the techniques used for the visualization of numerical data and descriptive statistics in business. 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 qualitative and quantitative business data, interpret numerical business data using measures of central tendency and dispersion, apply fundamental concepts probability theory for inferential decision making for business, and perform a linear regression for trend analysis.

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

Probability

  • Differentiate between discrete and continuous probability.
  • Apply the rules of probability to support business decision making.
  • Apply conditional probability to support business decision making.
  • Describe characteristics of standard normal distribution.

Data Collection and Sampling

  • Apply hypothesis testing to support business decision making.
  • Use confidence intervals to reflect uncertainties in sampling.
  • Evaluate the validity and reliability of data to support making statistical inferences.
  • Explain how to obtain a suitable sample of qualitative and quantitative business data.

Data Analysis

  • Interpret numerical business data using measures of central tendency and variability.
  • Produce tables and charts to organize and display qualitative and quantitative business data.
  • Explain the role of statistics in business and its application in business decision making.
  • Differentiate among the four levels of measurement and how they are used in quantitative and qualitative data collection.

Regression Analysis

  • Forecast a time series to analyze trends.
  • Calculate the correlation between two variables.
  • Perform a linear regression for trend analysis.

Business Decision Making

  • Interpret data analysis results to reach conclusions.
  • Communicate recommendations to decision makers.

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