qnt275 | undergraduate

Statistics For Decision Making

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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 This course is available to take individually or To enroll, speak with an Enrollment Representative.

Course details:

Credits: 3
Continuing education units: XX
Professional development units: XX
Duration: 5

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    Basic Probability and Random Variables

    • Describe a random variable and its characteristics.
    • Create and interpret probability distributions.
    • Determine events (mutually exclusive and independent), Venn diagram, contingency table, and tree diagrams.
    • Differentiate between discrete, continuous, and conditional probability.

    Normal Distribution and Central Limit Theorem

    • Describe the purpose and characteristics of the normal curve.
    • Use the normal curve to find probabilities.
    • Summarize sampling error.
    • Create and interpret confidence intervals for the mean.
    • Define the role of central limit theorem.

    Hypothesis Testing

    • Differentiate between type I and type II errors.
    • Describe hypothesis testing.
    • Calculate and interpret hypothesis tests for the difference between two means.

    Business Decision-Making

    • Interpret data analysis results to reach conclusions.
    • Create and interpret chi-square tests.
    • Calculate and interpret linear regression: line of best fit, correlation coefficient, and outliers.

    Introduction to Descriptive Statistics

    • Recognize and define key terms: element, variable, observation, data set, quantitative, qualitative, discrete, continuous variables.
    • Apply probability and non-probability sampling methods.
    • Create and interpret frequency tables, interpret graphs, histograms and box plots.
    • Differentiate between quantitative and qualitative data and levels of measurement.
    • Understand quartiles, percentiles, central tendency and variability.
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