Quantitative Analysis For Business – qnt351 (3 credits)
This course integrates applied business research with descriptive and inferential statistics. Students will learn to apply business research, descriptive, and inferential statistics in making data-driven business decisions. Topics include the examination of the role of statistics in business, statistic terminology, literature review, sampling design, the appropriate use of statistical techniques, and the interpretation of statistical findings.
Apply the six-step process for testing a hypothesis.
Understand the difference between a one-tailed and two-tailed test of significance.
Interpret results using the p-value approach.
Test hypotheses for means (sigma known and unknown).
Meaning of Statistics and Graphical Techniques
Examine the role of statistics and how they are useful for making managerial decisions.
Employ key research and statistical concepts â population versus sample, descriptive statistics versus inferential statistics, parameter versus sample statistic, categorical and continuous variables, levels of measurement, and qualitative versus quantitative data.
Contrast the various probability and non-probability sampling methods.
Evaluate tables and charts that organize and display quantitative versus qualitative business data.
Describing Data Using Numerical Techniques
Discuss various measures of central tendency â mean, median, and mode.
Employ various measures of variation â range, variance, and standard deviation.
Examine skewness of a distribution.
Interpret percentiles, quartiles, and the five-number summary.
Probability Concepts and Probability Distributions
Explain probability and why it is useful in making business decisions.
Analyze different approaches to assigning probability.
Identify the different probability distributions.
Contrast the difference between discrete and continuous random variables.
Evaluate the mean and variance of probability distributions.
Summarize the role and application of the binomial distribution.
Sampling Distribution and Confidence Interval Estimation
Apply normal distribution to calculate probabilities and expected values.
Analyze normal distribution to find probabilities and determining values.
Evaluate confidence interval estimation of means and proportions.
Describe confidence interval estimation of means and proportions.
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