Earn these career-relevant skills in weeks, not years.
- Evaluate the use of statistics in real-world applications.
- Describe descriptive and inferential statistical methods and the fundamental elements of statistics.
- Differentiate between qualitative and quantitative variables.
- Create visual representations of quantitative and qualitative data.
- Describe the distribution of quantitative data in terms of shape, center, and spread.
- Calculate central tendency and variability in the context of data sets, accounting for possible effects of extreme data points.
- Differentiate between discrete, continuous, and conditional probability.
- Describe how independence and conditional probability are applied in interpreting data.
- Calculate probabilities of compound events in a uniform probability model by applying the rules of probability.
- Evaluate the need to calculate expected values for the purpose of solving problems.
- Describe how the characteristics of standard normal distribution and the Empirical Rule can be used to interpret the significance of standard deviation.
- Evaluate the steps of hypothesis testing.
- Determine the distribution of the sample mean and the distribution of the sample.
- Distinguish an hypothesis regarding a population proportion verses a hypothesis regarding a population mean.
- Distinguish between the t-test and z-test.
- Formulate null and alternative hypotheses.
- Calculate a test statistic in a one-tailed and two-tailed tests.
- Determine statistically whether to accept or reject the null hypothesis.
- Interpret the results of a hypothesis test.
- State the steps of the decision rule for a hypothesis regarding the standard deviation.
- Perform a chi-square test using equal and unequal expected frequencies.
- Determine normality using a goodness-of-fit test.
- Summarize categorical data in two-way frequency tables.
- Interpret relative frequencies in the context of the data.
- Investigate patterns of association in bivariate data using scatter plots.
- Use technology to calculate coefficient of correlation and coefficient of determination.
- Distinguish between correlation and causation.
- Develop a single linear regression equation.
- Predict the value of y for a given value of x using a single linear regression equation.
- Apply analysis of residuals, standard error of estimate, confidence intervals, and prediction.
- Review course topics and objectives.
- Apply statistics to real-world scenarios.
