After successful completion of the course, the students will be able to: Define basic statistics vocabulary (e.g., levels of measurement (nominal, ordinal, interval, ratio), discrete vs. continuous variables, descriptive vs. inferential statistics, sample vs. population, independent vs. dependent variable, explanatory vs. response variable, confounding variables, experimental vs. observational)   Describe data both graphically and numerical   Create and interpret frequency tables and distributions Create and interpret various graphs (e.g., bar, pie, histogram, boxplot, scatterplot, line) Describe the shape of a graph (e.g., skewed, normal, bimodal) Recognize misleading graphs Calculate mean, median, mode Calculate range, interquartile range, variance and standard deviation Use descriptive statistics to describe data Use 68%, 95%, 99.7% estimates Calculate z-scores Calculate proportion of values under the curve, including percentile ranks   Explain facets of linear regression   Determine “r” and explain its meaning Determine and interpret “r2” Create linear regression equations Interpret slope and vertical intercept of linear regression equations   Choose, administer and interpret the correct tests based on the situation, including identification of appropriate sampling and potential errors   Describe different sampling methods (e.g., random, convenience, stratified) Describe practical applications of the Central Limit Theorem Choose the appropriate hypothesis test given a situation Describe the meaning and uses of alpha and p-values Write the appropriate null and alternative hypotheses, including whether the alternative should be one-sided or two-sided Determine and calculate the appropriate test statistic (e.g. z-test, multiple t-tests, Chi-Square, ANOVA) Determine and interpret “ ” Interpret results of a hypothesis test Differentiate between Type I and Type II errors and explain each in the context of a situation   Use technology in the statistical analysis of data   Communicate in writing the results of statistical analyses of data