Course Syllabus
Course Objectives: I can...
1) Identify key terms in context including population, sample, parameter, statistic, type of data (quantitative vs. qualitative), and types of variables in an experiment.
2) Understand and apply the different types of sampling including convenience, cluster, stratified, systematic, and simple random.
3) Identify and explain effective experimental design with respect to assignment, treatment, blinding, ethical considerations, and address possible sources of bias including sampling bias, lurking (confounding) variables, and ethical considerations.
4) Create and interpret graphical displays including pie charts, bar graphs, histograms, stem-and-leaf plots, and line graphs, and identify misleading elements of graphical displays.
5) Calculate a five-number summary of a data set, and create and interpret box plots, including determining if any data points are outliers.
6) Use mean, median, and standard deviation to describe the shape and spread of a distribution and calculate how many standard deviations a data point is from the mean.
7) Use and interpret a line of best fit for linear regression, including recognizing instances of extrapolation.
8) Use and interpret a correlation coefficient for linear regression, including determining whether the correlation is significant or not.
9) Answer probability and percentile questions about normally distributed data sets using the Normal Distribution Table.
10) Answer probability and percentile questions about sampling distributions that can be approximated using a normal distribution.
11) Properly interpret the Central Limit Theorem.
12) Construct and interpret a confidence interval for a sample mean using the normal distribution.
13) Construct and interpret a confidence interval for a sample mean using the t-distribution.
14) Construct and interpret a confidence interval for a sample proportion using the normal distribution.
15) Calculate the minimum sample size needed for a particular margin of error, either for a sample mean or a sample proportion.
16) List the null and alternative hypotheses for a one-sample hypothesis test and identify whether the test is left-tailed, right-tailed, or two-tailed.
17) Calculate a z-score or t-score for a sample mean in a one-sample hypothesis test.
18) Calculate a z-score for a sample proportion in a one-sample hypothesis test.
19) Calculate a p-value for a z-score (or the range of p-values for a t-score) in a one-sample hypothesis test and make the appropriate conclusion (comparing the p-value and alpha).
20) Identify a Type I or Type II error in context for hypothesis tests.
21) List the null and alternative hypotheses for a two-sample hypothesis test and identify whether the test is left-tailed, right-tailed, or two-tailed.
22) Calculate a z-score for a sample mean or sample proportion in a two-sample hypothesis test.
23) Calculate a t-score for a sample mean in a matched pairs hypothesis test.
24) Calculate a p-value for a z-score (or the range of p-values for a t-score) in a two-sample hypothesis test and make the appropriate conclusion (comparing the p-value and alpha).
25) Determine the appropriate hypothesis test (one or two samples, means or proportions, matched pairs) for a given scenario.