This course will review descriptive data techniques and focus on statistical inference. Participants will apply techniques to analyze data and prepare a project that illustrates sound statistical reasoning. Access to a computer and the Internet is required. A laptop computer is required. Prerequisites: Math X465 (Dealing with Data) and Math X464A (Perspectives on Functions 1)

REVIEW OF EXPLORATORY DATA ANALYSIS TECHNIQUES (DATAREVIEW)
Participants use real-life numerical and categorical data sets to review exploratory data analysis techniques. Participants use the TI-83 and the Fathom™ statistical software as tools to plot, summarize, and analyze data. Participants collect data about their own students to prepare a statistical presentation based on their findings.

USING NORMAL PROBABILITY MODELS (NORMAL)
Participants use data in context to review the Normal Probability model. Participants convert the original units to standardized deviations from the mean. Participants calculate the z-score and determine the probability for an observation from a distribution that is unimodal and symmetric.

SAMPLING AND CENTRAL LIMIT THEOREM (SAMPLING)
Participants will learn various types of techniques for random sampling. Participants will explore inferential statistics by using estimates from a sample to test hypothesis about a population. Participants will use a computer simulation to demonstrate the central limit theorem.

INTRODUCING STATISTICAL INFERENCE THROUGH CONFIDENCE INTERVALS (CONFIDENCE)
Participants revisit and use the information from the Four-Digit Phone Numbers, the Reese’s Pieces®, and Body Temperature Data investigations to construct confidence intervals by hand and via the graphing calculator. Participants examine the relationship between confidence intervals, margins of error, and the sizes of samples. Participants interpret disclaimers found in surveys that use confidence intervals and make conclusions about them.

HYPOTHESIS TESTING (HYPOTHESIS)
Participants will use appropriate tests of hypotheses to check claims made about the population.

BIVARIATE (PAIRED) DATA (BIVARIATE)
Participants use height and arm span data taken from a fifth grade class to review graphing and interpretation techniques for univariate data. Participants use the same data to learn how to plot bivariate data, find the linear regression. Participants determine how well the line predicts values for variables by making a residual plot. Participants observe the distribution of bivariate data sets and describe their graphs by shape, center, and spread. Participants continue to use the TI-83 graphing calculator and Fathom™ software to plot, summarize and analyze data.

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