Upon successful completion of this course, you will be able to:

Graphical and Numerical Data Analysis

• Identify the difference between qualitative, discrete quantitative, and continuous quantitative data.
• Construct and interpret graphical displays of data, including (but not limited to) frequency tables, box plots, line charts, histograms, and bar charts.
• Compute measures of center (mean, weighted mean, median, mode), measures of variation, (range, interquartile range, standard deviation, variance), and measures of position (percentiles, quartiles, standard scores).
• Apply the Empirical Rule 

Sampling/Experimental Design

• Recognize a representative sample and describe its importance.
• Identify methods of sampling.
• Explain the differences between observational studies and experiments. 
• Recognize and explain the key concepts in experiments.

Probability Concepts

• Describe the difference between relative frequency and theoretical probabilities and use each method to calculate probabilities of events. 
• Determine whether two events are mutually exclusive or independent.
• Determine probabilities of composite events using the complement rule, the addition rule, and the multiplication rule.
• Apply the Law of Large Numbers.
• Distinguish between discrete and continuous random variables.
• Use the binomial, normal, and t distributions to calculate probabilities.
• Recognize or restate the Central Limit Theorem and use it as appropriate.
• Identify when the use of the normal distribution is appropriate.
• Identify when the t distribution is preferable to the normal distribution in statistical inference.
• Distinguish between the distribution of a random variable and the sampling distributions of its associated sample statistics.
• Identify the sampling distributions of the sample mean and the sample proportion and use them to make statistical inferences.

Univariate Statistical Inference

• Explain the difference between point and interval estimates.
• Describe the concepts of best estimate and margin of error. 
• Construct confidence intervals for population means and proportions.
• Interpret the confidence level associated with an interval estimate.
• Distinguish between a two-tailed, left-tailed, and right-tailed hypothesis tests.
• Conduct hypothesis tests for population means and proportions.
• Interpret the meaning of both rejecting and failing to reject the null hypothesis.
• Describe Type I and Type II errors in the context of specific hypothesis tests.
• Use a p-value to reach a conclusion in a hypothesis test.
• Identify the interrelationship between hypothesis tests and confidence intervals.

Two-Sample Statistical Inference

• Construct and interpret a confidence interval for the difference between two population means where the samples are independent and the population variances are assumed unequal.
• Construct and interpret a confidence interval for the difference between two population means where the data consists of matched pairs.
• Conduct a hypothesis test for the equality of two population means where the samples are independent and the population variances are assumed unequal.
• Conduct a hypothesis test for the equality of two population means where the data consists of matched pairs.

Correlation and Regression

• Analyze scatterplots for patterns, linearity, and influential points.
• Determine the equation of a least-squares regression line and interpret its slope and intercept.
• Calculate and interpret the correlation coefficient and the coefficient of determination.
• Conduct a hypothesis test for the presence of correlation.

Technology Application

• Construct statistical tables, charts, and graphs using appropriate technology.
• Calculate descriptive and inferential statistics using an appropriate statistical software package.
• Complete statistical project. Students are required to complete some form of semester project in their course that is worth a significant portion of the student's grade. This could be either an individual or group effort, and could be completed in stages through the semester or as a single, stand-alone exercise. As a minimum, the project should require students to manipulate and draw statistical inferences from a large, realistic data set using a statistical software package.