Students should be able to:

• Define and apply the meaning of descriptive statistics and statistical inference, describe the importance of statistics, and interpret examples of statistics in a professional context;
• Distinguish between a population and a sample;
• Calculate and explain the purpose of measures of location, variability, and skewness;
• Apply simple principles of probability;
• Compute probabilities related to both discrete and continuous random variables;
• Identify and analyze sampling distributions for statistical inferences;
• Identify and analyze confidence intervals for means and proportions;
• Compare and analyze data sets using descriptive statistics, parameter estimation, hypothesis testing;
• Explain how the central limit theorem applies in inference, and use the theorem to construct confidence intervals;
• Calculate and interpret confidence intervals for one population average and one population proportion;
• Differentiate between type I and type II errors;
• Conduct and interpret hypothesis tests;
• Define statistic
• Define parameter
• Define point estimate
• Define interval estimate
• Define margin of error
• Compute the probability of a sample mean being at least as high as a specified value when σ is known
• Compute a two-tailed probability
• Compute the probability of a sample mean being at least as high as a specified value when σ is estimated
• State the assumptions required for item before
• What null hypothesis is tested by ANOVA
• Describe the uses of ANOVA
• Identify and evaluate relationships between two variables using simple linear regression; and
• Discuss concepts pertaining to linear regression, and use regression equations to make predictions.