• An overview of the ideas and concepts that are basic to modern statistics. Topics include descriptive statistics, probability, estimation, hypothesis testing, and linear regression. Students will be exposed to applications from a variety of fields.

• This course focuses on statistical reasoning and the solving of problems using real-world data rather than on computational skills. Emphasis is on interpretation and evaluation of statistical results that arise from simulation and technology-based computations using technology more advanced than a basic scientific calculator, such as graphing calculators with a statistical package, spreadsheets, or statistical computing software. Topics must include data collection processes (observational studies, experimental design, sampling techniques, bias), descriptive methods using quantitative and qualitative data, bivariate data, correlation, and least squares regression, basic probability theory, probability distributions (normal distributions and normal curve, binomial distribution), confidence intervals and hypothesis tests using p-values.

• This is a college entry statistics course, where any student can take this as long as you have basic algebra skills. AP Statistics students could also take this course to review all the basic concepts.

• There is one chapter on Probability, and it covers all the basics principle of probabilities needed for 1st semester statistics.

• We use TI Graphing Calculator to calculate all the statistics formulas in this course.

Upon successful completion of this course, the student should be able to do the following:

1. Produce and interpret descriptive statistics, graphically, numerically, and in tabular format.

2. Calculate and interpret probability using union and intersection rules.

3. Explain the concepts of random variable and distribution.

4. Use technology to calculate probabilities with the normal and binomial distributions.

5. Produce a confidence interval estimate from a given sample.

6. Explain the rationale of hypothesis testing.

7. Carry out, with the aid of technology, a variety of hypothesis tests, including z-tests and t-tests and interpret the meaning of the results.

8. Use correlation analysis to determine the strength of a linear relationship between bivariate data and apply linear regression to describe this relationship.