School of Arts and Sciences

College in High School Course Syllabus

Statistics 0200
Basic Applied Statistics
(4 Credits)

This course teaches methods of descriptive and inferential statistics. Topics include data collection and description, hypothesis testing, correlation and regression, the analysis of variance, and contingency tables. Students will learn how to use a statistical computer package.

Two years of high school algebra are recommended.

The grade is determined by the student's performance on two midterm exams, a comprehensive final, and teacher evaluation.

The recommended textbooks are either The Basic Practice of Statistics, fourth edition, by David S. Moore (W.H. Freeman & Co.). Alternate textbooks may be used but must include the material in this syllabus.

The following topics are covered in the University of Pittsburgh Statistics 0200 course. The statistical computer package MINITAB is used for all topics below.

  1. Introduction: What is statistics? Types of data.
  2. Descriptive statistics (one variable): histograms, box plots, symmetry and skewness, mean, median, percentiles, range, interquartile range, the standard deviation, changes of location, and scale.
  3. Association and regression: scatter plots, covariance, correlation, fitting straight lines, meaning of slope and intercept, residuals and residual plots, coefficient of determination (r-squared). Use of transformed dependent variables.
  4. Causation and evidence: Use of random number tables and designed experiments to attempt to answer questions of causation. Some basic types of sampling and experiments—stratified samples, simple and blocked designs.
  5. Probability: Random variables and their distributions: indices of distributions (mean, standard deviation, etc.). Statistical independence, independent observations. Normal and binomial distributions: use of tables. Computer simulation of observations from distributions.
  6. Distribution of sample mean from random samples: Central limit theorem, law of large numbers. Computer experiments to demonstrate these laws.
  7. Confidence intervals for means (known standard deviation) and proportions in one sample: Construct confidence intervals from data; use computer experiments to illustrate concept.
  8. Tests of hypotheses about means (known standard deviation) and proportions in one sample: P-value, level of significance. Type I and Type II error. Meaning of (but not calculation of) power and relation to effect size, sample size, and size of standard deviation. Computer experiments to illustrate these concepts.
  9. One-sample, paired-sample, and two-sample t-tests: Degrees of freedom and use of t-tables. Related confidence intervals, interpretation of computer output concerning tests and confidence intervals. Advantages and disadvantages of paired designs over two-sample designs.
  10. Introduction to more advanced topics: One-way analysis of variance tests (ANOVA table, degrees of freedom, sums of squares and mean squares, F statistic and F tables), contingency tables and chi-square tests for independence, inference concerning the slope(s), and intercept in linear regression models.
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