Principles of Statistics
Description:Graphical displays and numerical summaries, data collection methods, probability, sampling distributions, confidence intervals and hypothesis testing involving one or two means and proportions, contingency tables, correlation and simple linear regression.
Course Content:Lesson 1: Course Introduction, Syllabus, and Learning Strategies Lesson 2: The Big Picture in Statistics Lesson 3: Producing Data–Sampling Lesson 4: Cautions in Sample Surveys Lesson 5: Producing Data–Experiments Lesson 6: Design of Experiments Lesson 7: Examining Distributions of Quantitative Variables with Graphs Lesson 8: Examining Distributions with Numerical Measures, Part 1 Lesson 9: Examining Distributions with Numerical Measures, Part 2 Lesson 10: Introduction to Probability Lesson 11: Random Variables and Probability Distributions Lesson 12: Normal Probability Distributions and Standard Scores Lesson 13: The Standard Normal Distribution and Its Applications Lesson 14: Sampling Distribution of X-Bar and the Central Limit Theorem Lesson 15: Calculating Probabilities Associated with X-Bar Lesson 16: Statistical Process Control Lesson 17: Introduction to Inference Lesson 18: One-sample t Confidence Interval for Means Lesson 19: Margin of Error and Sample Size Calculations Lesson 20: Overview of Hypothesis Testing Lesson 21: One-sample t-Test for Means Lesson 22: Hypothesis Testing and Confidence Intervals Lesson 23: Error Probabilities and Power of a Test–Cautions in Inference Lesson 24: EDA for Categorical Variables and Sampling Distribution of P-Hat Lesson 25: One-Sample Z-Confidence Interval for Proportions Lesson 26: One-Sample Z-Test for Proportions Lesson 27: Role-Type Classifications; EDA for C to Q Data Lesson 28: Matched Pairs t Procedures Lesson 29: Two-sample t Procedures for Means Lesson 30: Analysis of Variance (ANOVA) Lesson 31: Two-Way Tables and Conditional Distributions Lesson 32: Two-sample z-Procedures for Proportions Lesson 33: Chi-square Test of Independence Lesson 34: Scatterplots and Correlation Lesson 35: Linear Regression and r-squared Lesson 36: Cautions in Correlation and Regression Analysis Lesson 37: Inference for Slope of Regression Line Lesson 38: Inference for Regression Predictions: CI and PI
Online Courses:Course materials are accessed online, and all assignments must be submitted online. Optional course readings may be available but do not include the self-check assignments or graded assignments.
No Materials required for this course!