# Principles of Statistics

## \$ 549.00

Instructor: Perpetua Lynne Nielsen MS
Credits: 3.00

## 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!