Introductory Statistics to Begin February 3

Begins 02/03/2023 (2 consecutive 4-week sessions). Open to any Homeschooling for College Credit student – any grade/age, no placement scores or transcript required and we have a coupon! 3 college credits. It is suggested that the student be in or have completed Algebra 2, but that is not required. There are no set times when you must be online.

This course is 8 weeks long and worth 3 college credits.

Learn More about the course

Tuition $699 (enter coupon code HS4CC for 20% off this course!)

Course Syllabus – Part 1

Week 1

Study Design, Statistical Significance

  • Intro, Study Design
  • Measures of Central Location and Variability
  • Distance
  • Data Format
  • Variables
  • Graphs
  • Null Hypothesis
  • Resampling
  • Normal Distribution
  • Significance

Week 2

Categorical Data, Contingency Tables

  • Categorical Data
  • Graphical Exploration
  • Indexing
  • Simple Probability
  • Distributions
  • Normal Distribution again
  • 2-Way (Contingency) Tables
  • Conditional Probability

Week 3

More Probability, Random Sampling, The Bootstrap

  • Bayes Rule
  • Independence
  • Surveys
  • Random Sampling
  • Bootstrap

Week 4

Confidence Intervals

  • Point Estimates
  • Confidence Intervals
  • Formula Counterparts
  • Standard Error
  • Beyond Random Sampling

Course Syllabus – Part 2

Week 5

Confidence Intervals for Proportions; 2-Sample Comparisons

  • CI for a proportion
  • The language of hypothesis testing
  • A-B tests (2-group comparisons)
  • Bandit Algorithms (briefly)

Week 6

Correlation and Simple (1-variable) Regression

  • Correlation coefficient
  • Significance testing for correlation
  • Fitting a regression line by hand
  • Least squares fit
  • Using the regression equation

Week 7

Multiple Regression

  • Explain or predict?
  • Multiple predictor variables
  • Assessing the regression model
  • Goodness-of-fit (R-squared)
  • Interpreting the coefficients
  • RMSE (root mean squared error)

Week 8

Prediction; K-Nearest Neighbors

  • Using the regression model to make predictions
  • Using a hold-out sample
  • Assessing model performance
  • K-nearest neighbors

Author:

Executive Director of Homeschooling for College Credit

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