University Statistics

 

Read OpenStax: “Introductory Statistics” Online

 

Online Lectures:
Business Statistics:
  1. Statistics Lecture 1.1: The Key Words and Definitions For Elementary Statistics
  2. Statistics Lecture 1.3: Exploring Categories of Data, Levels of Measuremen
  3. Statistics Lecture 1.5: Sampling Techniques. How to Develop a Random Sample
  4. Statistics Lecture 2.2: Creating Frequency Distribution and Histograms
  5. Statistics Lecture 3.2: Finding the Center of a Data Set. Mean, Median, Mode
  6. Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
  7. Statistics Lecture 3.4: Finding Z-Score, Percentiles and Quartiles, and Comparing Standard Deviation
  8. Statistics Lecture 4.2: Introduction to Probability
  9. Statistics Lecture 4.3: The Addition Rule for Probability
  10. Statistics Lecture 4.4: The Multiplication Rule for “And” Probabilities.
  11. Statistics Lecture 4.5: Probability of Complementary Events with “At Least One”
  12. Statistics Lecture 4.7: Fundamental Counting Rule, Permutations and Combinations
  13. Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
  14. Statistics Lecture 5.3: A Study of Binomial Probability Distributions
  15. Statistics Lecture 5.4: Finding Mean and Standard Deviation of a Binomial Probability Distribution
  16. Statistics Lecture 6.2: Introduction to the Normal Distribution and Continuous Random Variables
  17. Statistics Lecture 6.3: The Standard Normal Distribution. Using z-score, Standard Score
  18. Statistics Lecture 6.4: Sampling Distributions Statistics. Using Samples to Approx. Populations
  19. Statistics Lecture 6.5: The Central Limit Theorem for Statistics. Using z-score, Standard Score
  20. Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
  21. Statistics Lecture 7.3: Confidence Interval for the Sample Mean, Population Std Dev — Known
  22. Statistics Lecture 7.4: Confidence Interval for the Sample Mean, Population Std Dev — Unknown
  23. Statistics Lecture 7.5: Confidence Intervals for Variance and Std Dev. Chi-Squared Distribution.
  24. Statistics Lecture 8.2: An Introduction to Hypothesis Testing
  25. Statistics Lecture 8.3: Hypothesis Testing for Population Proportion
  26. Statistics Lecture 8.4: Hypothesis Testing for Population Mean. Population Std Dev is Known.
  27. Statistics Lecture 8.4: Hypothesis Testing for Population Mean. Population Std Dev is Unknown.
  28. Statistics Lecture 8.6: Hypothesis Testing Involving Variance and Standard Deviation.
University Statistics:
  1. Lecture 1: Probability and Counting | Statistics 110
  2. Lecture 2: Story Proofs, Axioms of Probability | Statistics 110
  3. Lecture 3: Birthday Problem, Properties of Probability | Statistics 110
  4. Lecture 4: Conditional Probability | Statistics 110
  5. Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110
  6. Lecture 6: Monty Hall, Simpson’s Paradox | Statistics 110
  7. Lecture 7: Gambler’s Ruin and Random Variables | Statistics 110
  8. Lecture 8: Random Variables and Their Distributions | Statistics 110
  9. Lecture 9: Expectation, Indicator Random Variables, Linearity | Statistics 110
  10. Lecture 10: Expectation Continued | Statistics 110
  11. Lecture 11: The Poisson distribution | Statistics 110
  12. Lecture 12: Discrete vs. Continuous, the Uniform | Statistics 110
  13. Lecture 13: Normal distribution | Statistics 110
  14. Lecture 14: Location, Scale, and LOTUS | Statistics 110
  15. Lecture 15: Midterm Review | Statistics 110
  16. Lecture 16: Exponential Distribution | Statistics 110
  17. Lecture 17: Moment Generating Functions | Statistics 110
  18. Lecture 18: MGFs Continued | Statistics 110
  19. Lecture 19: Joint, Conditional, and Marginal Distributions | Statistics 110
  20. Lecture 20: Multinomial and Cauchy | Statistics 110
  21. Lecture 21: Covariance and Correlation | Statistics 110
  22. Lecture 22: Transformations and Convolutions | Statistics 110
  23. Lecture 23: Beta distribution | Statistics 110
  24. Lecture 24: Gamma distribution and Poisson process | Statistics 110
  25. Lecture 25: Order Statistics and Conditional Expectation | Statistics 110
  26. Lecture 26: Conditional Expectation Continued | Statistics 110
  27. Lecture 27: Conditional Expectation given an R.V. | Statistics 110
  28. Lecture 28: Inequalities | Statistics 110
  29. Lecture 29: Law of Large Numbers and Central Limit Theorem | Statistics 110
  30. Lecture 30: Chi-Square, Student-t, Multivariate Normal | Statistics 110
  31. Lecture 31: Markov Chains | Statistics 110
  32. Lecture 32: Markov Chains Continued | Statistics 110
  33. Lecture 33: Markov Chains Continued Further | Statistics 110
  34. Lecture 34: A Look Ahead | Statistics 110
Open Textbooks:

Kundu, S: An Introduction to Business Statistics

Read Online:

 

mc-106

 

Diez, Barr and Çetinkaya-Rundel: OpenIntro Statistics

Read Online:

 

os3

 

Lane: Introduction to Statistics

Read Online:

 

Online_Statistics_Education

 

Download Mystat and install the program and the manuals. You will find an excellent 2200+ pages textbook about Statistics. (Located in the Mystat folder in the Start menu (Windows)).

Statistics_I_II_III_IV

Read Statistics_I_II_III_IV Online:

 

Statistics_I_II_III_IV
Further Readings: