notice that the last page of a lecture repeats in the next lecture.

• from semester A - the Week Law of Large Numbers. thank you, Dana, for the material!

• content of the notes

• lecture 1 - 7.3 (solution of semester A exam)
• lecture 2 - 8.3 (continuous random variables)
• lecture 3 - 21.3 (comulative distribution function)
• lecture 4 - 22.3 (expected value)
• lecture 5 - 29.3 (moments, normal distribution)
• lecture 6 - 4.4
• lecture 7 - 5.4 (centeral limit-theorem)
• lecture 8 - 25.4 (statistical inference)
• lecture 9 - 26.4 (parameter estimation)
• lecture 10 - 9.5 (parameter estimation - moment method)
• lecture 11 - 10.5 (optimal allocation)
• lecture 12 - 16.5 (parameter estimation - maximal likelihood method)
• lecture 13 - 17.5
• lecture 14 - 23.5 (confidence intervals)
• lecture 15 - 30.5
• lecture 16 - 31.5 (hypothesis assessment)
• lecture 17 - 6.6 (the Neyman-Pearson lemma)
• lecture 18 - 7.6
• lecture 19 - 13.6 (difficulty in achieving the desired significance level in a discrete distribution)
• lecture 20 - 14.6 (assessment of composite hypotheses)
• lecture 21 - 20.6
• lecture 22 - 21.6
• lecture 23 - 25.6 (hypothesis assessment - equality of means)
• lecture 24 - 27.6 (chi square test)

distributions:

• TAVLAT HAHITPALGUT HANORMALIT HASTANDARTIT
• t distribution - pdf
• chi square distribution - pdf
• distributions...