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The law of large numbers and the central limit theorem
Why averages converge and why they are normally distributed.
The law of large numbers: sample means converge to the expectation. The central limit theorem: their fluctuations, scaled by √n, are normal. Picture it: the histogram of averages narrowing and turning bell-shaped as n grows. Think it: the CLT is a fixed-point statement. The normal is the distribution that sums of itself leave unchanged.
ឧទាហរណ៍ដែលបានធ្វើ · limit of (1 + 1/n)^n as n -> oo
Limit of (1 + 1/n)^n as n → oo
ជំហានដោយជំហាន
- \lim_{n \to \infty^+-} \left(1 + \frac{1}{n}\right)^{n}
Try direct substitution first.
- \
As x grows without bound, compare the fastest-growing terms (or divide top and bottom by the highest power).
- = e
Take the limit.
បង្ហាញចម្លើយ
ឥឡូវអ្នក ជ្រើសបញ្ហា ឬវាយបញ្ចូល ឬគូររបស់អ្នកផ្ទាល់ ។ ជំហាននីមួយៗ រូបភាព ចម្លើយត្រូវបានលាក់រហូតដល់អ្នកសួរ ។
គណនីឥតគិតថ្លៃបន្ថែមចំណាំលើមេរៀននីមួយៗ កំណត់ហេតុនៃអ្វីដែលអ្នកបានបញ្ចប់ បញ្ហាដែលអ្នកបានដោះស្រាយនៅកន្លែងមួយ និងគ្រូដែលអ្នកអាចសួរអំពីទំព័រនេះ ។ គណិតវិទ្យាដោយខ្លួនវាគឺបើកចំហសម្រាប់អ្នកទាំងអស់គ្នា ចុះឈ្មោះចូល ឬមិនចុះឈ្មោះចូល ។
ចុះឈ្មោះ ចូលនិមិត្តសញ្ញាដែលបានប្រើនៅទីនេះ
ចុចនិមិត្តសញ្ញាណាមួយសម្រាប់ការកំណត់ពេញលេញ រូបភាព និងតួអក្សរនីមួយៗមានន័យអ្វី ។
របៀប: The law of large numbers and the central limit theorem
- Try direct substitution first.
- As x grows without bound, compare the fastest-growing terms (or divide top and bottom by the highest power).
- Take the limit.
សំណួរដែលមនុស្សសួរ
What is the difference between probability and statistics?
Probability goes from a known model to what the data should look like; statistics goes from data back to the model. Probability theory is the deductive half.
What does the law of large numbers promise?
That the average of many independent samples converges to the expected value. It says nothing about any single trial.
បន្ថែមទៀតក្នុង Probability Theory
Sample spaces and the axiomsRandom variables and expectationThe common distributions