Lesson 16 - Population and Sample Standard Deviation Calculation
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- Опубликовано: 1 окт 2024
- This is just a few minutes of a complete course.
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The student will learn about population standard deviation and sample standard deviation.
Video stopped at what I was waiting for standard deviation, such a shame as you break things down so well and in a way that’s, makes it very easy to understand
For some reason your video cuts short at 4 minutes and I can't see how you actually go through solving the equation.
I think the last part was going to be that he takes the squareroot of everything on both sides of the equations. So sample standard deviation is just s, and it's equal to the squareroot of everything he had on the right side of the equation for sample variance. Similarly, population standard deviation would be just sigma, and it's equal to the squareroot of everything he had on the right side of the equation for population variance.
Can someone tell me how to distinguish sample std from population std in a given stat problem?
This is my problem 😭😭
Came here for that problem tooo…. Vid wasn’t helpful
A sample is always a subset of the given population
The quality control department of Atherton Mills specifies that the mean net weight per package of cereal should not be less than 20 (μ=20) ounces. Experience has shown that the weights are approximately normally distributed with a standard deviation of 1.5 (σ=1.5) ounces. A random sample of 15 (n = 15) packages yields a mean weight of 19.5 (x̅=19.5) ounces. Is this sufficient evidence to indicate that true mean weight of the packages has decreased? Hint: (H0: μ≥20, HA: μ
P-value = 0.109 > alpha 0.05...do not reject Ho...with 95% confidence, there is not evidence to indicate the true mean weight of the packages has decreased....
Srly? Where's the answer? Lol
Thank you so much ... Again, Thank you so much
For calculating the spread why don't we just do.. summation of |deviations from mean of the data|. Why do we 1st calculate variance then standard deviation?...
hey it's because the deviations from the mean will always equal 0. We get around this issue by squaring everything via variance. As the SD is the root of the variance, we know have an interpretable parameter for understanding the distance between points around the mean.
why do we need to subtract 1 in the denominator?
That is a great question and the detailed answer is beyond the scope of an intro class. It has to do with the detailed derivation on a deeper level. I know this isn't a great answer, but this is more of a "it is defined that way" sort of thing. It relates to degrees of freedom in statistical theory.
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please dumb this down step by step