Udacity, Intro to Statistics

Udacity

Intro to Statistics

Making Decisions Based on Data

https://www.udacity.com/course/st101

Intro to Statistics:

24. Confidence Intervals

https://www.udacity.com/course/viewer#!/c-st101/l-48718432/m-48641634

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This is a very fundamental concept in statistics.

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It applies to coin flips and many of the other examples we discussed before.

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A candidate in election might have a true chance that any given voter votes for him.

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Let's call this chance P, the same as the coin flip, and of course if P larger than 0.5 in a 2% run off

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that person will not initially win in most cases, not always, but as a statistician

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we can't assess the two chances, so what we do is to form a sample.

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In coin flipping, we flip n coins. In elections, we ask n randomly chosen people.

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As we know, this gives us an estimated mean.

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It gives us also a variance and therefore, standard deviation.

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What's new now is the confidence interval, and the confidence interval is not the same as the variance.

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In fact, drop the consideration of a variance.

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What this says is based on the outcome, we believe for the parameter µ.

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This is the best guess we can get, which is usually maximum value estimate,

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but we are not quite certain, so you're going to asses a wider range,

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in which we believe with high probability that whatever the outcome will be

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that the outcome will be high probability within this range.

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Very often this range is defined as 95% chance that the final outcome falls into this range.

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That will take some work to compute. So let's dive in.

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26. Hypothesis Test

https://www.udacity.com/course/viewer#!/c-st101/l-48687890/e-48738239/m-48693715

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So now we talk about one of my favorite topics hypothesis testing.

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And the reason why I love this topic is because it's really about decision making.

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We talked a lot about data or often called the sample and we used statistics to extract information

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and now we use more statistics to make decisions and the decisions to be binary, be it a yes or no.

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And the reason why I like this, I think life is all about decision making.

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I think there is no meaning in doing statistics without eventually at least making a decision.

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This is the first time in this class, you're going to make an actual decision.

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So in this box of pills for weight loss and particularly it says that 90% probability,

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taking these pills, a substantial weight loss is being guaranteed.

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Now suppose your job is not to make a decision whether to buy this box or not.

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I can promise you in all likelihood why those pills don't work but whether this claim is correct or not

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so you really care about why the company that sells this product made a correct thing.

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And to do so, you talk to people who have taken this medicine

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and you ask question--did you lose weight?

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Yes or no and say this is a magical medication.

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And 11 out of 15 people tell you yes, but 4 out 15 people tell you no.

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Obviously, this is not 90%. In fact, what do you think it is in percent?