Sorry about the lateness

The first thing we learned yesterday was an easier way to find z scores.

1. Go to stat then edit.

2. Then go to L1 and push in the list of numbers.

3. Go to L2 and at the top push in (L1-mean)/the standard deviation.

4. Now you have your z scores :)



We also learnt about Normal distribution





Above is a picture of normal distribution. Notice how it has a bell shape, this will help you indentify normal distribution in a graph. Most of the data in normal distribution is continuous (has decimal places). There are certain properties of normal distribution which are...
1. 68% of data lies within 1 standard deviation of the mean.
2. 95% of data lies within 2 standadrd deviations of the mean.
3. 99.7% of data lies within 3 standard deviation of the mean.

There are other facts about normal distribution. The area under the curve equals one. The x axis is an asymptote for the curve (the line goes close but never hits zero). Tha graph is symetrical. as well the farther away you go out the less data there is.

Another thing we talked about in class is how to find the mean with the z score. To do this you have to follow the formula (z score * standard devation)+ mean



Next is Daniel

Today's Slides: March 27

Here they are ...

Applied 40S March 27, 2009

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Niwatori's Corner (Statistics - Zscores!)

KONICHIWA MINA!

Welcome back to Niwatori-san's corner and today is Z-scores. Feed the need for more MATH!

For Your Information. . . . Red Anecdotes Stuff to know/IMPORTANT STUFF but take it however you will
Blue Anecdotes kinda important so like review it if you like
Green is for formulas/calculations with the calculator and so forth


After the briefing of when a standard deviation is 1,2,3 or even 4 deviations above or below the mean... might as well tell you what "above/below standard deviations from the mean" means.

**In short, you have the mean of a set of data and then a standard deviation from the data the whole basis is when you are "below" the mean you subtract the number of standard deviations from the mean getting a negative number. Being "above the mean" just means to add the number of standard deviations to the mean.
The formula for this is:When you need to get a Z-score to determine which side is greater than the other you get the Z-scores of all the needed info. Then you compare with which number is greater.

Sorry but I couldn't think of an example right at the moment but look over the slides MR.Kuros posted up.

Look look the thing is for now is to use this formula for the distribution tables and follow along with the lessons to come.

Sorry that this post is short but that's all I can think of with my Knowledge for now.
(Plus it's just a review of what we did anyways so meh sue me >-<)


Anywho.. . . . David-san is the next Scriber! (>_<>

"Eagerness brings the most out of Enthusiasm!"

Niwatori's Corner (Statistics - Looking for patterns)

O hayao gozaimasu mina! (forgot what this means it means "Hello everyone in formal context =p)

!!!!For Ya Imfomation. . . . . . !!!!!!
Blue anecdotes sorta inportant
Red anecdotes Stuff to check/IMPORTANT STUFF take it however you will
Green is for formulas/any calculator stuff!



Anywho. . . today I will serve you with both the Looking for patterns in Statistics "Grouped data" as well as how to get these Z-scores but keep yourself tuned for the next posting for the Z-scores part =p


Yes, well just to quote Mr.Kuros and his masterful words of math ( = [ apparently flattery doesn't get free marks) "Math is the Science of Patterns" it really is. You are looking for simliar patterns and then apply them to what we are given to us. Basically saying, having things you know and utilize the ideas/patterns to solve something new unknown to us.

Ok? Ok! >_<

Remember hearing about the difference between "S" and "O" Standard Deviations?
Well here's the thing "S" is for a sample of a large data in which for the learning process of Statistics we're only using "o" in this case the sign "SIGMA" anecdotes for "sum of" to find the neccessary info which is the population

an example is a survey of favorite car around the world where 1000 people out of 40000 people in the country take the survey and then taking that info to predict everyone elses choice of car. This is considered the "S" type being that this is a large piece of data.

"o" type for example is the survey given to Sisler High School and then seeing who likes which car. A small population such as a local school is considered "o" type where data is small.
Ok now?

Now on with Niwatori-san's Diagrams
--follow them and ask for questions like comments/or at school ask for some help you all know who Niwatori-san is =P--

-See now we were taught the day before of something called a "Frequency distribution table"
as well as a "Probability distribution table" actually the probablity one is just the figures/numbers in percentages! So these diagrams will review my knowledge of how to crack at this shell in a nutshell! -





The above diagram shows a Histogram and this bar graph will be your friend.

To build one go to (STAT PLOT) on the (Y=) button but press the (2nd) button 1st from that press the 1st one for now. After this Highlight and press the ON side. The x-value will always be L1, the frequency will be different depending if your working with a probability/frenquency graph. --HINT-- L3 for probability most of the time L2 for the frequency most of the time trust me you`ll understand when you press them!


Well that concludes this portion. Wait till morning for the next page. I needed to rest sorry folks.

Anyways the next scriber is David-san!

*Passes baton skoots away pwoosh pwoosh pwoosh*

"My hope is today is tommorrow`s colorful"
-By Niwatori-san-

Today's Slides: March 25

Here they are ...

Applied 40S March 25, 2009

View more presentations from dkuropatwa.

Today's Slides: March 24

Here they are ...

Applied 40S March 24, 2009

View more presentations from dkuropatwa.

MAN AM I TIRED.

Alright childrens, here's some things you should consider.

IMPORTANT CONCEPTS

1. When grouping, it is important to understand that patterns are more important than details. For example, if you had two tons of fruits, all different kinds, and half of your fruits were rotten... You want to group those and get rid of them. Whether or not it's an apple or an orange can wait, honestly!

2. When measuring how extreme a particular piece of data is, find it's distance from the mean in standard deviations! For example, If there are twenty cars on the road, and the mean speed of the cars is 50 mph with a standard deviation of two mph... Well, let's just say that if one of those cars is flying down the road at 60 mph, that'd be a little extreme! *note: 50-60 with standard deviation of 2 is a 5x(standard deviation) difference! Five standard deviations, in relation to road speed, is too dang fast!

3. REMEMBER! There are two ways to find Standard Deviation! There's a sample version and a population version. The most pronounced difference is that to find the Standard Deviation of a sample, you divide by n-1 instead of n! 

Well. Those are a few concepts we should consider for next class. I'd go into more detail, but I'd rather wait until I have a firmer grasp of this unit, I wouldn't want to sabotage any of you folks.

THE NEXT SCRIBE WILL BE PHONG, BECAUSE HE CAN SAVE US ALL.