Statistics example
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May 28, 2007 at 2:26 pm #47099
KatrinaParticipant@Katrina Include @Katrina in your post and this person will
be notified via email.Can someone give me a pratcical application or examples for the below statistics, “1.Mode, 2. Standard deviation, 3.Variance and 4.Kurtosis” pls?. I did lots of googling nothing great. It would be great if someone could cite me some pratical application of these statistics. Thanks
0May 28, 2007 at 3:01 pm #156666K,
Use the dictionary function here or the help sections in minitab…between the two you should gain an adequate undertanding. As for my two cents:Mode – the value that occurs most often in your data set. Along with mean (ie the average) and median (the midpoint of the data set once set in order)
For example:0May 28, 2007 at 3:18 pm #156667Katrina:Go to a real estate website and get the prices for all listings. Graph the data, calculate the statistics and interpret them in the context of, “the price for an average home,” and, “the average price for a home.”Why are these different? If the average price for a home is X dollars, why don’t I see half the houses below average?This makes a good classroom example too.Cheers, BTDT
0May 28, 2007 at 3:21 pm #156668Sorry—
You will usually see the Central Tendency presented in three forms: Mean (the average), Median (the center of your data once it has been rank ordered), and Mode (the value in your data set that occurs the most frequently).
For example: (1, 6, 7, 7, 7, 9, 2 , 0, 4, 3, 4, 5) Here 7 is your mode.Std Dev is a measure of the differences between your data …often referred to as the spread…it can be thought of as the average distance any data point is away from your mean….(we use mean and std dev together to help describe a data set that follows an approximately normal distribution)
Variance is std dev before the mathmatical conversion…see the formula for insight….but it is simply another measure of spread
Kurtosis is a relative measure….it indicates how closely the peak of your distribution follows the predicted peak of the bell curve associated with normality….if it is positive, your distribution peak is higher than the normal dist. peak, and if neg, then the peak of your data set is below that of the normal distribution…..I believe it is another measure which helps you determine how close to normal your data set is….but i have never had anyone press me on it or ask for it during a report out…MTB can provide more…..hope it helps…..good luck.0May 28, 2007 at 4:01 pm #156671
Fake Gary AlertParticipant@FakeGaryAlert Include @FakeGaryAlert in your post and this person will
be notified via email.Variance is equal to (sd)2
0May 29, 2007 at 7:45 am #156683
KatrinaParticipant@Katrina Include @Katrina in your post and this person will
be notified via email.Hi Annon
Thanks for your help. I was curious to know when and where do they apply these statistics. I will check out with Minitab.0May 29, 2007 at 8:53 am #156685Hi BTDT,
If the average price for a home is X dollars, half the houses will always fall below average and the rest above.
Cheers DD0May 29, 2007 at 1:54 pm #156699Sorry DD, they might but there is no guarantee that they will – that’s why you want to know the median and not the mean.
Example – the average salary at my company is $100,000.
By your reasoning half would be below $100,000 and half would be above.
The reality is that we have 10 people in the company. The boss makes $910,000/year and the other 9 make $10,000/year. Total salary – $1,000,000. Divide that by 10 and you get an average of $100,000…only we have 9 people below the average and 1 person above.0May 29, 2007 at 6:37 pm #156709hi katrina,
try this link, i think it will help….
http://www.statsoft.com/textbook/stathome.html
Helper…0 
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