Stats

1. Statistical techniques be methods that convert data into information

2. Descriptive Statistics and pictorial Presentation are often as important as inference
a. Descriptive techniques describe and summarize
* Mean, Median, Mode, Quartile, Percentile
* Arithmetic, Weighted Average, Geometric
* Range, Variance, streamer Deviation, IQR
* Covariance, Correlation
b. A major(ip) value of regression is the production of a model that describes the relationships among variables
* Intercept, Slope, SS, MS, t, F, Rsq, Adj Rsq, stock(a) Error

3. There are a large number of techniques because in that location are numerous objectives and types of data

4. Probability and Probability Distributions form a key foundation of statistical inference

5. Probability Definitional Rules:
a. 0 ? P (A) ? 1
b. P (A) + P (not-A) = 1
c. P (A or B) = P (A) + P (B) P (A and B)
d. P (A and B) = P (A) * P (B | A)
P (A and B) = P (B) * P (A | B)

6. Probability Counting Rules
a. Experiment = a period of k steps ( flavour 1: n1 outcomes, Step 2: n2 outcomes… Step k: nk outcomes) The total number of experimental outcomes is given by (n1)*(n2)*…*(nk)
b. Permutation of n objects taken r at a condemnation ( dedicate counts): Count the number of experimental outcomes when r objects are to be selected from a set of n objects P= n!



n-r!
c. Combinations of n objects taken r at a time (Order doesnt count): Count the number of experimental outcomes when r objects are to be selected from a set of n objects C= n!r!n-r!

7. Random Variables and Probability Distributions:
a. To calculate the Expected assess: EX= x*Px
b. To calculate the Variance:
VarX= (x-EX)2*P(x)
c. To calculate the Standard deviation: ?X= (x-EX)2*P(x)
d. Linear Transformations:
If Y = a*X + b, Then EY=a*EX+b, VarY=a2*VarX, ?Y=|a|*?X
e. Linear Combinations:...If you want to get a full essay, order it on our website: Orderessay



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