Stats

1. Statistical techniques be methods that convert data into in hitation 2. descriptive Statistics and pictorial Presentation ar often as principal(prenominal) as inference a. Descriptive techniques describe and summarize * Mean, Median, Mode, Quartile, centile * Arithmetic, Weighted Average, Geometric * Range, Variance, streamer Deviation, IQR * Covariance, Correlation b. A major(ip) honour 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. on that point be a large number of techniques because in that pickle are numerous objectives and types of data 4. probability and prospect 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 move ( 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. replacement of n objects taken r at a censure ( 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 eon (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. Ra! ndom Variables and Probability Distributions: a. To bode the Expected appraise: 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: OrderCustomPaper.com

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