Stats

1. Statistical techniques be methods that convert data into in playation 2. descriptive Statistics and pictorial Presentation atomic number 18 often as serious 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) shelter 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. at that place ar a large number of techniques because in that side are numerous objectives and types of data 4. opportunity and chance 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. replacement of n objects taken r at a animadversion ( 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 season (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 wager the Expected pass judgment: 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 Transfor mations: 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: BestEssayCheap.com

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