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1 ••• Master List of Formulas Chapter 1 IntroduCtIon and desCrIptIve statIstICs NONE. Chapter 2 FrequenCy dIstrIbutIons In tables and Graphs Σx (Frequency) Σx n (Relative frequency) Σx n × 100 (Relative percent) Chapter 3 summarIzInG data: Center tendenCy µ= Σx N (Population mean) M = Σx n (Sample mean) M Mn w n = Σ × Σ (Weighted sample mean) Chapter 4 summarIzInG data: varIabIlIty
BEDM(S)AS – Brackets, Exponents, Division, Multiplication, Sum ( ∑ ), Addition, Subtraction. Central Tendency (Ch. 3) ∑X Population mean: μ = Sample mean: N. Sum of Squares (Ch. 4, p. 70-71) =. Definitional Formula: SS = ∑(X − μ)2. Computational Formula:
Linear Regression Model. Population Version: Mean: PY x E ( Y ) E 0 E 1 x Individual: y E E x H. 0 1 i. where H is N ( 0 , V ) Sample Version: Mean: y ˆ b b x.
List of Formulae and Statistical Tables. Cambridge Pre-U Mathematics (9794) and Further Mathematics (9795) For use from 2017 in all papers for the above syllabuses. CST317. . PURE MATHEMATICS. Mensuration. Surface area of sphere = 4πr2 Area of curved surface of cone = πr × slant height. Trigonometry. a2 = b2 + c2 − 2bc cos A.
Σ = sum. w = weight. ∑ x Sample mean: x = n. ∑. Population mean: μ = x. N. Weighted mean: x = ∑ ( w • x ) ∑ w. x. ∑. Mean for frequency table: = ( f • x ) ∑ f. Midrange = highest value + lowest value. 2. Range = Highest value - Lowest value. Sample standard deviation: s = 2 ∑ ( x − x ) n − 1. Population standard deviation: σ =
100+ online courses in statistics Symbol Text Equivalent Meaning Formula Link to Glossary (if appropriate) MS M-S Mean square MS= df SS Analysis of variance (ANOVA) n Sample size. n = number of units in a sample. N Population size N = Number of units in the population. P n,r n-p-r Permutation (number of ways to arrange in order n distinct
Mathematical models allow us to easily calculate the probability of occurrence of any specific value of the variable of interest. The binomial distribution is used in situations where the discrete variable is the number of occurrences in a sample of n observations. P (X = x | n, p) = (1 − ) − !( − )!
