Solving the Data Analysis Questions on the GRE Quantitative Reasoning Test is like fixing something at your house. You need a set of tools, and you need to know how to use them.
The tools are the equations needed to solve each particular problem, and Union Test Prep has you covered! In the following chart, you’ll find the essential formulas to solve the Data Analysis Questions on the GRE Quantitative Reasoning Test.
And you can hone your skills for solving the problems by reading our free study guide, and solving our practice questions and free flashcards.
Remember that this chart only shows formulas for one part of the test: Data Analysis. Access our other three formula charts for the GRE Quantitative Reasoning section, here:
Formula  Symbols  Comments 

\(rg = lv  sv\)  rg = range lv = largest value in the data set sv = smallest value in the data set 

\(\overline{x} = \dfrac{\Sigma x_i}{n}\)  \(\overline{x}\) = mean \(x_i\) = value of each measurement n = number of measurements 

\(Md = (\frac{n+1}{2})^{th}\) term  Md = Median n = number of measurements (odd) 

\(Md = \dfrac{(\frac{n}{2})^{th} \text{ term} + (\frac{n}{2} + 1)^{th} \text{ term}}{2}\)  Md = Median n = number of measurements (even) 

\(s = \sqrt{\Sigma (x_i  \overline{x})^2 \div (n1)}\)  s = standard deviation \(\overline{x}\) = mean \(x_i\) = value of each measurement n = number of measurements 

\(V = s^2\)  V = Variance s = standard deviation 

\(CV = RSD = 100 \cdot s \div \overline{x}\)  CV = Coefficient of variation RSD = Relative standard deviation s = standard deviation 

\(Q1 = \frac{1}{4}(n+1)^{th} term\) \(Q2 = \frac{2}{4}(n+1)^{th} term\) \(Q3 = \frac{3}{4}(n+1)^{th} term\) \(IQR = Q3Q2\) 
Q1 = Lower Quartile Q2 = Middle Quartile (Median) Q3 = Upper Quartile n = number of measurements IQR = Interquartile range 

\(P(n,r) = \dfrac{n !}{(nr) !}\)  P = number of permutations n = total number of objects in the set r = number of choosing objects from the set 

\(C(n,r) = \dfrac{n!}{r! \cdot (nr)!}\)  C = number of combinations n = total number of objects in the set r = number of choosing objects from the set 

\(p = \frac{d}{t}\)  p = probability of an event d = desired event t = total number of possible events 

\(P(A \cup B) = P(A) + P(B)  P(A \cap B)\)  \(P(A \cup B)\) = Probability of A or B P(A) = Probability of A P(B) = Probability of B \(P(A \cap B)\) = Probability of A and B 
Rule of Addition 
\(P(A \cap B) = P(A) \cdot P(B)\)  \(P(A \cap B)\) = Probability of A and B P(A) = Probability of A P(B) = Probability of B 
Independent Events 
\(P(A \cap B) = 0\)  \(P(A \cap B)\) = Probability of A and B  Mutually Exclusive Events 
\(P(A \vert B) = \dfrac{P(A \cap B)}{P(B)}\)  \(P(A \vert B)\) = Probability of A given B \(P(A \cap B)\) = Probability of A and B P(B) = Probability of B 
Conditional Probability 
\(P(B \vert A) = \dfrac{P(A \vert B) \cdot P(B)}{P(A)}\)  \(P(B \vert A)\) = Probability of B given A \(P(A \vert B)\) = Probability of A given B P(B) = Probability of B P(A) = Probability of A 
Bayes’ Theorem 
\(P(x) = \dfrac{n!}{x! \cdot (nx)!} \cdot p^x \cdot q^{nx}\)  P(x) = Probability of x successes p = Probability of success in one trial q = 1  p = Probability of failure in one trial n = number of trials x = number of successes 
Binomial Distribution Formula 
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