The following released test questions are taken from the statistics Standards Test
Statistics Tutorial: Important Statistics Formulas
This web page presents statistics formulas described in the Stat Trek tutorials. Each formula links to a web page that explains how to use the formula.
Parameters
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Population mean = μ = ( Σ Xi ) / N
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Population standard deviation = σ = sqrt [ Σ ( Xi - μ )2 / N ]
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Population variance = σ2 = Σ ( Xi - μ )2 / N
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Variance of population proportion = σP2 = PQ / n
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Standardized score = Z = (X - μ) / σ
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Population correlation coefficient = ρ = [ 1 / N ] * Σ { [ (Xi - μX) / σx ] * [ (Yi - μY) / σy ] }
Statistics
Unless otherwise noted, these formulas assume simple random sampling.
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Sample mean = x = ( Σ xi ) / n
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Sample standard deviation = s = sqrt [ Σ ( xi - x )2 / ( n - 1 ) ]
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Sample variance = s2 = Σ ( xi - x )2 / ( n - 1 )
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Variance of sample proportion = sp2 = pq / (n - 1)
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Pooled sample proportion = p = (p1 * n1 + p2 * n2) / (n1 + n2)
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Pooled sample standard deviation = sp = sqrt [ (n1 - 1) * s12 + (n2 - 1) * s22 ] / (n1 + n2 - 2) ]
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Sample correlation coefficient = r = [ 1 / (n - 1) ] * Σ { [ (xi - x) / sx ] * [ (yi - y) / sy ] }
Correlation
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Pearson product-moment correlation = r = Σ (xy) / sqrt [ ( Σ x2 ) * ( Σ y2 ) ]
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Linear correlation (sample data) = r = [ 1 / (n - 1) ] * Σ { [ (xi - x) / sx ] * [ (yi - y) / sy ] }
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Linear correlation (population data) = ρ = [ 1 / N ] * Σ { [ (Xi - μX) / σx ] * [ (Yi - μY) / σy ] }
Simple Linear Regression
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Simple linear regression line: y = b0 + b1x
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Regression coefficient = b1 = Σ [ (xi - x) (yi - y) ] / Σ [ (xi - x)2]
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Regression slope intercept = b0 = y - b1 * x
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Regression coefficient = b1 = r * (sy / sx)
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Standard error of regression slope = sb1 = sqrt [ Σ(yi - yi)2 / (n - 2) ] / sqrt [ Σ(xi - x)2 ]
Counting
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n factorial: n! = n * (n-1) * (n - 2) * . . . * 3 * 2 * 1. By convention, 0! = 1.
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Permutations of n things, taken r at a time: nCr = n! / (n - r)!
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Combinations of n things, taken r at a time: nCr = n! / r!(n - r)! = nPr / r!
Probability
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Rule of addition: P(A ∪ B) = P(A) + P(B) - P(A ∩ B)
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Rule of multiplication: P(A ∩ B) = P(A) P(B|A)
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Rule of subtraction: P(A') = 1 - P(A)
Random Variables
In the following formulas, X and Y are random variables, and a and b are constants.
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Expected value of X = E(X) = μx = Σ [ xi * P(xi) ]
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Variance of X = Var(X) = σ2 = Σ [ xi - E(x) ]2 * P(xi) = Σ [ xi - μx ]2 * P(xi)
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Normal random variable = z-score = z = (X - μ)/σ
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Chi-square statistic = Χ2 = [ ( n - 1 ) * s2 ] / σ2
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f statistic = f = [ s12/σ12 ] / [ s22/σ22 ]
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Expected value of sum of random variables = E(X + Y) = E(X) + E(Y)
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Expected value of difference between random variables = E(X - Y) = E(X) - E(Y)
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Variance of the sum of independent random variables = Var(X + Y) = Var(X) + Var(Y)
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Variance of the difference between independent random variables = Var(X - Y) = E(X) + E(Y)
Sampling Distributions
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Mean of sampling distribution of the mean = μx = μ
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Mean of sampling distribution of the proportion = μp = P
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Standard deviation of proportion = σp = sqrt[ P * (1 - P)/n ] = sqrt( PQ / n )
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Standard deviation of the mean = σx = σ/sqrt(n)
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Standard deviation of difference of sample means = σd = sqrt[ (σ12 / n1) + (σ22 / n2) ]
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Standard deviation of difference of sample proportions = σd = sqrt{ [P1(1 - P1) / n1] + [P2(1 - P2) / n2] }
Standard Error
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Standard error of proportion = SEp = sp = sqrt[ p * (1 - p)/n ] = sqrt( pq / n )
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Standard error of difference for proportions = SEp = sp = sqrt{ p * ( 1 - p ) * [ (1/n1) + (1/n2) ] }
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Standard error of the mean = SEx = sx = s/sqrt(n)
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Standard error of difference of sample means = SEd = sd = sqrt[ (s12 / n1) + (s22 / n2) ]
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Standard error of difference of paired sample means = SEd = sd = { sqrt [ (Σ(di - d)2 / (n - 1) ] } / sqrt(n)
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Pooled sample standard error = spooled = sqrt [ (n1 - 1) * s12 + (n2 - 1) * s22 ] / (n1 + n2 - 2) ]
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Standard error of difference of sample proportions = sd = sqrt{ [p1(1 - p1) / n1] + [p2(1 - p2) / n2] }
Discrete Probability Distributions
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Binomial formula: P(X = x) = b(x; n, P) = nCx * Px * (1 - P)n - x = nCx * Px * Qn - x
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Mean of binomial distribution = μx = n * P
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Variance of binomial distribution = σx2 = n * P * ( 1 - P )
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Negative Binomial formula: P(X = x) = b*(x; r, P) = x-1Cr-1 * Pr * (1 - P)x - r
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Mean of negative binomial distribution = μx = rQ / P
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Variance of negative binomial distribution = σx2 = r * Q / P2
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Geometric formula: P(X = x) = g(x; P) = P * Qx - 1
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Mean of geometric distribution = μx = Q / P
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Variance of geometric distribution = σx2 = Q / P2
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Hypergeometric formula: P(X = x) = h(x; N, n, k) = [ kCx ] [ N-kCn-x ] / [ NCn ]
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Mean of hypergeometric distribution = μx = n * k / N
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Variance of hypergeometric distribution = σx2 = n * k * ( N - k ) * ( N - n ) / [ N2 * ( N - 1 ) ]
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Poisson formula: P(x; μ) = (e-μ) (μx) / x!
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Mean of Poisson distribution = μx = μ
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Variance of Poisson distribution = σx2 = μ
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Multinomial formula: P = [ n! / ( n1! * n2! * ... nk! ) ] * ( p1n1 * p2n2 * . . . * pknk )
Linear Transformations
For the following formulas, assume that Y is a linear transformation of the random variable X, defined by the equation: Y = aX + b.
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Mean of a linear transformation = E(Y) = Y = aX + b.
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Variance of a linear transformation = Var(Y) = a2 * Var(X).
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Standardized score = z = (x - μx) / σx.
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t-score = t = (x - μx) / [ s/sqrt(n) ].
Estimation
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Confidence interval: Sample statistic + Critical value * Standard error of statistic
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Margin of error = (Critical value) * (Standard deviation of statistic)
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Margin of error = (Critical value) * (Standard error of statistic)
Hypothesis Testing
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Standardized test statistic = (Statistic - Parameter) / (Standard deviation of statistic)
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One-sample z-test for proportions: z-score = z = (p - P0) / sqrt( p * q / n )
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Two-sample z-test for proportions: z-score = z = z = [ (p1 - p2) - d ] / SE
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One-sample t-test for means: t-score = t = (x - μ) / SE
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Two-sample t-test for means: t-score = t = [ (x1 - x2) - d ] / SE
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Matched-sample t-test for means: t-score = t = [ (x1 - x2) - D ] / SE = (d - D) / SE
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Chi-square test statistic = Χ2 = Σ[ (Observed - Expected)2 / Expected ]
Degrees of Freedom
The correct formula for degrees of freedom (DF) depends on the situation (the nature of the test statistic, the number of samples, underlying assumptions, etc.).
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One-sample t-test: DF = n - 1
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Two-sample t-test: DF = (s12/n1 + s22/n2)2 / { [ (s12 / n1)2 / (n1 - 1) ] + [ (s22 / n2)2 / (n2 - 1) ] }
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Two-sample t-test, pooled standard error: DF = n1 + n2 - 2
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Simple linear regression, test slope: DF = n - 2
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Chi-square goodness of fit test: DF = k - 1
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Chi-square test for homogeneity: DF = (r - 1) * (c - 1)
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Chi-square test for independence: DF = (r - 1) * (c - 1)
Sample Size
Below, the first two formulas find the smallest sample sizes required to achieve a fixed margin of error, using simple random sampling. The third formula assigns sample to strata, based on a proportionate design. The fourth formula, Neyman allocation, uses stratified sampling to minimize variance, given a fixed sample size. And the last formula, optimum allocation, uses stratified sampling to minimize variance, given a fixed budget.
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Mean (simple random sampling): n = { z2 * σ2 * [ N / (N - 1) ] } / { ME2 + [ z2 * σ2 / (N - 1) ] }
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Proportion (simple random sampling): n = [ ( z2 * p * q ) + ME2 ] / [ ME2 + z2 * p * q / N ]
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Proportionate stratified sampling: nh = ( Nh / N ) * n
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Neyman allocation (stratified sampling): nh = n * ( Nh * σh ) / [ Σ ( Ni * σi ) ]
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Optimum allocation (stratified sampling):
nh = n * [ ( Nh * σh ) / sqrt( ch ) ] / [ Σ ( Ni * σi ) / sqrt( ci ) ]
AP Statistics Test
Sample Questions & Scoring Guidelines
The AP Statistics Exam covers the following major topics: exploring data; planning a study (deciding what and how to measure); anticipating patterns (using probability and simulation); and statistical inference (confirming models).
Ideally, students should have access to a computer for work both in and out of the classroom. While it is not yet possible to have access to a computer during the AP Statistics Exam, the exam may include standard computer output, and students will be expected to interpret it.
You can find additional free-response questions and scoring guidelines on AP Central, along with grade distributions and examples of actual students' responses and commentary that explains why the responses received the scores they did.
Multiple-Choice Questions
For sample multiple-choice questions, refer to the Course Description.
AP Statistics Course Description (.pdf/2.52M)
Requires Adobe Reader (latest version recommended).
Free-Response Questions
2011 Free-Response Questions (.pdf/417K)
2011 Form B Free-Response Questions (.pdf/331K)
2010 Free-Response Questions (.pdf/170K)
2010 Form B Free-Response Questions (.pdf/231K)
2009 Free-Response Questions (.pdf/671K)
2009 Form B Free-Response Questions (.pdf/582K)
2008 Free-Response Questions (.pdf/267K)
2008 Form B Free-Response Questions (.pdf/272K)
2007 Free-Response Questions (.pdf/462K)
2007 Form B Free-Response Questions (.pdf/349K)
2006 Free-Response Questions (.pdf/224K)
2006 Form B Free-Response Questions (.pdf/165K)
2005 Free-Response Questions (.pdf/322K)
2005 Form B Free-Response Questions (.pdf/304K)
2004 Free-Response Questions (.pdf/289K)
2004 Form B Free-Response Questions (.pdf/287K)
2003 Free-Response Questions (.pdf/225K)
2003 Form B Free-Response Questions (.pdf/275K)
2002 Free-Response Questions (.pdf/268K)
2002 Form B Free-Response Questions (.pdf/215K)
2001 Free-Response Questions (.pdf/275K)
Scoring Guidelines
2010 Scoring Guidelines (.pdf/123K)
2010 Form B Scoring Guidelines (.pdf/104K)
2009 Scoring Guidelines (.pdf/105K)
2009 Form B Scoring Guidelines (.pdf/101K)
2008 Scoring Guidelines (.pdf/175K)
2008 Form B Scoring Guidelines (.pdf/274K)
2007 Scoring Guidelines (.pdf/277K)
2007 Form B Scoring Guidelines (.pdf/260K)
2006 Scoring Guidelines (.pdf/139K)
2006 Form B Scoring Guidelines (.pdf/282K)
2005 Scoring Guidelines (.pdf/212K)
2005 Form B Scoring Guidelines (.pdf/204K)
2004 Scoring Guidelines (.pdf/653K)
2004 Form B Scoring Guidelines (.pdf/607K)
Hunter College
Statistics Course Rotation Schedule
Course Number |
Course Title |
Semester Offered |
|
|
|
STAT 110 |
Selected Topics in Elementary Probability and Statistics as Applied to Popular Science and Current Events |
TBA |
STAT 113 |
Elementary Probability and Statistics -syllabus |
fall, spring, summer |
STAT 212 |
Discrete Probability - syllabus |
fall. spring |
STAT 213 |
Introduction to Applied Statistics - syllabus |
fall, spring, summer |
STAT 214 |
Data Analysis Using Statistical Software - syllabus |
fall |
STAT 220 |
Statistical Analyses in Forensics - syllabus |
spring |
STAT 295 |
Intermediate Topics in Statistics |
TBA |
STAT 311 |
Probability Theory -syllabus |
fall, spring |
STAT 312 |
Stochastic Processes - syllabus |
spring |
STAT 313 |
Introduction to Mathematical Statistics - syllabus |
spring |
STAT 319 |
Bayesian Statistical Inference in the Sciences - syllabus |
fall |
STAT 391-2-3 |
(Undergraduate) Independent Study in Statistics |
fall, spring, summer |
STAT 395 |
Advanced Topics in Statistics |
TBA |
STAT 486 |
Elements of Visualization - syllabus |
even spring semesters |
STAT 612 |
Discrete Probability |
spring |
STAT 614 |
Data Analysis Using Statistical Software - syllabus |
fall, summer |
STAT 701 |
Advanced Probability Theory I |
fall, spring |
STAT 702 |
Advanced Probability Theory II |
spring |
STAT 703 |
Mathematical Statistics |
spring |
STAT 706 |
General Linear Models I |
fall |
STAT 707 |
General Linear Models II |
spring |
STAT 715 |
Time Series Analysis |
spring |
STAT 716 |
Data Analysis |
TBA |
STAT 717 |
Multivariate Analysis |
fall |
STAT 718 |
Analysis of Variance - syllabus |
odd spring semesters |
STAT 722 |
Theory of Games |
even fall semesters |
STAT 724 |
Topics in Applied Mathematics I |
TBA |
STAT 725 |
Topics in Applied Mathematics II |
TBA |
STAT 726 |
Theory and Methods of Sampling |
odd fall semesters |
STAT 739 |
Bayesian Statistics |
fall |
STAT 750 |
Applied Biostatistics I |
fall, spring |
STAT 751 |
Applied Biostatistics II |
fall |
STAT 752 |
Categorical Data Analysis -syllabus |
even spring semesters |
STAT 753 |
Longitudinal Data Analysis |
even spring semesters |
STAT 754 |
Analysis and Design of Complex Surveys |
even fall semesters |
STAT 755 |
Survival Analysis |
odd spring semesters |
STAT 761 |
Advanced Concepts in Financial Markets |
spring |
STAT 786 |
Visualization for Stat. and Applied Math. |
even spring semesters |
STAT 787 |
Statistical Models for Spatial Data |
odd fall semesters |
STAT 790 |
Case Seminar |
fall, spring |
STAT 791-2-3 |
(Graduate) Independent Study in Statistics |
fall, spring, summer |
|