Statistics 104: Quantitative Methods for Economics Formula and Theorem Review

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1 Harvard College Statistics 104: Quantitative Methods for Economics Formula and Theorem Review Tommy MacWilliam, 13 March 10, 2011

2 Contents 1 Introduction to Data Sample Mean Interquartile Range Outlier Detection Mean Absolute Deviation Variance Standard Deviation Coefficient of Variation Empirical Rule for Standard Deviation Chebyshev s Rule Linear Transformations Z-Score Covariance Correlation Combining Data Sets Probability and Random Variables Definition of Probability Addition Rule Complement Rule Conditional Probability Independence Joint Probability x2 Matrix Bayes Theorem Probability Function Cumulative Distribution Function Expected Value Variance of a Random Variable Linear Transformations of Random Variables Joint Distribution Function

3 2.15 Marginal Distributions Independence of Random Variables Conditional Distribution of Random Variables Conditional Expectation of Random Variables Covariance of Random Variables Correlation of Random Variables Combinations of Random Variables Probability Distributions Combinations Binomial Distribution Formula Characteristics of Binomial Distributions Probability of an Interval Z-Score for Normal Distribution Central Limit Theorem Confidence Intervals Confidence Interval Sample Proportion Central Limit Theorem for Proportions Confidence Interval for Proportions Confidence Interval for Correlation Confidence Interval for Difference in Proportions Confidence Interval for Difference in Means Hypothesis Testing Test Statistic for Population Mean Test Statistic for Proportion Test Statistic for Two Samples Test Statistic for Two Proportions Test Statistic for Chi-Square Test

4 6 Regression Residual Least Squares Method Coefficient of Determination Standard Error Regression Test Statistic Confidence Interval for Predicting an Average Confidence Interval for Predicting a Value Adjusted Coefficient of Determination Overall F-Test Standardized Residuals Logistic Function

5 1 Introduction to Data 1.1 Sample Mean 1.2 Interquartile Range x = 1 n x i n i=1 IQR = Q3 Q1 1.3 Outlier Detection An observation x i in a set of data is considered an outlier if x i > Q IQR x i < Q1 1.5 IQR 1.4 Mean Absolute Deviation 1.5 Variance MAD = 1 n x i x n i=1 s 2 x = nx=1 (x i x) 2 n Standard Deviation 1.7 Coefficient of Variation nx=1 (x i x) s x = 2 n 1 ( s x) CV = 100% 5

6 1.8 Empirical Rule for Standard Deviation For mound-shaped, symmetric data: 68% of the data is in the interval ( x s x, x + s x ) 95% of the data is in the interval ( x 2s x, x + 2s x ) 1.9 Chebyshev s Rule For any set of data, the proportion of data that lines within k standard deviations of the mean is at least 1 1 k Linear Transformations V ar(a + bx) = b 2 V ar(x) Average(a + bx) = a + b(average(x)) StdDev(a + bx) = b StdDev(X) Q i (a + bx) = a + bq i IQR(a + bx) = b IQR(X) 1.11 Z-Score To obtain a set of data with mean 0 and variance 1: 1.12 Covariance z = X X s x s xy = 1 n 1 Covariance > 0: Larger X Larger Y Covariance < 0 : Larger X Smaller Y Cov(X, X) = V ar(x) n (x i x)(y i ȳ) i=1 6

7 1.13 Correlation r xy = s xy s x s y 1.14 Combining Data Sets Z = a X + bȳ V ar(z) = a 2 s 2 X + b 2 s 2 Y + 2(ab)s XY 2 Probability and Random Variables 2.1 Definition of Probability P (event) = outcomes where the event occurs total outcomes 2.2 Addition Rule P (A B) = P (A) + P (B) P (A B) 2.3 Complement Rule P (Ā) = 1 P (A) 2.4 Conditional Probability P (A B) = P (A B) P (B) 2.5 Independence Two events A and B are said to be independent if: P (A B) = P (A) P (B A) = P (B) 7

8 2.6 Joint Probability If two events A and B are independent: P (A B) = P (A) P (B) 2.7 2x2 Matrix B B A P (A B) = P (B) P (A B) P (A B) = P ( B) P (A B) Ā P (Ā B) = P (Ā) P (B Ā) P (Ā B) = P (Ā) P ( B Ā) 2.8 Bayes Theorem P (B A) = 2.9 Probability Function P (A B) P (B) P (A B) P (B) + P (A B) P ( B) P X (x) = P (X = x) 2.10 Cumulative Distribution Function 2.11 Expected Value F X (x 0 ) = P (X x 0 ) = P X (x) x x 0 µ X = E(X) = 2.12 Variance of a Random Variable all x i x i P (x i ) σx 2 = V ar(x) = E((X µ X ) 2 ) = (x i µ) 2 P (x i ) all x i σx 2 = E(X 2 ) µ 2 X = E(X 2 ) E(X) 2 8

9 2.13 Linear Transformations of Random Variables E(a + bx) = a + be(x) = a + bµ X V ar(a + bx) = b 2 σx 2 E(a) = a V ar(a) = Joint Distribution Function P X,Y (x, y) = P (X = x Y = y) 2.15 Marginal Distributions P X (x) = y P Y (y) = x P X,Y (x, y) P X,Y (x, y) 2.16 Independence of Random Variables Two random variables X and Y are independent if x, y: P X,Y (x, y) = P X (x) P Y (y) P X Y (X = x Y = y) = P (X = x) 2.17 Conditional Distribution of Random Variables P X Y (X = x Y = y) = P X,Y (x, y) P Y (y) 2.18 Conditional Expectation of Random Variables E(X Y = y) = all x xp (X = x Y = y) 2.19 Covariance of Random Variables σ X,Y = Cov(X, Y ) = E((X µ X )(Y µ Y )) = E(XY ) E(X) E(Y ) where E(XY ) = xyp (X = x, Y = y) 9

10 2.20 Correlation of Random Variables ρ = σ X,Y σ X σ Y 2.21 Combinations of Random Variables If X and Y are independent: E(X + Y ) = E(X) + E(Y ) V ar(x + Y ) = V ar(x) + V ar(y ) If X and Y are not independent: E(X + Y ) = E(X) + E(Y ) V ar(x + Y ) = V ar(x) + V ar(y ) + 2Cov(X, Y ) General case: E((a + bx) + (c + dy )) = a + be(x) + c + de(y ) V ar((a + bx) + (c + dy )) = b 2 V ar(x) + d 2 V ar(y ) + 2(bd)Cov(X, Y ) 3 Probability Distributions 3.1 Combinations ( ) n = x n! x!(n x)! 3.2 Binomial Distribution Formula P (x) = n! x!(n x)! px q n x 10

11 3.3 Characteristics of Binomial Distributions µ = E(X) = np σ 2 = npq σ = npq 3.4 Probability of an Interval P (a X b) = F X (b) F X (a) where F X is the CDF, such that F X (X x) = x f(x) dx 3.5 Z-Score for Normal Distribution If X N(µ, σ 2 ), then Z = X µ σ N(0, 1). Therefore: P (a X b) = P ( a µ σ Z b µ σ ) 3.6 Central Limit Theorem If random samples are taken from any population with mean µ and variance σ 2, as the sample size n increases, the distribution approaches a normal distribution with µ X = µ and σ 2 X = σ2 n. 4 Confidence Intervals 4.1 Confidence Interval x ± z α/2 σ n 4.2 Sample Proportion ˆp = X = 1 n X i n i=1 11

12 4.3 Central Limit Theorem for Proportions ˆp N ( p, ) p(1 p) n 4.4 Confidence Interval for Proportions ˆp(1 ˆp) ˆp ± z α/2 n 4.5 Confidence Interval for Correlation r ± r 2 n Confidence Interval for Difference in Proportions (ˆp 1 ˆp 2 ) ± z α/2 ˆp1 (1 ˆp 1 ) n 1 + ˆp 2(1 ˆp 2 ) n Confidence Interval for Difference in Means 5 Hypothesis Testing (µ 1 µ 2 ) ± z α/2 s 2 X n 1 + s2 Y n Test Statistic for Population Mean z stat = x µ 0 σ/ n 5.2 Test Statistic for Proportion T = ˆp p 0 p 0 (1 p 0 )/n 12

13 5.3 Test Statistic for Two Samples T = X 1 X 2 s 2 1 n 1 + s2 2 n Test Statistic for Two Proportions T = ˆp 1 ˆp 2 ˆp(1 ˆp)( 1 n n 2 ) 5.5 Test Statistic for Chi-Square Test where e i = np i χ 2 = (o i e i ) 2 e i 6 Regression 6.1 Residual e i = Y i Ŷi 6.2 Least Squares Method We can minimize (Y i b 0 b 1 X i ) 2 = (Y i Ŷi) 2 = e 2 i by using the coefficients: b 1 = ni=1 (X i X)(Y i Ȳ ) ni=1 (X i X) = r XY 2 b 0 = Ȳ b 1 X ( ) sy s X 6.3 Coefficient of Determination R 2 = SSR SST = 1 SSE SST 13

14 6.4 Standard Error s e = SSE n k 1 = 6.5 Regression Test Statistic SSE SSE n 2 = df error T = b 1 β 1 s b Confidence Interval for Predicting an Average ( 1 b 0 + b 1 X new ± 1.96 s e n + (X new X) 2 ) 1/2 (n 1)s 2 X 6.6 Confidence Interval for Predicting a Value ( b 0 + b 1 X ± 1.96 s e n + (X new X) 2 ) 1/2 (n 1)s 2 X 6.7 Adjusted Coefficient of Determination 6.8 Overall F-Test adjusted R 2 = 1 f = 6.9 Standardized Residuals 6.10 Logistic Function SSE/(n k 1) SST/(n 1) SSR/k SSE/(n k 1) r i = e i ɛ i s e σ f(x) = N(0, 1) ex 1 + e = exp(x) x 1 + exp(x) 14