Download The Proportion of data within k standard deviations of the mean is at

UDJ CHECKLIST
SUMMARIZING DATA (Populations)
Histograms & frequency distributions
measures of location
mean
median
mode
measures of dispersion
variance
standard deviation
coefficient of variation
Chebyshev’s Theorem
The Proportion of data within k standard deviations of the mean is at
least 1 - 1/k 2
Empirical Rule
PROBABILITY DISTRIBUTIONS
Binomial
Poisson
Normal
Normal approximation to binomial
if np ≥ 5 and n(1 -p) ≥ 5
don’t forget continuity correction
Convert from X to Z and back again
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FUNCTIONS OF RANDOM VARIABLES
Suppose that W = a + bX + cY
Mean and variance of W.
Covariance and Correlation
If we know the distributions of X and Y, what can we say about the distribution of W?
Rule of thumb : if X and Y are normal, you may assume W is also normal.
See 1-page handout for details
USING SAMPLES
X and s as estimates for µ and σ
Central Limit Theorem
Distribution of the Sample Mean
X is normal for large samples (n ≥ 30) , or if X is normal
Confidence Intervals
for means
large samples vs. small samples
for proportions
E (maximum bound on errors)
solving for sample size
Hypothesis Testing
Type I and Type II errors: α and β
One sided vs. two sided tests: decision rules
Critical Values (for Z or X)
P-values
Small vs. Large samples: t test vs. Z test
Means vs. proportions
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REGRESSION
Simple Regression (one independent variable)
Multiple Regression (more than one independent variable)
Dummy Variables (X = 0 or 1, e.g., sex), Lagged variables
Correlation Matrix
t-tests
F-test
R2 and adjusted R2
Standard deviation of regression
Confidence Intervals
For forecasts
For model parameters (coefficients)
Dealing with multicolinearity
Dealing with outliers
Residual Analysis (testing model assumptions)
autocorrelation & Durbin-Watson test
heteroscedasticity
normality of errors
Transformations
Spurious relationships and causality
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