Contents
- Preface
1Sampling and Data
- Introduction
- 1.1Definitions of Statistics, Probability, and Key Terms
- 1.2Data, Sampling, and Variation in Data and Sampling
- 1.3Frequency, Frequency Tables, and Levels of Measurement
- 1.4Experimental Design and Ethics
- 1.5Data Collection Experiment
- 1.6Sampling Experiment
- Key Terms
- Chapter Review
- Practice
- Homework
- Bringing It Together: Homework
- References
- Solutions
10Hypothesis Testing with Two Samples
- AAppendix A Review Exercises (Ch 3–13)
- BAppendix B Practice Tests (1–4) and Final Exams
- CData Sets
- DGroup and Partner Projects
- ESolution Sheets
- FMathematical Phrases, Symbols, and Formulas
- GNotes for the TI-83, 83+, 84, 84+ Calculators
- HTables
- Index
Formula Review
Probability density function (pdf) f(x):
- f(x) ≥ 0
- The total area under the curve f(x) is one.
Cumulative distribution function (cdf): P(X ≤ x)
X = a real number between a and b (in some instances, X can take on the values a and b). a = smallest X, b = largest X
X ~ U(a, b)
The mean is
The standard deviation is
Probability density function: for
Area to the left of x: P(X < x) = (x – a)
Area to the right of x: P(X > x) = (b – x)
Area between c and d: P(c < x < d) = (base)(height) = (d – c)
Uniform: X ~ U(a, b) where a < x < b
- pdf: for a ≤ x ≤ b
- cdf: P(X ≤ x) =
- mean µ =
- standard deviation σ
- P(c < X < d) = (d – c)
Exponential: X ~ Exp(m) where m = the decay parameter
- pdf: f(x) = me(–mx) where x ≥ 0 and m > 0
- cdf: P(X ≤ x) = 1 – e(–mx)
- mean µ =
- standard deviation σ = µ
- percentile k: k =
- Additionally
- P(X > x) = e(–mx)
- P(a < X < b) = e(–ma) – e(–mb)
- Memoryless property: P(X > x + k|X > x) = P (X > k)
- Poisson probability: with mean λ
- k! = k*(k−1)*(k−2)*(k−3)*…3*2*1