Cumulative normal distribution function.The probability of an event given that the random experiment produces an outcome in another event.Ī dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables). The arithmetic mean is usually denoted by x, and is often called the averageĪn effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest. The arithmetic mean of a set of numbers x1, x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? =. Eficient computer algorithms have been developed for implementing all possible regressions In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.Ī method of variable selection in regression that examines all possible subsets of the candidate regressor variables. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).Ī variation of the R 2 statistic that compensates for the number of parameters in a regression model.
Key Statistics Terms and definitions covered in this textbookĪ full factorial experiment with k factors and all factors tested at only two levels (settings) each. Chapter 7: Scatterplots, Association, and Correlation.Chapter 6: The Standard Deviation as a Ruler and the Normal Model.Chapter 5: Understanding and Comparing Distributions.Chapter 4: Displaying and Summarizing Quantitative Data.Chapter 3: Displaying and Describing Categorical Data.Chapter 21: More About Tests and Intervals.Chapter 20: Testing Hypotheses About Proportions.Chapter 19: Confidence Intervals for Proportions.Chapter 18: Sampling Distribution Models.Chapter 14: From Randomness to Probability.Chapter 13: Experiments and Observational Studies.Chapter 10: Re-expressing Data: Get It Straight!.