Chapter 6 Flashcards
Standardizing
We standardize to eliminate units. Standardizing values can be compared and combined even if the original variables had different units and magnitudes. ;)
Standardized Value
A value found by subtracting the mean and dividing by the standard deviation. ;)
Shifting
Adding a constant to each data value the same constant to the mean, the median, and the quartiles, but does not change the standard deviation or IQR. ;)
Rescaling
Multiplying each data value by a constant multiplies both the measures of position ad the measures of spread by that constant. ;)
Normal Model
A useful family of models for unimodel, symmetric distributions. ;)
Parameter
A numerically valued attribute of a model. For example, the values of u and o in a N(u-o) model are parameters. ;)
Statistic
A value calculated from data to summarize aspects of the data. For example, the mean, y and standard deviation, s, are statistics. ;)
z-score
A z-score tells how many standard deviations a value is from the mean; z-scores have a mean of 0 and a standard deviation of 1. ;)
Standard Normal Model
A normal model with mean of 0 and standard deviation of 1. Also called the standard normal distribution. ;)
Nearly Normal Condition
A distribution is nearly normal if it is unimodal and symmetric. We can check by looking at a histogram or a normal probability plot. ;)
68-95-99.7 Rule
In a normal model, about 68% of values fall within 1 standard deviations of the mean, about 95% fall within 2 standard deviations of the mean, and about 99.7% fall within 3 standard deviations of the mean. ;)
Normal Percentile
The normal percentile corresponding to a z-score gives the percentage of values in a standard normal distribution found at that z-score or below. ;)
Normal probability plot
A display to help asses whether a distribution of data is approximately normal. If the plot is nearly straight, the data satisfy the nearly normal condition. ;)
