PS296 Chapter Notes - Chapter 6: Standard Deviation, Unimodality, Abscissa And Ordinate
Chap 6: The Normal Distribution
Terms to remember:
X axis: the horizontal axis, also called the abscissa
Y axis: the vertical axis, also called the ordinate
Histogram: a plot of data w the values of the dependent variable on the X axis and the frequency with
which they occurred on the Y axis
Bar chart: a graph with the independent variable on the X axis and the mean or other measure on the Y
axis
-if we know something about the distribution of events (or of sample statistics) we know something
about the probability that one of those events is likely to occur
Reasons why the normal distribution is one of the most important:
-many of the dependent variables we deal w are commonly assumed to be normally distributed in the
population
-if we can assume a variable is at least approximately normally distributed, then the techniques that are
discussed allow us to make a number of inferences about values of that variable
-most of the statistical procedures we will employ have, somewhere in their derivation, an assumption
that that a variable is normally distributed
-a normal distribution is symmetric, unimodal distribution (bell shaped) and has limits of ±∞
-the abscissa/horizontal axis represents different possible values of X
-the ordinate/y axis if referred to as the density f(x) and is related to the frequency or probablity of
Document Summary
X axis: the horizontal axis, also called the abscissa. Y axis: the vertical axis, also called the ordinate. Histogram: a plot of data w the values of the dependent variable on the x axis and the frequency with which they occurred on the y axis. Bar chart: a graph with the independent variable on the x axis and the mean or other measure on the y axis. If we know something about the distribution of events (or of sample statistics) we know something about the probability that one of those events is likely to occur. Reasons why the normal distribution is one of the most important: Many of the dependent variables we deal w are commonly assumed to be normally distributed in the population. If we can assume a variable is at least approximately normally distributed, then the techniques that are discussed allow us to make a number of inferences about values of that variable.
