Sep 7/8
Sep 7 — Statistics
Normal Distributions.
Gauss. 1775—1846. Gottingen, Germany.
1801: discovery of the asteroid Ceres.
Sun has 8 planets (1801). 2-dim measurements, must find 3-dimensional equation.
the distribution associated with measurement; the effect of the error of measurement on any sampling.
The graph of the normal distribution: normal distributions are the result of chance occurrences. Measurements of a single object are usually distributed in a normal pattern. For instance, if each student independently measures the length of the table at the front of the room, the measures will follow a normal distribution.
The vast majority of measurements follow a normal distribution.
The function is normalpdf(x, [μ, σ]) where x is a dummy variable and the mean and standard deviation are 0 and 1 and can optionally be changed to whatever you need.
The formula for the function with mean 0 and std. dev. 1 is
No matter what value are given to the mean and standard distribution, the total area under the curve is always 1.
On the screen: To obtain the graph of the function, go to
2nd VARS DISTR 1
Do this while in the Y= menu.
Y1=normalpdf(X,0,1)
WINDOW:
Xmin=-3
Xmax=3
Xscl=1
Ymin=-.01
Ymax=.4
Yscl=.1
Then GRAPH.
For this graph, 68% is within one standard deviation, 95% within two standard deviations, and 99.7% within three standard deviations.
The areas can be computed with shadenorm(lowerbound, upperbound, [μ, σ]).
This function will shade the indicated area, and display the percentage of area shaded.
One must set the window as above: [-3,3] and [-.1,.4].
In your text you will see frequent reference to z-values.
The z-value is a normalized value, intended principally for use with tables of the standard normal.
reduces any x value to a z value, where the mean and standard deviation of the distribution are known.
With the TI, one can insert μ and σ directly, and not bother with computing z.
How does one find the x or z value which corresponds to a given area?
Two methods: 1) table; 2) TI-83+.
On the TI, we use invNorm(area, m, s) .
Use of this function is similar to other statistical operations.
Unfortunately this only gives us the boundary of the “left tail”, similar to the output of Normcdf.