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Hand in: A. Use Table A (back flyleaf, both sides,
or pp.. T-2&3, back of book) just like the Density handout.
The "z" and ".00" columns are the equivalents of "x" and "area to the
left of x". Confirm that the area for z less than 0 = .5, area less
than 1=.8413, area less than 2 =.9772, area less than -1 = .1587.
By appropriate subtractions, calculate the area between 0 and 1, the
area between -1 and 1, the area between 1 and 2, and the area above 1. p. 63, 1.105 & 106 test scores, proportions C. A small difference in means may give a surprisingly
large proportional difference in tails: |
Read, discuss p. 77 1.173 summary #s enough? .Postpone the rest.
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Optional .Postpone these. Normal density 1.121 Use Normal Density Curve Applet to check on the Rule 1.23 (horse preg's , rule) 1.127 (forward), 1.129 (backward) (standard normal table, more practice) 1.130 & 131 (Wechsler WAIS, more practice) Use Normal Density Curve Applet http://www.whfreeman.com/ips7e/ (or theapplet for ips6e, etc.) to check on all your Normal calculations! (The Applet goes by .02's, and the text by .01's so the answers may differ slightly)
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Meet in Mac 101 lab
Wednesday (unless I email otherwise) Day 6 for big SPSS
intro. Bring a disk or stick.
Friday Day
7 Quiz: In class, closed book: Stemplot, 5#summary and
boxplot. Mean & s.d. by hand, showing all steps.
Boxplot Cartoon
Handout:
Density handout (solutions,
not handed out)
HW questions? Esp. Linear
transformations Notes, Day 4
Solutions
1.3
Density function or curve: idealized histogram.
"Model"
Area = relative frequency.
Any curve that is above the x-axis and has area exactly 1 under
it can be thought of as the idealization of some set of observations,
and
can be called a Density curve. We carry over our terms for shape,
and our summary measures.
Densities
(When values can take on any of a continuous interval of numbers)

Example: Spinner: Label edge with continuous values from
0 to 1. Spinning should produce 1/10 of all spins in each colored
sector.
Simulations of 500, 3000 spins show roughly true. More spins would get
closer to Uniform shape.
Abstraction, idealized histogram ("Probability Model") =
Density
curve. Describes a theoretical distribution of
data.
Any such model is a curve
--always on or above the horizontal axis
--has area exactly 1 underneath it.
.Got to here Monday..
"Normal" Density :("Gaussian",
"Bell-shaped") Normal
Curve Applet http://www.whfreeman.com/ips7e/
Standardizing: A way of comparing an individual
against
its pack.
Comparing individuals from different packs, each relative to its own.
Removes "units of measurement" from the discussion.
Enables use of the standard normal table.

~ ~~ ~ ~ ~ ~ ~ ~ ~ ~ First standard normal table use, then with
"real" values~ ~ ~ ~ ~ ~ ~ ~ ~
..
OPTIONAL aids:
Normal curve template (you can count
squares), Normal practice handout
Standard Normal N(0, 1). Our tables give area to the left of a z value--"Cumulative Proportion".
Table A, back flyleaf
Using standard normal table: p. 77
z | .00
.01 .02 .....
...|
1.4 | .9192
.9207 .9222 ....
P(Z < 1.40) = .9192, P(Z < 1.41)
= .9207 P(Z < 1.42) = .9222.
?z has more than 2 dec. places? Round to 2.
Sketch the density, mark the area
you're
looking for.
Figure out how to get it using areas to
the left of one or more z-values.
Think cutting up paper
bell-curves.
(Remember whole area is 1.) Like handout.
Example: Proportion of observations between 0.5 and
1.4
P(0.5 < Z <1.4) =
Proportion of observations below 1.4 minus
Proportion
of observations below 0.5
P (Z < 1.4) - P(Z < 0.5) = .9192 - .6915 =
.2277
.
Example: Proportion of observations above
0.5,
P( Z > 0.5) =
ONE minus proportion of observations below 0.5,
1 - P(Z < 0.5) = 1-.6915 = .3085
. Reading
table "backward":
What z value has area ..... to the left/right
of it?
Sketch roughly.
Restate
(if
needed) as "What z value has area A to the LEFT of it."
Look
in body of table for the value closest to A.
Go
to edge(s) of table to find what z that goes with.
Example: "What z value has 10%
of the observations above it?" This is the same z as the one for:
"What
z value has 90% of the observations below (to the left of) it."
(What z is the 90th percentile.)

Find
in the table .8997 and .9015 -- .9000, our number, is
between
them.
.8997 is a little closer to.9000, so use it.
For .8997, the z value is 1.28. 1.28 is the
90th
percentile.
1.28 has 10% of the observations above it.
Example: Proportion with scores
between 100 and 145?

x = 145 gives z = 1.4 (done
above.) Area to left of z = 1.4 is .9192
x = 100 gives z = –.4
Area to left of z = –.4 is .3446
Desired area = Difference= .5746; about 57%.
Looks about right from picture.or
P ( 100 < X < 145) = P ( –.4 < Z < 1.4) = P( Z
< 1.4) – P(Z < –.4) = .9192 – .3446 = .5746
Read
"Proportion of x's with 100 <x<145" for P(100<X<145)
- - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - - -
"Backward problems"
"What raw
(x) value has area ___ to the left/right
of it?"
Sketch the curve, labeled with x values and z values, and the
Area, roughly.
Restate
(if needed) as "What z value has area A to the LEFT of it."
Look
in body of table for the value closest to A.
Go to edge(s)
of table to find what z that goes with.
Convert the z to an x: z
is the number of standard deviations above the mean.
Multiply z by the size of 1
standard deviation. Now you have
distance above the mean, measured in raw units.
Add the mean. Now you have the "raw" value x. (You have
"unstandardized")
Example: "W" test: What x value has
10% of the observations above it? This is the same x as the one for:
What x value has 90% of
the observations below (to the left of) it.

The table gives z = 1.28, approximately.
The "W" score x= mean
+ z (s.d.) = 110
+ 1.28 (25)= 110
+ 32 = 142
Percentiles: a "W" score of 142 has 90% of the scores at or below
it. 142 is the 90th percentile.
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