MATH 251, P & S I, Fall 2011, Mon. Sept. 5,Day 5.After class.  Hit reload!

Meet in Mac 101 lab Wednesday YES  for big SPSS intro.  Bring a disk or usb, book.
Day 5: Reading: IPS7e 1.3 Density curves 50-54,  Normal distribution, pp.54-64. 
Read ahead:  Normal quantile plots, 65-67; then Ch. 2

Hand in: 
N.b.:  "cf." is short for "compare to" in reference speak. From Latin "confer."
ff.  = and following. I.e. = "That is" (Latin "id est").  E.g. = "For example" (Latin "Exempli gratia") N.b.="nota bene", note well.


Sec. 1.3,, pp.69ff.
Density Handout:
complete the tables by counting squares.  (Look for patterns, to stay entertained...)
 
p.70, 1.116, 117, 1.18  (uniform density. 116 and118 are using the first density on the handout )
1.19 (mean, median, mode)

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.
.Only the above:  Postpone the rest..
Normal density p. 84ff. Always sketch the curve, mark the area(s) you need.
p. 57, 1.101&2, test scores, Rule
1.120 (sketch) 1.122 (pregnancies, rule)
1.165 compare 2 curves (graph, p. 76)
1.112 (Women talk more?)  Use the Rule to give the limits containing 68, 95, and 99.7% of the speakers, M & F; and use that to answer (b).  (I.e. are these data Normal?)
1.24 Use  Normal Density Curve  Applet to find Quartiles in Normal
p. 681.103 test score z-scores
1.132&1.33 (SAT/ACT, compare)  Find the z-scores.  Also sketch both normal curves, labeling  the axes  in points, at + 1 s.d.  and mark all the students' scores on them.

1.126 (forward), 1.128 (backward) (standard normal table)
- - - - - - - - - - - -
The rest of these on separate paper as part of Day 7: Do all of these using the  Normal Density  Applet  http://www.whfreeman.com/ips7e/ to get answers. Leave space to do the calculations to get the answers from Table A. Always sketch the curve, mark the area(s) you need.
B. Simple warmup: Non-standard normal, table problems: 
    X is normal with mean 3 and s.d. 2. Find: 
    The proportion with X<1.5.  1.5 < X < 4.5. 

p. 63, 1.105 & 106 test scores, proportions

1.144 (osteoporosis)  Also sketch both curves on the same axis.
1.145 a, b (pregnancy)  (c will be assigned later) 
p.77 1.176d    (75's are Raw scores.  You could transform each to the N(100,20) distribution, if you remembered your formulas from parts a and b, or (shorter) you can find the percents asked for directly from the separate grades' distributions.  In each case you've found the percentile for  a grade of 75.)

C.  A small difference in means may give a surprisingly large proportional  difference in tails: 
a) Return to the data of 1.165, p. 75&6, and calculate what proportion of Females score below 450 and what proportion of Males score below 450.  Answer this:  The proportion of Males scoring below 450 is ____ times  the proportion of Females scoring below 450.
b)  Some of the difference in  (a) may be because the Male s.d. is larger, not just the difference in means.  Recalculate the proportion of Males who would score before 450 if their mean were still 565 but their s.d. 49 (= F s.d.)  The proportion is now ______.
This assumes, of course, that the Normal model holds up into the tails;  always questionable.

Read, discuss 

p. 77 1.173 summary #s enough?

p. 69, 1.111, sketch densities.

.Postpone the rest.
p. 69, 1.109&10: Sketch Normal;  shift and stretch





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)


 
 
 


 

 


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.

Many, many models are possible, modeling many phenomena:  (Histograms of data for some models) Median, mean, percentiles, standard deviation are defined for a density model in analogy to those for a histogram.
-- median has half of area below and half above.
-- mean is balance point.  On the long-tail side of median if distribution is skewed. Same as median if symmetric.
--First quartile has 1/4 of area below, 3/4 above. Etc. for others.
--Greek labels "mu" for mean and "sigma" for std. dev. of a Density.
Complex models require tables to find proportions. 
Make some cumulative proportion tables: Handout Density  (Solutions)

.Got to here Monday..
"Normal" Density
:
("Gaussian", "Bell-shaped")  Normal Curve Applet  http://www.whfreeman.com/ips7e/

Standardizing: A "raw value" x is standardized by telling how many standard deviations above the mean it is.
    Find z:  Subtract the mean from x.  Now you know how far "above" the mean x is, in "raw" units. (If it's below the mean, the number will be negative.)  Find how far this is in "standard deviations" by dividing by the standard deviation.
That's the z-score.

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.

Examples: Psychology test "W" scores are approximately N(110, 25)
   A score of   85 is 1 s.d. below the mean.  Computation:  z = (85 110)/25 = (–25 raw points)/25 = –1 s.d. from mean.
           (About the 16th percentile--16% get scores < 85)
   145 is how many s.d.'s above the mean?
            Computation: z = (145110)/ 25=  (35 raw points above mean)/25 = 1 2/5 = 1.4 s.d. above mean

 
~ ~~ ~ ~ ~ ~ ~ ~ ~ ~ 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

.bell curves. Use 202x515 pixels to print.
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.


Real/Raw data:
  Check with 
Normal Curve  Applet  http://www.whfreeman.com/ips7e/
 "What proportion"problems:  

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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