**Kin
550 Statistics Review:**

Use
the organizational tree in the back of the stats book.

Nominal
Ð numbers are mutually exclusive categories- football players by number

Ordinal-
ranking ( but not based on time for instance)

Internal:
ranking and intervals are equal

Ratio-
0 represents absence of attribute, distance between numbers is meaningful
order, and intervals are equal

Descriptive
statistics:

Mean
sum of x / n

Median
Ð middle score

Mode-
most frequent score

Standard
deviation- extent to which data deviate from the mean

Tests
between two means

**Assumptions:**

Normal
Distribution

Not
skewed in either direction- positively or negatively

**If
the distribution is not normal then you need to use non-parametric statistics
such as:**

** Chi squared, Wilcoxin, mann- whitney U,
Kruskal Wallace,**

If
the distribution is normal then you use parametric statistics.

Use T-tests to test between two means of different groups

paired
T-tests to test between two conditions of the same group

If
you are testing two or more groups you can use a t-test with a bonferonni
adjustment makes it less likely ( more rigorous) that you will find a
significant difference each test you perform.

**Analysis
of variance**

but
the more accepted method is to use an analysis of variance- This takes into
account the fact that you are testing multiple groups.

This
test only tells you that there is a difference between groups it doesn't tell
you where the differences are.

Need
to use post hoc tests to tell where the differences are. It tests between group
means. You can only use this if there is a significant difference for
"main effects" using the F- test.

**Two
way analysis of variance.**

Two
conditions with two or more groups. Smallest would be a 2 X 2 analysis of
variance. Possible main effects would be.

1)
a significant difference between either of the conditions pre and post

2)
an interaction meaning differences that do not change the same way over time.

If
there are significant differences then post hoc tests can be performed to see
where the differences are. This is not such a problem with a 2 X 2 but with
more groups than that it is difficult to see the differences.

** **

**Calculation
of the **

F
statistic: MSb/ MSw = (between
groups variance ) / ( within groups variance)

F
obt > or equal to F crit then reject the null hypothesis

F
obt < F crit then retain the
null hypothesis

the
greater the number of subjects the lower the F statistic needs to be to be
significant

Sum
of Squares =

**Power
Ð **Higher the power the
more sensitive the experiment

Power
is a probability and varies between 1 and 0

The
power is the probability of making a correct decision when Ho is false.

** **

Null
hypothesis: independent variable has no effect on dependent variable

Example:
marijuana has no effect on reaction time.

Type
1 error: reject null hypothesis when null hypothesis is true

Find
significant difference when there is no difference.

Poor
design or want to see a difference when there is none.

Type
2 error: retain null hypothesis
when null hypothesis is false

DonÕt
find a significant difference when there is one

(small
sample size etc)

Regression
Analysis:

Predicting
grade point average from IQ.

Grade
point average is the dependent variable

IQ
is the independent variable

Other
factors

Study
time

Taken
the course before

Quality
of reading materials

Number
of class periods attended etc.

Each
factor can be weighted to determine how much it contributes to grade point
average.

**Correlation:**

Not
used to predict one variable from another but used to see if there is a
relationship and what direction it
is.

For
instanceÉ price of real estate as a function of distance from San Francisco or
some other desireable location.

Real
estate price as a function of quality of the school district.

Correlation
of 1 as one variable increases so does the other

BW
and sprint time.

Correlation
of Ð1 as one variable increases the other decreases:

BW
and sprint speed.