Lecture 5: Counting rules, Compressed
Complex experiments use counting rules.
1. Permutations: the number of different ways a set of N things can be arranged
N!( n factorial)
until (2)(1) bil
-Random sample: a sample selected from a population such that all members of the population
have an equal probability of being selected (true random sample)
-names on a piece of paper: pull out of a hat, one by one until select 4
Interpreting Standard Deviation
-From last lecture
more than 10 times larger
- Since they had the same variable, we can just compare the 2 standard deviation and there is
substantial more variability in sample 2 than 1.
-Suppose we have just on
May 6th lecture
-. Statistics: involves the measurement, description, and analysis of variables
-. Variables: anything that can be measured and which show some variability in the population (eg.
Height, weight, scores on test (nume
Lecture 10: Large-Sample Hypothesis Testing about
Review of concept
Logic behind the rule,
X number of outcomes
Why does it work?
probability is really small
If the coin is fair. If the coin is biased then well get more 9-10 heads,
If its fair then its r
Lecture 11: Simple sample t Test about
1. Generic, 2 2 tailed, 3-4 1 tailed
Large sample (n >30)?
Small sample (n<30)?
Do we know the SD?
we only know the sample sd
Sx is sd
t critical defined by degrees of freedom
(n-1) degree of freedom
Lecture 9: Sampling Distribution
-the sampling distribution of the mean is the distribution of the means of all possible samples of
a given size that could be drawn from a population
How many such samples might there be?
Even with a sm
Lecture 8-Continuous Random Variables
Z scores and p values
Normal approximation to the binomial distribution
-Continuous random variables can assume an infinite number of possible values.
discrete binomial can be
calculated by co
Lecture 7: Using Binomial Experiments to Test Hypotheses
Null hypotheses and alternative hypotheses
Rejection regions and decision rules
Type I and Type II errors
task: experiment try to figure out, is the
coin fair or biased?
Lecture 6: Discrete Random Variables (DRV)
Discrete Random Variables (DRVs): variables that can only assume a finite set of precisely
Example: scores on a test where each item is scored 0 or 1
Possible scores: 48 63
a. Quantitative variables can be measured numerically and can have a number score (ex.
Height) while qualitative variables are expressed in verbal categories (ex. Hair color).
b. Discrete variables assume a finite set of precisely countable values (ex.