Statistics 285 Cheat Sheet
Population - is a set of experimental units we are interested in studying
Variable - Characteristic or properties of individual units
Sample Subset of population
Statistics is a summary measure computed to describe a characteris
List of Practice Problems:
2/20,21,38,40,41,57,58,62,72,73,74,75,91,106,108,109,141,142,
144,146,147
3/3,4,6,7,28,29,30,31,32,49,50,51,52,54,55,56,60,62,90,91,92,9
3,94,95,96,97,98
4/1,2,11,12,13,14,15,16,17,18,19,27,31,40,41,44,45,49,51,53,56
,67,75,77,7
Stat 285-6, Spring 2011
Guidelines for Mid-term Exam 1. The Mid-term Exam will be held on March 8, 2011 (Tue) in the usual classroom starting at 8:10 pm and will last for one hour and twenty minutes. 2. The Exam will cover Chapters 1 through 4 (up to Sect
Central Limit Theorem - when n is sufciently large,
Large sample 100(1-)% C.I. for (Z-Table - test statistic)
- if is known and n is sufciently large, the C.I. for is
- if unknown and large?
sampling dist. of x is approx. a normal dist.
Small sample C.I.
Target Parameter
Identifying and Estimating the Target Parameter
In practical situations, our goal is to estimate the value of an unknown population
parameter
Mean gas mileage for a new car model
Average expected life of a TV
Proportion of dot-com compani
SYLLABUS Introductory Statistics for Business (Stat 285)
Course: 01:960:285:06, Spring 2011 Class Times: TTH 8:10-9:30 pm Room: SEC 117, Busch Campus Instructor: Dr. Shyam Moondra e-mail: moondra@rci.R ("R" means "rutgers.edu") Office: Hill Center 469, Bu
Variability
Numerical Measures of Variability
While measures of central tendency are important, they provide only partial
knowledge of the data
In order to get a more complete picture, we must look at the variability, or spread,
of the data.
With both a m
Intro to Statistics
The Science of Statistics
What is statistics?
Statistics is the science of data. It involves collecting, classifying, summarizing,
organizing, analyzing, and interpreting numerical and categorical information
Types of Statistical Appli
Data Methods
Methods for Describing Sets of Data
Consider a set of 400 incoming freshman and evaluating their SAT scores. What
kinds of ways can we describe this dataset?
Most frequent score
Range of scores
Variability of scores
Shape of the data
Summary of Discrete Probability Distributions
Description Binomial (Discrete) Examples p(x) or f(x)
n P( X = x) = p xqn- x , x where x = 0,1,2,., n
= E(x)
Variance 2
npq
Std. Dev.
npq
Experiments involving only two outcomes. Example: Flipping a coin and
Final Review Study Guide
Answers
Sorry, I do not have the time to make these more elaborate.
1. Pcfw_-.3 < Z < .95 = .447.
2. mean: 8. std. dev.: 4.31. CV: 53.9%.
3.
a)
44479
2245667
18
2349
32346
b)
Relative
CumulativCumulativ
Relative
e
e
Frequenc Frequ
20
Gender
#
1
Female
15
10
Male
5
0
Color of
Hair
#
%
20
15
10
5
0
20
Live in
Dorm
#
%
15
10
5
0
1
Color of Hair
(X)
Frequency
(f)
Relative
Frequency
(%)
Blond
2
7.7
7.7
Black
19
73.1
80.8
Brown
5
19.2
100.0
Table (Report)
Variable
Class or Category
Frequ
Collection of Data
Sampling and Related Issues
Published Sources
Experimental studies: the researcher has strict control over the units in the study
US census data
A placebo-controlled study of a new drug treatment for heart disease
Observational studies:
Fundamentals of Statistics
Fundamental Elements of Statistics
When we study populations, we focus on one or more characteristics or properties
of the individual units in the population. These characteristics we call variables.
Lung function of cystic fibr
Events, Sample Spaces, and Probability
Consider rolling a die and recording the outcome
The result we see is called an observation, and the process of making this
observation we can call an experiment
An experiment is an act or process of observation that
Statistics 285 Cheat Sheet
Population - is a set of experimental units we are interested in studying
Variable - Characteristic or properties of individual units
Sample Subset of population
Statistics is a summary measure computed to describe a characteris
Chapter 6
A. Confidence Interval For mu:
a. Large Sample: N>30- Z-score Formula
b. Small Sample: N<30 -Sigma is givenZ-Score; Sigma is not given t formula
B. Confidence Interval for Mu when N>30
a. Central Limit Theorem
b. X is normal regardless of popula
S2
S
Median is the middle measurement
once data is in increasing order
Intersection =
The most frequent measurement
Union =
Additive
Compliment
Rule:
Sample Variance Formula
=
Z- Score Formula
Probability Rules
If 2+ things can happen together,
then multi
"I NOTATIONS MEASURES OF CENTER
_ POPULATION SAMPLE
C(I'IJ] _ I" (n-r)' , _ N MEAN Epopu awn mean
' I MEAN U X Z X _ Z = summatlon sxgn
2 U = 1:1 1 xi :value ofElement: ofthe sample
n. VARIANCE 0 52 N N : popul an on 5:25
p = ' Sample Mean
1') ("4)! ST. D
Random Variables
A random variable (RV) is a variable that assumes numerical values associated
with the random outcomes of an experiment, where one (and only one) numerical
value is assigned to each sample point.
We use the term random variable instead of
Binomial Distribution
The binomial distribution, one of the most useful discrete distributions, is based
on the notion of a Bernoulli trial. A Bernoulli trial is one that has exactly 2
outcomes
Many experiments can be modeled as a sequence of Bernoulli tr
Unions and Intersections
Often we can think of events as a composition of 2 or more events.
These events are called compound events
Compound events can be formed in 2 different ways
Unions
Intersections
Intersections
The intersection of two events A and B
Sampling Distributions
A parameter is a numerical descriptive measure of a population. Because it is
based on the observations in the population, its value is almost always
unknown.
The success probability p in the binomial experiment or u and mu in the
n
RUTGERS UNIVERSITY
Department of Statistics
Instructor: H.K. Dong
Phone: (732) 742-7184
hkdong@rutgers.edu
Time: TTH 8:10-9:30PM
Room: SEC 117
Office Hours: TTH 7:00-8:00PM
(By appointment only)
01:960:285:06 INTRO STAT FOR BUS
Spring 2016
This is a cours