1. Given Probability (X<6.5) = 84% and the mean is 4.2 minutes
1A) Probability (X<4.2) = 100%-84%= 16% then (16%x2=32%) which accounts for both
sides of x being less than 1.9 and greater than 6.5. 100%-32%= 68% which is the area
between 1.9 and 6.5. 68%/2
1.
A)
Sample proportion of funds that have a sales charge: 80/140 = 57.14%
B) Based on a sample proportion of 57.14%, there is a 95% probability that the
population proportion of mutual funds with an up-front sales charge lies between
48.95% and 65.34%.
C
Part 1: Independent Samples
1. A) Sample mean= Mean Hourly= 227.58333
Mean Commission= 247.91667
Sample variance= Variance Hourly= 191.53788
Variance Commission= 466.08333
B) Stating Null and Alternative Hypothesis for f-test (variances):
2
2
2
H 0 : 1= 2
Introduction to Statistics
2
1.
Fundamental Statistical Concepts
Objectives
Decide what tasks to complete before analyzing
the data.
Use the Summary Statistics task to produce
descriptive statistics.
3
Defining the Problem
The purpose of the study is to d
Continuous Random Variables
Def n of a continuous random variable
Consider an experiment having an infinite number of
possible outcomes.
Assign some real number to each possible outcome,
and associate this number with some random variable,
say X.
Example:
Categorical Data Analysis
C.I. on the population proportion
We assume that
(1 )
~ N ,
n
true for large n
So that
P z 2
z 2 = 1
(1 )
n
We need to solve
= z 2
(1 )
n
for ; which yields the following quadratic equation
in :
(1 )
n
z2 2 2
z2 2
2
Alternative Nonparametric Tests
One Sample Test of Location Sign Test
x1 , x2 ,
xn ~ iid Fx (continuous)
The sign test tests an hypothesis about pop n median :
H0 : = 0
H1 : 0
Equivalent to
H 0 : P [ x < 0 ] = 0.5
Use binomial test
H 0 : = 0.5
with test s
Hypothesis Testing
Hypothesis - A statement concerning one or more
populations
Statistical Hypothesis - consists of a null hypothesis and
an alternative hypothesis
Null hypothesis - denoted by H0
states claim to be tested and is often
formulated with hope
Correlation
The correlation between two random variables X and Y is
defined as
XY
xy
Cov( X , Y )
=
=
Var ( X ) Var (Y ) x y
where
cfw_
Cov( X , Y ) = xy = E [ x x ] y y
cfw_
Var ( X ) = 2x = E [ x x ]
2
Properties of
(1) 1 1
(2) is independent of t
Sample Statistics
Let X 1 , X 2 , ", X n be a random sample of size n from
some population with probability distribution f(x; ).
Let X (1) X (2) " X ( n ) denote the order statistics from
this sample.
Mean
1 n
1
x = xi = ( x1 + x2 + " + xn )
n i =1
n
Vari
Discrete Random Variables
Def n of a discrete random variable
Consider an experiment having a finite (or countably
infinite) number of possible outcomes.
Assign some real number to each possible outcome,
and associate this number with some variable, say X
Parameter Estimation
Point Estimation
Let X 1 ,L, X n be a random sample from some
population, characterized by the unknown
parameter .
Let = h( X 1 ,L, X n ) be some f n of the sample data.
is a statistic which is an estimator of .
Example:
X i ~ iid N
Statistics 201 Fall 2016
Exam 1 Practice Exam (from Spring 2016)
KEY
Disclaimer:
This practice exam is provided solely for the purpose of familiarizing you with the
format and style of the Stat 201 exams. There is no explicit or implicit guarantee
that th
Statistics 201 Fall 2016
Exam 1 Practice Exam (from Spring 2016)
Disclaimer:
This practice exam is provided solely for the purpose of familiarizing you with the
format and style of the Stat 201 exams. There is no explicit or implicit guarantee
that the up
Alex Ziomek
Statistics 201
Mrs. Morris
February 14, 2016
Statistics 201 Project #1
Submitted by: Alex Ziomek
The following document is a statistical analysis I have conducted on February 14th, 2016 which is
in response to a student survey consisting of 55
Statistics 201-Project 2- Spring 2016
Submitted by: Alex Ziomek
This report has been prepared and analyzed by Alex Ziomek on March 5, 2016. I will utilize
various techniques of the JMP program as well as Microsoft Excel. The last 2 digits of my student
nu
Statistics 201Project 3Fall 2016
Submitted by Alex Ziomek
This report has been prepared by Alex Ziomek using the data provided I will use excel and JMP to display
this data and describe it in many different ways.
1)
I took a random sample of n=90 for my p
Exercises Lesson 5
Kellie Duncan
In these exercises you will use a new data file, custandhol.dat, which contains information on customer
holidays arranged by a leading travel company. This table lists the fields in the file, which include
customer informa
Exercises Lesson 4
Kellie Duncan
In this exercise, we will first use the Data Audit node to assess a smaller version of the charity data in which
we have introduced some missing data. We will then use the stream created in the previous exercise and the
co
Exercises Lesson 9 Due Friday
Kellie Duncan
In this exercise you will restructure the data that was merged in the exercises for Lesson 8 to create a file that
has one record per holiday code. Each new record will contain information including total number
Exercises Lesson 8
We will use data from the travel company that we used previously for the exercises in Lesson 5. In that
lesson the data file had already been combined; here our job is to combine separate files to create the
merged holidays file.
custtr
Exercises Lesson 7
Kellie Duncan
In this session we will use the stream created in the previous exercises and investigate whether there are any
simple relationships in the data. In future lessons we will attempt to predict the field Response to campaign,
Exercises Lesson 10
Kellie Duncan
In these exercises, we will use the charity data file.
1. Import data from charity.sav (Statistics File; Read names and labels; Read labels as data).
2. First, we will sort the file on Pre-campaign expenditure (ORISPEND)
Exercises Lesson 6
Kellie Duncan
In this exercise we some manipulation on the fields in the charity data.
1. Read data from charity.sav (Read names and labels; Read labels as data).
2. Connect a Filter node between the Statistics File node and the Table n
Exercises Lesson 3
Kellie Duncan
In these exercises we will practice using the source nodes demonstrated in this lesson. The exercise data file is to be
used throughout the course and exists in three formats; comma delimited (charity.csv), Excel (charity.
Chapter 20
Chapter20 Presentation 1013
Testing Hypotheses About
Proportions
Still using the sampling
distribution model for p^
#
A Trial as a Hypothesis Test
Think about the logic of criminal jury trials in the
USA:
All suspects are presumed innocent unti
Chapter 1
Chapter01 Presentation 0813
Stats Starts Here
#
Stats Starts Here
Statistics gets a bad rap
Statistics courses are not necessarily chosen as
fun electives
Statistics can be fun! Learning to think clearly
with data will open your eyes to seeing t