SignificanceTesting
The assumption that what is being tested
has no significant relationship. The null
hypothesis attempts to show that no
variationexistsbetweenvariables,orthata
singlevariableisnodifferentthanzero.
Residuals:
Standarddeviationoftheresid
Stat 102: Quiz 1
Name:
Section:
Record your answers here. Answers not recorded here will not be graded. There is one
answer to each question.
1.
2.
3.
4.
1. The daily receipts of a fast food franchise are normally distributed with a mean of 2000
and a sta
Statistics 102 Homework 1: Due Monday September 15th, 2014
Notes: 1. All data sets are available in the homework folder in Canvas.
2. Unless stated otherwise, all tests should be performed at an =.05 level and confidence
intervals should be 95%.
Textbook
STAT 102 Problem Set 5, Spring 2015
Due: 5pm Tuesday, April 14 (NOTE: This problem set is due on a TUES)
Read:
Indicators, polynomial regression:
Dielman 7.1-7.2, 5.1-5.2
Model building:
Dielman 8.1-1.4
Finalcial timeseries
Dielman 3.6, 4.7, 7.3
Do:
Probl
Stat 102 Solutions: Homework 1
1
A
a. P (0.0 Z 1.2) = 0.3849
c. P (0.0 Z 1.45) = 0.4265
g. P (Z 2.50) = 0.0062
h. P (Z 1.66) = 0.9515
B
i) X:= monthly demand,
X N (200, 502 ), Z =
P (X > 275) = P (
X 200
N (0, 1)
50
X 200
> 1.5) = P (Z > 1.5) = 0.0668
50
Hyunseung Kang
STAT 102 Review Guide
Mar. 19, 2011
March 19, 2011: Thanks to Eliza and Jennie, xed typo on Sm and Sp in simple linear
regression as well as the typo on on the prediction interval for dierence in only ONE x
March 20, 2011: Thanks to Tianpu,
STAT 102
Homework 5
Due Date: Tuesday, March 23 before 5:00pm in 400 JMHH.
Note: Ignore the output from the text book. You should produce JMP output and use that
in your solution to all problems.
1. Problem 4.4 (p. 158)
2. Problem 4.8 (p. 169) from the te
Solutions for Statistics 102 Midterm #1
Professors Lawrence Brown & Linda Zhao
Solutions prepared by Adam Kapelner and Emil Pitkin
Some editing by J. Bleich many years later
100 points total
Feb 23, 2010
Problem 1
a
We conduct a matched pairs test. Let Di
Statistics 102 Overview Lecture 18
Theme: Building Models with Categorical Variables
Two kinds of models:
Additive model (no interactions) parallel lines
Interaction model: The effect of the categorical
variable on the response differs by value of anot
Overview Lecture 1 Statistics 102
o Administrative Issues
o Overview of Course
Theme: Predict a variable of interest (Y) using available
information (X variables- predictors)
Questions:
How would we predict Y for a new instance and with what
accuracy?
W
Statistics 102
Fall, 2016
Introduction to Business Statistics
Instructor and TA
Robert Stine
Linjun Zhang
444 Huntsman Hall
[email protected][email protected]
Professor Stines office hours follow classes on Tuesday and Thursday, from 4:30-6 pm.
Mr. Zhang will
Statistics 102 Overview Lecture 13
Multiple Regression Model and the Nitty Gritty
o Multiple Regression Model
Statistical view: as subpopulations Meaning of the
slope in multiple regression- partial slope
Practical view: The role of the predictors as si
STAT 102: Introduction to Business Statistics
Lecture 2: Standard Error and Central Limit Theorem
Zongming Ma
The Wharton School
Spring 2017
Agenda
Agenda for today
Last time
The population/sample paradigm
Standard error
Central Limit Theorem
Summary
STAT 102: Introduction to Business Statistics
Lecture 5: Fitting A Line to Data
Zongming Ma
The Wharton School
Spring 2017
Agenda
Agenda for today
Last time
A motivating example
The least squares regression lines
Summary
Next time
STAT 102, Lecture 5
STAT 102: Introduction to Business Statistics
Lecture 6: A Probabilistic Model for Simple Regression
Zongming Ma
The Wharton School
Spring 2017
Agenda
Agenda for today
Last time
The simple regression model
Inference in regression
Summary
Next time
ST
STAT 102: Introduction to Business Statistics
Lecture 4: Comparative Analytics
Zongming Ma
The Wharton School
Spring 2017
Agenda
Agenda for today
Last time
Comparative analytics
Comparison of means
Summary
Next time
STAT 102, Lecture 4
2
Last time
La
Statistics 102
Spring 2013
Midterm Exam I
Text
Read these instructions carefully.
This is a closed-book exam. You are allowed to use a calculator and one page (8.5 by
11 inches or A4, both sides) of notes. No use of cellular telephones or other portable
e
Extra Hypothesis Testing Problems
The human resource department division of a US company is concerned that interviewers are biased
against women applicants and this might lead to a lawsuit. Therefore, it devised the following test for its
interviewers.
45
1
Department of Statistics
The Wharton School
University of Pennsylvania
Statistics 102
Fall 2014
Midterm Exam 1
Read these instructions carefully.
This is a closed-book exam. You are allowed to use a calculator and one page (8.5 11 inches
or A4, both sid
Statistics 102 Overview Lecture 16
Review Multi-collinearity
Model Improvement: Residuals Residual Residuals
o Diagnostics to Uncover Improvements
Residual Plot
Outliers in the Y-direction- lonely vertical
Transformation of Y- U or shape
Unequal varia
Review for Second Midterm
Stat 102
Bob Stine
Wharton
Department of Statistics
1
Midterm
Admin
Next Tuesday, Nov 8, in class Be on time!
1 hour, 15 minutes
Multiple choice
Exam rules
No phones
Bring your Penn ID with you
Pencil and eraser
Allowed: Calculat
Corporate Finance
Discounting
Yrj Koskinen
1
First Basic Principle of Finance
A dollar today is worth more than a dollar tomorrow
But how much more?.
2
Topic Overview
Compounding & Future Value
Discounting & Present Value
Multiple Cash Flows
Special Stre
Statistics 102 Overview Lecture 26
Topic: Logistic regression- predicting the probability
of a dichotomous outcome
o Review: Basic questions (Begin with one X)
What is the meaning of the slope? Is X significant?
What is the meaning of the intercept?
Ho
Statistics 102 Overview Lecture 25
o Contingency Table
Applications
1. Bayes Rule: Col % Count Row %
2. Understanding through simulation
Review Example
o Logistic regression- predicting the probability of
a dichotomous outcome
CONFUSERS: 1.The meaning o
Statistics 102 Overview Lecture 23
Theme: Y(#s) versus Xs (Categories).
o One Way Analysis of Variance (ANOVA) Review
Question 1: Are Y and X related (F-test)?
Question 2: Which levels are different (Tukey; Hsu)?
Question 3: Are the assumptions reasona
Statistics 102 Overview Lecture 24
Theme: Do the counts for categorical variables agree with some
model (null distribution)
o Application I- Goodness of Fit
Establish Approach
o Application II- Are two categorical variables related
Apply the goodness of
Statistics 102 Overview Lecture 17
Review: Modifying the model
Residual Plot
o Extreme observations in the Y-direction
o Fanning out unequal variability
o U or shape- non-linearity
Leverage Plot
o Extreme observations in the X-direction
o U or shape- non-
Statistics 102 Overview Lecture 19
o Review: Building Models with Categorical Variables
Two kinds of models:
Additive model (no interactions) parallel lines
Interaction model: The effect of the categorical
variable on the response differs by value of a
Statistics 102 Overview Lecture 22
Theme: Y(#s) versus Xs (Categories).
o One Way Analysis of Variance (ANOVA) Review
Question 1: How do we estimate the parameters?
Question 2: Are Y and X related (F-test) ?
Question 3: Are the assumptions reasonable?
Lecture 1
Introduction
Stat 102
Bob Stine
Wharton
Department of Statistics
1
Topic for the Day
Welcome back
Course overview, grading,
Variation in data
Sample-to-sample variation
Anticipating the amount of variation among samples
Role of models and assum
Lecture 6
Linear Patterns
Stat 102
Bob Stine
Wharton
Department of Statistics
1
Review
Testing several proportions: chi-squared
Broad test for any difference between two collections of
proportions
Generalizes two-sample comparison of proportion
Conditions
Lecture 8
Logs and Curves
Stat 102
Bob Stine
Wharton
Department of Statistics
1
Admin
Office hours
Changed to earlier in the day on Monday, Wednesday
Monday
2:30 to 4 pm
Wedsnesday 12:45 to 2 pm
Quiz
At start of class on Thursday
10 minutes, no calculator
Lecture 10
Simple Regression Model
Stat 102
Bob Stine
Wharton
Department of Statistics
1
Midterm exam
Admin
Thursday evening, October 1, 6-8 pm
No adjacent seating
Locations (also on Canvas)
Section 1 10:30 am
JMHH F85 and F95
Section 2 1:30 pm
SHDH 350 a
Lecture 4
Comparing Two Averages
Stat 102
Bob Stine
Wharton
Department of Statistics
1
Admin
Quiz in class on Thursday
Begins at start of class
10 minutes
No calculator necessary
Coverage: Through todays class
Text: Emphasis on Ch 14-17
Wharton
Department