John Lorge
N13495925
John Lorge
Answer Sheet, Homework 2
1.
a) The histogram seems to have a reasonably bell shaped distribution with no outliers.
b) The mean is 98.6 degrees Fahrenheit.
c) Yes, the histogram mean is reasonably close to the 98.6 mean. Whi
Inferences Based on Two Samples Solutions
STAT-UB.0103 Statistics for Business Control and Regression Models
Comparing Two Populations
1. Here are boxplots of the passing distances (in meters) for a bike rider with and without a
helmet. Is there evidence
Condence Intervals Solutions
STAT-UB.0103 Statistics for Business Control and Regression Models
Condence Interval
1. A random sample of 36 measurements was selected from a population with unknown mean
and known standard deviation = 18. The sample mean is
Introduction to Linear Regression Solutions
STAT-UB.0103 Statistics for Business Control and Regression Models
Linear Regression
1. Consider two variables measured on 294 restaurants in the 2003 Zagat guide:
y = typical dinner price, including one drink a
Complementary Events / Counting Solutions
STAT-UB.0103 Statistics for Business Control and Regression Models
Probability
1. When rolling a die, event the outcome is even. What is the probability of the event ?
Solution: There are 6 elementary outcomes, al
Statistics for Business and
Economics
Chapter 11
Simple Linear Regression
1
Introduction
Usually one of the variables is naturally
seen as causing, influencing, or
predicting the other variable
X variable
independent variable, predictor, explanatory
va
Statistics for Business and
Economics
Chapter 12
Multiple Regression and Model
Building
Learning Objectives
Introduce a multiple regression model as a
means of relating a dependent variable y to
two or more independent variables
Different multiple regress
Course Outline Spring 2014
STAT-UB.0103.004 Statistics for Business Control and Regression Models
Meeting Time & Place
Lectures: Tuesday, Thursday, Friday: 9:30 AM 10:45 AM
Final Exam: May 14th (Thursday) 8:00 AM 9:50 AM
Midterm 1: Feb 27th (Friday) 9:30
Chapter: 2
Methods for Describing
Sets of Data
What we will learn?
1. Describe data using graphs
2. Describe data using numerical measures
(summary table and statistics)
2
Data Presentation
Data
Presentation
Qualitative
Data
Quantitative
Data
Summary
Tabl
Discrete Random Variables Solutions
STAT-UB.0103 Statistics for Business Control and Regression Models
Discrete random variables: PDF & Expectation
1. Consider the following game:
1. You pay $6 to ip a coin.
2. If the coin lands heads, you get $10; otherw
Chapter 8
Inferences Based on Two
Samples: Confidence Intervals
and Tests of Hypothesis
Content
1. Target Parameter: difference between two
population means
2. Comparing Two Population Means:
Independent Sampling
3. Comparing Two Population Means: Paired
Statistics for Business Control,
Regression and Forecasting Models
Instructor: Xi Chen
1
Course Information
Instructor: Xi Chen (xichen@nyu.edu )
Office: KMC 8-50
Office Hours: Tue, Thu: 11:00 AM to 12:00 AM
Teaching Fellow: Sen Tian (stian@stern.nyu.
The Central Limit Theorem Solutions
STAT-UB.0103 Statistics for Business Control and Regression Models
The Central Limit Theorem
1. You draw a random sample of size n = 16 from a population with mean = 100 and standard
deviation = 20. From this, you compu
Sampling Distributions
Content
1. The Concept of a Sampling Distribution
2. Properties of Sampling Distributions:
Unbiasedness
3. The Sample Distribution of the Sample
Mean and the Central Limit Theorem
Learning Objectives
Establish that a sample statist
STAT 226 F - HANDOUT 5
SAMPLING DISTRIBUTION AND CENTRAL LIMIT THEOREM
1. Biscuits. University of Louisville researchers J. Usher, S. Alexander, and D. Duggins examined
the process of ﬁlling plastic pouches of dry blended biscuit mix (Quality Engineering,
The Normal Distribution
Statistics for Business Fall 2016
1
The Normal Distribution
Very important distribution in statistical theory
Many phenomena around us and in nature are
modeled by a normal distribution
Quality characteristics of finished product
The Normal Distribution
Statistics for Business Fall 2016
1
The Normal Distribution
Very important distribution in statistical theory
Many phenomena around us and in nature are
modeled by a normal distribution
Quality characteristics of finished product
Jack Lorge
April 8, 2013
Statistics for Business
Professor Giloni
Homework 8 Answer Page
1. HO: Parameter = 863, HA: Parameter < 863
2. a) HO: Parameter = 25,000, HA: Parameter not equal to 25,000
b) Mean = 25,477, Standard deviation = 2430
c) Z = 2.48
d)
Stats Module 2
April 18, 2013
John Lorge (jpl412)
Robert Allen (rwa230)
Module 2
1. The Normality Test Total Lifetime Gross Revenue shows a high density of values
beginning around 200,000,000 that have a strongly increasing, positive slope. Around the
600
StatsModule3
JohnLorge(jpl412)
RobertAllen(rwa230)
May13,2013
Module 3
1. Interpretation of slope coefficient:
For LogGross vs. LogOpening CoEff = .644, P Value = 0.00
Because the slope is .644, it is statistically significant as it varies from zero and c
Jack Lorge
March 31, 2013
Statistics for Business
Problem Set 7
1. Sincich, Ex. 5.1
a) 1.645
b) 2.575
c) 1.96
d) 1.2816
2. Sincich, Ex. 5.4
a) 25.9 +/- 1.96 (2.7 / sqrt 90)
b) 25.9 +/- 1.645 (2.7 / sqrt 90)
c) 25.9 +/- 2.575 (2.7 / sqrt 90)
3. Sincich, Ex
John P. Lorge
jpl412
HW5
Answers Page
1. A) 1.645
B) 1.96
C) -1.96
D) 1.28
E) 1.28
2. A) 1
B) -1
C) 0
D) -2.5
E) 3
3. A) 53
B) 55.88
C) 45.065
D) 53.3
E) 46.16
F) 56.98
4. A) .4107
B) .1508
C) .9066
D) .0162
5. A) .5, .5
B) $8
C) $28
6. Not a normal distr
John Lorge
April 5, 2013
Statistics for Business
HW 10 Answers
Question 1
A. I would expect score to be positively related to age; higher age = higher score.
B. The graph does not show the relationship I expected. In fact, it suggests more of a
negative r
Jack Lorge
Statistics
Giloni
Answer Sheet 6
1. a) Yes, it is reasonable to approximate the probability distribution of x with a normal
distribution; u +/- 3 all lie between 0 and 25.
b) The mean is 10 and the variance is 2.4495.
c) Exact value of P(X>9) =
Jack Lorge
Statistics PS 4
Solutions page
1. A) Mean = 34.5 Variance = 174.75 Standard Deviation = 13.219
B) Graph on second sheet.
C) 100 per cent probability of falling within +/- 2
2. A) x p(x) for firm A = 2450, for firm B = 2450; verified.
B) Firm A
2*)
i i 0.3).(9.<D.? I: 3!. may“;
Y = add l3= Whack 5mm 9mm [wan/f
‘ 3: W :I
_’<_ ﬁr)
7‘ ' 9- '/56 0 I In [BIZ
S \ 3 2/36 I l lllll t
4% Ill Li 3/56 7— I (II\
s nu 5 4/24 2 l m.
6 w a, s 34, 4f l u 4
7 1m M 7 é/Sc, s u 5
2 W 8 5/36 a;
0! HH 9 4/36 _3_ mm)