Chapter 15
Raising Capital
Multiple Choice Questions
1. Jones & Co. is funded by a group of individual investors for the sole
purpose of providing funding for individuals who are trying to convert
their new ideas into viable products. What is this type of
Chapter 03
Working with Financial Statements
Multiple Choice Questions
1. Activities of a firm which require the spending of cash are known as:
A. sources of
cash.
B. uses of
cash.
C. cash
collections.
D. cash
receipts.
E. cash on
hand.
2. The sources and
Chapter 07
Interest Rates and Bond Valuation
Multiple Choice Questions
1. Mary just purchased a bond which pays $60 a year in interest. What is
this $60 called?
A. coupo
n
B. face
value
C. discou
nt
D. call
premium
E. yiel
d
2. Bert owns a bond that will
Chapter 08
Stock Valuation
Multiple Choice Questions
1. What is the model called that determines the present value of a stock
based on its next annual dividend, the dividend growth rate, and the
applicable discount rate?
A. zero
growth
B. dividend
growth
Chapter 05
Introduction to Valuation: The Time Value of Money
Multiple Choice Questions
1. You are investing $100 today in a savings account at your local bank.
Which one of the following terms refers to the value of this investment
one year from now?
A.
Allen Dengler Example of Academic Work
To determine what my class would consider a car versus what was a truck crossing the
intersection of Spring Street and Route 27, we conducted an open class discussion. A truck was
simply decided to be any vehicle tha
Summa r y f or Ca r s
A ndersonD arling N orm alit y T est
A Squared
PV alue <
M ean
St D ev
V ariance
Sk ew ness
Kurt osis
N
30
60
90
120
54. 455
25. 363
643. 262
1. 46317
3. 53570
88
M inim um
1st Q uart ile
M edian
3rd Q uart ile
M axim um
150
1. 61
0.
Danielle Paulson
Section L
DSC PROJECT II
Analysis:
I used Minitab to analyze the data taken from our class sections. Four separate box plots
(Morning-Car, Morning-Truck, Afternoon-Car, Afternoon-Truck) were used to find outliers.
They are as follows:
Sec
CAR MORN
K MORN
11:00
11:00
10:05
10:42
9:15
118
81
60
64
161
16 R
35 F
67 T
34 M
71 M
MA
HH
JB
LN
WW
E
E
L
L
L
CAR AFTERNOON
4:19
104
15 F
AD
F
1:15
4:20
116
126
30 F
3F
DP
DT
L
L
37 F
OC
E
TRUCK AFTERNOON
3:05
93
Time
Cars
Truck
Day
Initial
Section
9:15
MEMORANDUM
TO: Mr. Fred Ahrens
FROM: Danielle Paulson
DATE: February 13, 2012
SUBJECT: Gathering Data
I was able to distinguish between a car and a truck while collecting data because of my
definitions of the two. A truck was any vehicle with a bed or any
Chapters 2-3
1.
(b) 150, 160, 100, 120, 120, 140, 90, 170, 190 (Round your mean value to 1 decimal place.)
N (Population)
MEAN
MEDIAN
MODE
2.
Lauren is a college sophomore majoring in business. This semester Lauren is taking courses in
accounting, economi
Chapters 1-2
1.
Explain the difference between a census and a sample.
Census
Sample
: examine all of the population units.
: subset of the units in a population.
2.
A bank manager has developed a new system to reduce the time customers spend waiting to be
Name:_
Test 4
1) What information could be give about the slope the y-intercept and correlation between
the two variables? What is the residual for the smallest point on the graph?
2) Does this plot indicate a good
fit? Why or why not?
3) An analysis of v
Lecture on 12/03/10
Prediction Intervals for Growth Curve Models
Handling First Order Autocorrelation
Growth Curve Model
()
t
yt = 0 1 t
Using logarithms we can convert this to a model
linear in its parameters.
ln(y t ) = ln(B 0 B )
= ln(B o ) + tln(B1 )
Lecture on 12/01/10
Growth Curve Models
Model Linear in its Parameters
A model is linear in its parameters if each term
(excluding error term and dependent variable)
includes one and only one parameter that is raised
to the first power.
Examples:
1. Simpl
Lecture on 11/29/10
Modeling Increasing Seasonal Variation
Seasonal Variation
Two types:
1. Constant
Magnitude of the seasonal swing does not
depend on the level of the time series
2. Increasing
Magnitude of the seasonal swing increases
with the level o
Lecture on 11/22/10
Modeling Additive Seasonal Variation
Time Series Definition
A time series is a set of observations/measurements
collected over a certain number of time periods.
Seasonal Variation
Two types:
1. Constant
Magnitude of the seasonal swing
Lecture on 11/19/10
Application of the Durbin Watson Statistic
Modeling Seasonal Variation
Time Series Definition
A time series is a set of observations/measurements
collected over a certain number of time periods.
Trend Models
These models represent the
Lecture on 11/17/10
Introduction to Time Series Regression
Detecting Autocorrelation
Time Series Definition
A time series is a set of observations/measurements
collected over a certain number of time periods.
Trend Models
These models represent the time s
Lecture on 11/15/10
Detecting Outlying and Influential Observations
Detecting Outliers and Influential Observations
Outlier - Observation well separated from rest of data.
Influential Observation- Observation that causes least square
point estimates to be
Lecture on 11/10/10
Variable Selection Methods
Stepwise Regression
Max - R
Residual Analysis for Multiple Regression
Models
C Statistic
SSE
C = 2 [ n 2( k + 1)]
sp
p = Total number of possible independent variables
SSE = unexplained variation of the k-var
Lecture on 11/08/10
Handling Multicollinearity in Quadratic
Models
Methods for Evaluating Alternative Models
Variable Selection Methods
Multicollinearity in Quadratic Models
Y = B0+B1x + B2x2 +
Often x and x2 are highly correlated resulting
in muticoll
Lecture on 11/03/10
Methods for Evaluating Alternative Models
Comparing Models on the Basis of Adjusted
R2, s, and Prediction Interval Length
k n 1
2
R = R
n 1 n ( k + 1)
2
s=
SSE
n - (k + 1)
y t /2 s 1 + distance value
Adjusted R2, s, and 95% predicti
Lecture on 11/01/10
Assessing Multicollinearity
Multicollinearity
A condition that exists when two or more
independent variables are correlated
Regression Equation: Chill =33.0823-.4595*TEMP
R-Square = .5158
20
C
Correlation between
H
I
L
10
CHILL index a
Lecture on 10/29/10
Conclusion of Electronic World Example
The Partial F-Test
Example 4.15
Quantitative Variables
y= Sales volume in July ($1000s)
x =Number of households in stores area (1000s)
Qualitative Variable
Type of location - street, shopping mall
Lecture on 10/27/10
Regression Models Containing Qualitative
Independent Variables (cont)
Example 4.15 - Model with one qualitative
variable having more than two levels
Quantitative Variables
y= Sales volume in July ($1000s)
x =Number of households in sto
Lecture on 10/25/10
Matrix Representation of Regression Models
Containing Interaction Terms
Regression Models Containing Qualitative
Independent Variables.
X Matrix for Problem 4.15
Data is in Table 4.10 on page 197.
Regression Model:
y = 0 + 1 x 1 + 2 x