SUMMARY OUTPUT
Pown Residual Plot
Re s id ua ls
Regression Statistics
Multiple R 0.8985686
R Square 0.8074256
Adjusted R 0.7953897
Square
Standard Error
30.162842
Observations
18
100
0
$
 100 500
$ 600
$ 700
$ 800
$ 900
$ 1,000
Pown
ANOVA
df
Regression
R
I have used this as part of my lecture. When used this way, the NOTES
worksheet provide lecture notes for covering this in class using computer
projection onto a screen.
If students print the NOTES worksheet, they can use the detailed outline
presented th
It may help to print this 2 page set of questions. This lab uses the data from the last lab as the
starting point but asks a different series of questions. These questions require the use of some
paper calculations as well as just working on the computer,
The data on the data sheet provides variable cost estimates for two plants A and B for a
variety of output levels.
These plants differ in one important aspect: the amount of capital involved in each plant.
Suppose plant A has a capital cost of $2,000 per
points for this task Parts of the problem.
Answer by filling in the yellow cells below. Do not worry about formatting your sentences.
Your score Maximum
Setup
Tasks/Questions
NOTE: All rank orderings are from least to most expensive. You can use the lette
This data was used to introduce the notion of dummy variables. It is also useful to test your
knowledge of serial correlation. (D3:E20 are not write protected, you can change them as you wish.)
Canadian Bond Sales (19331949)
Year
1933
1934
1935
1936
1937
Working with seasonal indicies
The discussion of calculating seasonal indicies in the text focused on additive rather than multiplicative
indicies. Either can be calculated from time series data. If the dependent variable is Y then an additive form
is ass
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.3344387
R Square
0.1118493
Adjusted R Square
0.0925416
Standard Error
64.174398
Observations
48
ANOVA
df
Regression
Residual
Total
SS
MS
F
Significance F
1 23857.66 23857.66 5.7930095 0.0201617
46 189444.2
To see comments, place the mouse's white cross near a red triangle. Start in cell A2.
How can we see Serial Correlation from an Error Plot?
A= 0.00
Durbin
Watson
Autocorrelation=
i.i.d.
time
random
variables
t
0.3002
1.2777
0.2443
1.2765
1.1984
1.7331

This file can be used as a lecture supplement prior to the first regression lab assignment. This file
introduces multivariate regression using the Miller Pharmaceutical dataset discussed in the text*.
The object of univariate analysis is to provide a best
Monopolistic Competition
Many markets are monopolistically competitive. The defining features of a
monopolistically competitive market are:
1. The product in question is differentiated, meaning that different producers
produce different versions of the pr
Examining how firms adjust to changes in demand in competitive markets
In the last tutorial, we examined the firms cost curves. The perunit curves developed
there: marginal cost, MC; average variable cost, AVC; and average total cost, ATC, are
useful for
Examining Elasticity of Demand using Linear Demand
Inverse Demand) P(Q) = a  bQ
a= $100 P Intercept (Maximum price)
b=
2 2 Slope
50
Q
P
25.0
Percent decline from
2 maximum price (slide control)
$50.00
1.00
(Q,P) point associated with
the percent decline
Copyright 2004, Stephen E. Erfle. All rights reserved.
Ch09: Page 1
Part III: Theory of the Firm
Part III examines the theory of the firm and Part IV puts together consumers and f
irms to examine market interaction. We typically assume that firms wish to
Copyright 2004, Stephen E. Erfle. All rights reserved.
Ch11: Page 1
Chapter 11: Cost Curves
The cost analysis in Chapter 10 links the cost of factors of production with how
they are combined to produce goods. Cost minimization is based on a comparison of
Diagrams to the right are smaller versions of ones below
LTC
show SRTC(Q;Ki)
LTCdisc
Total costs STC(Q;K10)
$800
160
with Ki = K2 $
LTC
STC(Q;K16)
371.164
minLAC
LTCdiscrete
K4
K10
K16 $
STC(Q;K2)
57.6
minLAC
LTCdisc
STC(Q;K10)
LTC
S TC(Q;K16)
371.164
Tot
This lab provides a general introduction to working with statistical data. You should be aware
before you start that this lab is openended in nature and it may take more than an hour to complete. It
is also worth knowing that some of the manipulations yo
Figure 15.1
Profit maximizing output in a monopoly market
TR, TC,
TC
$1,500
Total
$1,000
TR
$500
$0
0
5
10
15
20
25
X
$500
$120
MC
ATC
Per unit
$100
$80
$60
b
$40
D
a
$20
MR
$0
0
5
10
15
20
25
X
x
MR=MC ATC Price Profits
15.00 $25.00 $45.00 $62.50
Copyr
0.99324998 Correlation between PB&J
To see comments, place the mouse's white cross near a red triangle. Start in cell A2.
Predicting demand for bread as a function of peanut butter and jelly consumption.
Underlying model is PB=J and for each sandwich you
Introduction to Graphical Displays using Excel
It may help to print this sheet.
Excel is able to visually display data in a wide variety of fashions using the Chart Wizard button on the Standard Toolbar. The Chart Wizard
has 14 chart types available on th
Figure 16.1
Calculating consumer surplus for a discrete good
Price per cup
$6
$2.50
$5
Excess benefit on first cup.
$4
$1.04
Excess benefit on second cup.
$0.39
$3
Excess benefit on third cup.
$0.00 Excess benefit on fourth cup.
P = $2.50
$2
$1
$0
0
5
10
Copyright 2004, Stephen E. Erfle. All rights reserved.
Ch16: Page 1
Chapter 16. Welfare economics
We now turn to the issue of measuring the benefits and costs associated with
various market outcomes. Welfare economics involves measuring the net benefits
i
Profit Maximization in the Long Run
Rick Rentaheap owns a small car rental agency. His long run total cost function given by TC(Q) = 36 + 6*Q + Q^2. The car rental business is perfectly competitive and the public's willingness to pay for car rentals (the