2. (30%) Trip Generation Model 下列為針對淡水區進行的旅次產生調查，共分為 6 個交通分區(traffic zone)： Zone Trip production Car ownership 1 650 250 2 450 190 3 950 715 4 850 625 5 750 290 6 290 135

(1) 試建立一線性迴歸函數(linear regression model)，進行參數校估，列出校估後之函 數，並計算模式之 R2、透過 t 檢定(t-test)檢驗顯著性。 (2) 試建立一對數線性迴歸函數(log-linear regression model) (即乘冪迴歸模式) 進行參 ， 2 數校估，列出校估後之函數，並計算模式之 R 、透過 t 檢定(t-test)檢驗顯著性。 (3) 比較上述兩個模式之差異，討論孰優孰劣。

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3. (30%) Mode Choice Model 考慮旅運者對三種運具的(負)效用函數： COSTk U k ak 0.3OVTk 0.15IVTk 0.02

13 WAGE b AUTOS
k

where k: drive-alone, shared-ride, transit for k=1,2,3, respectively OVTk : out of vehicle time in minutes for mode k IVTk : in-vehicle time in minutes for mode k COSTk : out of pocket travel cost in cents for mode k WAGE: wage rate in cents/min AUTOS: auto ownership ak : 0.0, +3.0, +0.3 for k=1,2,3, respectively bk : -0.03, +0.03, +0.05 for k=1,2,3, respectively (a) 試討論效用函數中各係數符號與係數值大小所代表的意義 (彈性、時間價值)。 (b) 試由效用函數中係數值，推估需求彈性？ (c) 試以下列資料，應用 Logit Model 估算各社經群組(groups 1, 2, 3)之 各運具分配(modal split)的機率為何？ 並討論運具分配結果。 e U m Pm e U k all k

Mode characteristics k=1 (drive-alone) k=2 (shared-ride) k=3 (transit) Socio-economic group Group 1 (white-collar) Group 2 (blue-collar) Group 3 (captive)

...
Unit 5 – RegressionAnalysis
Mikeja R. Cherry
American InterContinental University
Abstract
In this brief, I will demonstrate selected perceptions of the company Nordstrom, Inc., a retailer that specializes in fashion apparel with over 12 million dollars in sales last year. I will research, review, and analyze perceptions of the company, create graphs to show qualitative and quantitative analysis, and provide a summary of my findings.
Introduction
Nordstrom, Inc. is a retailer that specializes in fashion apparel for men, women and kids that was founded in 1901. The company is headquartered in Seattle, Washington with over 61,000 employees world-wide as of February 2, 2013. (Business Wire, 2014)
Nordstrom, Inc. offers on online store, e-commerce, retail stores, mobile commerce and catalogs to its consumers. It operates 117 full-line stores within the United States and 1 store in Canada, 167 Nordstrom Rack stores, 1 clearance store under the Last Chance Banner, 1 philanthropic treasure & bond store called Trunk Club and 2 Jeffrey boutiques. The option of shopping online is also available at www.nordstrom.com along with an online private sale subsidiary Hautelook. They have warehouses, also called fulfillment centers, which manages majority of their shipping needs that are located in Cedar Rapids, Iowa. (Business Source Premier, 2014)
Nordstrom, Inc. continues to make investments in their e-commerce...

...we will review non-linearity and model transformations covered in lectures 6 and 7.
Question 1: Logarithms
(i) The interpretation of the slope coefficient in the model Yi = β0 + β1 ln(Xi ) + ui is as follows:
(a) a 1% change in X is associated with a β1 % change in Y.
(b) a 1% change in X is associated with a change in Y of 0.01 β1 .
(c) a change in X by one unit is associated with a β1 100% change in Y.
(d) a change in X by one unit is associated with a β1 change in Y.
(ii) The interpretation of the slope coefficient in the model ln(Yi ) = β0 + β1 Xi + ui is as follows:
(a) a 1% change in X is associated with a β1 % change in Y.
(b) a change in X by one unit is associated with a 100β1 % change in Y.
(c) a 1% change in X is associated with a change in Y of 0.01β1 .
(d) a change in X by one unit is associated with a β1 change in Y.
(iii) The interpretation of the slope coefficient in the model ln(Yi ) = β0 + β1 ln(Xi ) + ui is as
follows:
(a) a 1% change in X is associated with a β1 % change in Y.
(b) a change in X by one unit is associated with a β1 change in Y.
(c) a change in X by one unit is associated with a 100β1 % change in Y.
(d) a 1% change in X is associated with a change in Y of 0.01β1 .
(iv) To decide whether Yi = β0 + β1 X + ui or ln(Yi ) = β0 + β1 X + ui fits the data better, you
cannot consult the regression R2 because
(a) ln(Y) may be negative for 0 < Y < 1.
(b) the TSS are not measured...

...lineaire regressiemodel wordt er een model gecreëerd. Dit model bevat een onafhankelijke variabele (X) en een afhankelijke variabele (Y), het Monte Carlosimulatiemodel wordt hierop toegepast (Dougherty, 2002, p.72).
Met Monte Carlosimulatie als toepassing wordt als eerste voor het lineaire regressiemodel willekeurig de waarden voor α en β gekozen. Vervolgens wordt met EViews 5.0 voor een vastgesteld aantal waarnemingen, hier uitgaande van 1000 waarnemingen per simulatie (T=1000), waarden getrokken voor de onafhankelijke X –variabele en de storingsterm. Hierna wordt op basis hiervan de waarden voor de afhankelijke variabele Y bepaald. Op de waarnemingen die voortkomen uit deze verschillende modellen, dus de variabelen X en Y, zal regressie uitgevoerd worden. Om te concluderen wat de invloed is, zal er uiteindelijk een Breusch-Godfrey Serial Correlation LM Test gebruikt worden. Dit hele proces wordt een aantal keer herhaald.
2.3 Hypothese
Voor beantwoording van de centrale vraag: in hoeverre heeft eerste orde autocorrelatie invloed op het lineaire regressiemodel, moeten er hypothesen gesteld worden. Het stellen van hypotheses gaat vooraf aan het proces van het ontwikkelen en toepassen van het gecreëerde Monte Carlosimulatiemodel.
Er zijn twee hypotheses: H0 en H1. H0 gaat ervan uit dat er geen autocorrelatie is. H1 gaat van het tegenovergestelde uit. H1 stelt dat er wel autocorrelatie is en dat het wel degelijk invloed heeft op het...

...1.
Qeach brand t=β0+β1*PMinute Maid t+β2*PTropicana t+β3*PPrivate label t+ueach brand t
Q: quantity P: price
By running the above regressionmodel for each brand, we got the following elasticity matrix and the figures for “V” and “C.” Note that we used the average price and quantity for P and Q to calculate each brand’s elasticity.
Price Elasticity | Tropicana | Minute Maid | Private Label |
Tropicana | -3.4620441 | 0.40596537 | 0.392997566 |
Minute Maid | 1.8023329 | -4.26820251 | 0.765331803 |
Private Label | 1.3138871 | 1.41197064 | -4.130754362 |
VTropicana = 0.40596537+0.392997566 = 0.7989629
CTropicana = 1.8023329+ 1.3138871 = 3.11621998
VMinute Maid = 1.8023329+0.765331803 = 2.5676647
CMinute Maid = 0.40596537+1.41197064 = 1.81793601
VPrivate Label = 1.3138871+1.41197064 = 2.7258577
CPrivate Label = 0.392997566+0.765331803 = 1.15832937
“V” suggests the vulnerability of each brand to the price changes of other two brands. On the other hand, “C” suggests the clout of each brand to the other brands. For example, the brand that has the highest vulnerability is private label (2.73), which means private label is most vulnerable to the other two brands’ price changes. If Tropicana and private label each depreciate their price by 1%, then the sales of private label will decrease by 2.73%. In contrast, the brand that has the highest clout is Tropicana (3.12), which means Tropicana is the most influential...

...Introduction
This presentation on RegressionAnalysis will relate to a simple regressionmodel. Initially, the regressionmodel and the regression equation will be explored. As well, there will be a brief look into estimated regression equation. This case study that will be used involves a large Chinese Food restaurant chain.
Business Case
In this instance, the restaurant chain's management wants to determine the best locations in which to expand their restaurant business. So far the most successful locations have been near college campuses. This opinion is based on the positive numbers that quarterly sales (y) reflect and the size of the student population (x). Management's mindset is that over all, the restaurants that are within close proximity to college campuses with large student bodies generate more sales than restaurants located near campuses with small student bodies.
In the sample box below, xi is the size of the student population (in thousands) and yi is the quarterly sale (in thousands of dollars). The value for xi and yi for all of the 10 Chinese Food restaurants given in the sample are reflected as follows:
Sample Data:
(measured in 1,000s) (measured in $1,000s)
Restaurant Student Population Quarterly Sales
(i) (xi) (yi)
1 2 58
2 6 105
3 8 88
4 8 118
5 12 117
6 16 137
7 20 157
8 20 169
9 22 149
10 26 202...

...RegressionAnalysis Exercises
1- A farmer wanted to find the relationship between the amount of fertilizer used and the yield of corn. He selected seven acres of his land on which he used different amounts of fertilizer to grow corn. The following table gives the amount (in pounds) of fertilizer used and the yield (in bushels) of corn for each of the seven acres.
|Fertilizer Used |Yield of Corn |
|120 |138 |
|80 |112 |
|100 |129 |
|70 |96 |
|88 |119 |
|75 |104 |
|110 |134 |
a. With the amount of fertilizer used as an independent variable and yield of corn as a...

...Analysis on Inflation RegressionModel
Done by: Hassan Kanaan & Fahim Melki
Presented to: Dr. Gretta Saab
Due on: Tuesday, January 25, 2011
Outline:
I. Introduction
A. Definition of Variables
B. Type of Variables
II. Background and Literature Review
A. Inflation and Unemployment
B. Inflation and Oil Prices
C. Inflation and GDP
D. Inflation and Money Supply
III. Analysis
A. SPSS 17analysis
B. E-Views 5 analysis
IV. Conclusion and Recommendation
V. Indexes
A. SPSS17 results Enter and Stepwise (Index 1)
B. E-Views 5 results Stationarity and Granger Causality (Index 2)
C. Data Collection (Index 3)
The project that the group will be handling is about Inflation and how can these four variables affect it. The variables are GDP, Unemployment, Money Supply (M2), and Oil Prices.
First, a definition on Inflation; inflation is the overall general upward price movement of goods and services in an economy (often caused by an increase in the supply of money); usually as measured by the Consumer Price Index and the Producer Price Index. In the project the group will analyze how the above mentioned variables are going to affect inflation. (Investopedia)
An overview on why these variables where chosen and what are these variables. The first variable GDP (Gross Domestic Product), the total market value of all final goods and...

...Airlines, a commuter firm serving the Boston hub, are shown for the past 12 weeks:
|Week |1 |2 |3 |4 |5 |6 |
|Demand |17 |19 |15 |21 |20 |23 |
Problem 7 [6]
A careful analysis of the cost of operating an automobile was conducted by a firm. The following model was developed:
Y = 4,000 + 0.20X
where Y is the annual cost and X is the miles driven.
a) If the car is driven 15,000 miles this year, what is the forecasted cost of operating this automobile? [3]
b) If the car is driven 25,000 miles this year, what is the forecasted cost of operating this automobile? [3]
Problem 8[12]
A study to determine the correlation between bank deposits and consumer price indices in Birmingham, Alabama, revealed the following (which was based on n = 5 years of data):
(x = 15, (x2 = 55, (xy = 70, (y = 20 and (y2 = 130
a) What is the equation of the least square regression line? [5]
b) Find the coefficient of correlation. What does it imply to you? [4]
c) What is the standard error of the estimate? [3]
Problem 9 [8]
Given the following data, use least squares regression to develop a relation between the number of rainy summer days and the number of games lost by the Boca Raton Cardinal base ball team.
Year 1994 1995 1996 1997...