PEDESTRIAN CROSSING SPEED MODEL USING MULTIPLE REGRESSION ANALYSIS Mako C. DIZON Undergraduate Student Department of Civil Engineering Polytechnic University of the Philippines 13 Bayabas St.Anthony Taytay, Rizal 1920 Email: makolet10@yahoo.com Lyvan G. DE PEDRO Undergraduate Student Department of Civil Engineering Polytechnic University of the Philippines Mandaluyong City Dr. Manuel M. MUHI Faculty Department of Civil Engineering Polytechnic University of the Philippines Sta. Mesa, Manila Email: manuel_muhi@yahoo.com Abstract: Pedestrian signal allocates the appropriate time for pedestrian to cross safely a place where vehicle and pedestrian conflict are great. This study intends to formulate a model that will be used in determining the pedestrian speed using the available data that can be found in an actual situation in a signalized crosswalk. Four crosswalks will be observed to gather necessary data to come up with an appropriate model that can be used in other studies with regards to pedestrians. SpeedDensity relationship will also be observed to find out whether the considered crosswalk adheres to the basic concept of SpeedDensity relationship that the Highway Capacity Manual stated. These data includes: pedestrian volume, pedestrian crossing time, crosswalk dimension and actual crossing cycle time. After a thorough research and experimentation, the researcher found out several factors that may affect the speed of the pedestrians and the researchers came to conclusion that it is not merely by density. The dimension of the crosswalk affect the speed in terms that when the length of the crosswalk is lengthened; the tendency is to increase their speed as not to be caught up by the movement of traffic. In the course of our research, we conclude that the presented variables are not enough to explain the variation on speed. The researchers had formulated a model to predict the pedestrian signal but due to its low coefficient of determination, the researchers...
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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 worldwide as of February 2, 2013. (Business Wire, 2014)
Nordstrom, Inc. offers on online store, ecommerce, retail stores, mobile commerce and catalogs to its consumers. It operates 117 fullline 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 ecommerce...
...Project: MultipleRegressionModel
Introduction
Today’s stock market offers as many opportunities for investors to raise money as jeopardies to lose it because market depends on different factors, such as overall observed country’s performance, foreign countries’ performance, and unexpected events. One of the most important stock market indexes is Standard & Poor's 500 (S&P 500) as it comprises the 500 largest American companies across various industries and sectors. Many people put their money into the market to get return on investment. Investors ask themselves questions like how to make money on the stock market and is there a way to predict in some degree how the stock market will behave? There are lots and lots of variables involved in how the stock market behaves at a specific time. The stock market is in a way an information agency. Based on new information, whether good or bad regarding almost everything from political issues to interest rates and inflation, the stock market can go up or down. The market is anticipating economic occurrences proactively, ignoring already occurred events that were predicted before. This way it is very hard to predict how it is going to move in the future. As S&P 500 is considered to be the most reliable benchmark for the overall U.S. stock market, we decided to study what factor has the most impact on it. We created two regressionmodels and...
...MULTIPLEREGRESSIONANALYSISUSING DUMMY VARIABLE
HDI RegressionUsing Health, Education &Income
3/21/2012
Department Of Business Economics
Jasmine Kaur(598)
Kshama (577)
Maanya Kaushik
ShikhaChaurasia(600)
ABSTRACT
In this project we have employed tools of empirical econometric analysis to examine the relationship between the Human Development Index and the indicators of Human Development.
Table of contents
Topics  Page no: 
1.Abstract  (i) 
2. Literature Review  
3. Theory 3.1 Data 3.2 Dummy Variable 3.3 Regression3.4 Interpretation  
4. Hypothesis testing  
LITERATURE REVIEW
Human development plays a fundamental role and remains the most important factor in Economic growth and development in countries of the world. The Human Development Index (HDI), first introduced in the 1990 Human Development Report (UNDP: 1990), was in response to the need for a measure that could better represent human achievements in several basic capabilities. This a composite statistic used to rank countries by level of “human development” and to separate countries into developed (high development), developing (middle development), and underdevelopment (low development) categories. The statistic is computed using data on Life Expectancy, Education and Per Capita GDP, each as...
...we will review nonlinearity 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...
...Park University
MultipleRegressionAnalysis
Pamela Lima
EC315 Quantitative Research Methods
Dr. Bell
11/22/2013
MultipleRegressionAnalysis
Miami Heat Average Attendance per season
Miami Heat History
The Miami Heat is a professional basketball team, based in Miami, Florida. The team is a member of the Southwest Division in the Eastern Conference of the National Basketball Association (NBA). The Miami Arena was the home of the Heat until the end of the 1999.
The American Airlines Arena, located in Downtown Miami is the new home of the Heat since January 2nd, 2000. Micky Arison is the team owner; he also owns the giant Carnival cruise lines. Pat Riley is the team president and general manager, and Erik Spoelstra is the head coach. Their mascot is Burnie, an anthropomorphic fireball.
The Miami Heat team was formed in 1988; the Heat has won three league championships so far (2006, 2012, and 2013). They also added four conference titles, and 10 division titles. With 27 wins in a row between February 3 to March 27, 2013 the Heat hold the secondlongest streak in NBA history. In 2013, Forbes Magazine valued the Heat at $625 million.
Leading the recent winning tradition that the Heat had achieved are LeBron Raymone James, and Dwayne Wade. Together they are responsible for most part of the points scored by the Heat. “King James”, as he is called by the Miami Heat...
...MultipleRegressionAnalysis of exchange rate with the determinant factors
RegressionAnalysis: USD versus GDP Growth, FER, FDI Growth, Interest Rate, Money Supply, Terms Of Trade
The regression equation is
USD = 41.5  1.95 GDP Growth + 0.000943 FER  0.139 FDI Growth + 0.048 Differential Interest Rate + 0.000067 Money Supply + 0.166 Terms of Trade  0.000097 External Debt 
Predictor T PConstant 2.32 0.039GDPGrowth 3.43 0.005 FER 1.01 0.332FDIGrowth 1.55 0.146Differential Int Rate 0.11 0.913Money Supply 0.89 0.393Terms of Trade 0.35 0.731External Debt 0.73 0.479 
Where,

T is t stat. Tstat is a measure of the relative strength of prediction (is more reliable than the regression coefficient because it takes into account error). 
The pvalue is a percentage. It tells you how likely it is that the coefficient for that independent variable emerged by chance and does not describe a real relationship.
A pvalue of .05 means that there is a 5% chance that the relationship emerged randomly and a 95% chance that...
...Multipleregression: OLS method
(Mostly from Maddala)
The Ordinary Least Squares method of estimation can easily be extended to models involving two or more explanatory variables, though the algebra becomes progressively more complex. In fact, when dealing with the general regression problem with a large number of variables, we use matrix algebra, but that is beyond the scope of this course.
We illustrate the case of two explanatory variables, X1 and X2, with Y the dependant variable. We therefore have a model
Yi = α + 1X1i + 2X2i + ui
Where ui~N(0,σ2).
We look for estimators so as to minimise the sum of squared errors,
S =
Differentiating, and setting the partial differentials to zero we get
=0 (1)
=0 (2)
=0 (3)
These three equations are called the “normal equations”. They can be simplified as follows: Equation (1) can be written as
or
(4)
Where the bar over Y, X1 and X2 indicates sample mean. Equation (3) can be written as
Substituting in the value of from (4), we get
(5)
A similar equation results from (3) and (4). We can simplify this equation using the following notation. Let us define:
Equation (5) can then be written
S1Y = (6)
Similarly, equation (3) becomes
S2Y = (7)
We can solve these two equations to get:
and
Where =S11S22 – S122. We may therefore...
...Topic 8: MultipleRegression Answer
a.
Scatterplot
120 Game Attendance 100 80 60 40 20 0 0 5,000 10,000 15,000 20,000 25,000 Team Win/Loss %
There appears to be a positive linear relationship between team win/loss percentage and
game attendance. There appears to be a positive linear relationship between opponent win/loss percentage and game attendance.
There appears to be a positive linear relationship between games played and game
attendance. There does not appear to be any relationship between temperature and game attendance.
b. Game Attendance Game Attendance Team Win/Loss % Opponent Win/Loss % Games Played Temperature Team Win/Loss % Opponent Win/Loss % Games Played Temperature
1 0.848748849 1 0.414250332 0.286749997 1 0.599214835 0.577958172 0.403593506 1 0.476186226 0.330096097 0.446949168 0.550083219
1
No alpha level was specified. Students will select their own. We have selected .05. Critical t = + 2.1448 t for game attendance and team win/loss % = 0.8487/ (1 − 0.84872) /(16 − 2) = 6.0043 t for game attendance and opponent win/loss % = 0.4143/ (1 − 0.41432) /(16 − 2) = 1.7032 t for game attendance and games played = 0.5992/ (1 − 0.59922) /(16 − 2) = 2.8004 t for game attendance and temperature = 0.4762/ (1 − ( − 0.4762 ) ) /(16 − 2) = 2.0263 There is a significant relationship between game attendance and team win/loss % and games played. Therefore a multipleregression...
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