In order to explain the effect that winnings percentage has on attendance, I have created an adjusted economic model that I have specified above. In order to test my economic model, I have compiled data for each of the variables specified in the model from the years 2003 to 2005. The question that I will be answering in my regression analysis is whether or not wins have an affect on attendance in Major League Baseball (MLB). I want to know whether or not wins and other variables associated with attendance have a positive impact on a team's record. The y variable in my analysis is going to be attendance for each baseball team. I collected the data for each team's average attendance for 2003-2005 from an internet site entitled www.baseballreference.com. The summary statistics for this variable show that the mean winning percentage for all MLB teams is 50.4 percent with a standard deviation of 7.6 percent. There is a minimum and maximum of 27 percent and 65 percent respectively. I am taking the log of attendance in order to explain relationships with the independent variables in the form of percent changes in our dependent variable. The main independent variable that I am going to be looking at, as stated above is winning percentage, and its effects on a team's attendance. I feel that winning percentage is positively related to attendance as a team with a higher winning percentage will be more likely to attract fans than a team with a low winning percentage. Fans want to see their teams perform well during the season and are therefore more likely to attend games when their team does so. I obtained my data for attendance from a site entitled www.baseballreference.com. The total attendance over the time period of 2003-2005 was 2,395,300 with a standard error of 686,650. There was a minimum attendance of 749,550 and a maximum total attendance of 4,090,700. There...

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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....

...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 in the same units between the two models.
(c) the slope no longer indicates the effect of a unit change of X on Y in the log-linear
model.
(d) the regression R2 can be greater than one in the second model.
1
(v) The exponential function
(a) is the inverse of the natural logarithm function.
(b) does not play an...

...Correlation and regression are techniques which are used to see whether a relationship exists between two or more different sets of data
Learning Objectives:
To identify, by diagram, whether a possible relationship exists between two variables;
To quantify the strength of association between variables using the correlation coefficient;
To show how a relationship can be expressed as an equation;
To identify linear equations when written and when...

...Introduction
This presentation on RegressionAnalysis will relate to a simple regression model. Initially, the regression model 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...

...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...

... c) Starting with week 5 and ending with week 11, forecast registrations using a four-week moving average. [3]
d) Plot the original data and the three forecasts on the same graph. Which forecast smoothes the data the most? Which forecast responds to change the best? [4]
Problem 2 [4]
Given the following data, use exponential smoothing (( = 0.3) to develop a demand forecast. Assume the forecast for the initial period...

...Park University
Multiple RegressionAnalysis
Pamela Lima
EC315 Quantitative Research Methods
Dr. Bell
11/22/2013
Multiple RegressionAnalysis
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...

...
Mortality Rates
RegressionAnalysis of Multiple Variables
Neil Bhatt
993569302
Sta 108 P. Burman
11 total pages
The question being posed in this experiment is to understand whether or not pollution has an impact on the mortality rate. Taking data from 60 cities (n=60) where the responsive variable Y = mortality rate per population of 100,000, whose variables include Education, Percent of the...