ECON 6130 WEEKLY DISPATCH: November 24, 2014
My Regression Paper Proposal
Tittle: Estimation of car sale per year in Marion County, Indiana.
ABSTRACT: In this study, I will try to determine and examine factors that contribute to the sale of cars a year long in Marion County. I will propose a simple table as an aid in examining and reporting my estimation regression analyses. The key to my proposal is the determination of variables contributing to the sale of car in Marion County. I will use data from 2013 for the purpose of a linear regression analysis. These variables include population (than to people moving in the county), youth turning 18 years old, and active population in Marion County. I retrieved my data from STATS Indiana.
REGRESSION PAPER GUIDE LINE
1. Are all the necessary elements present? Is there an abstract? Are there sources and citations? Are there heads and titles and other elements one would expect to find?
2. How is the grammar and spelling?
3. Are the citations present when needed?
4. Did you explain your research? Can one understand what you are attempting to explain? Is anything missing in terms of logic? Any leaps in reasoning that are not substantiated? Would a reader stumbling across this article be able to follow it?
5. Did you collect the right data? Were you able to collect data that fit the research (adequate amount, reliable source, etc.)? Is there data they should have used, but was not? Are you correctly using the data (not overlooking key changes that were made due to recession, changes outside the scope of the data, etc.)?
6. Are the results correctly interpreted? Are you using the coefficients correctly (positive values, negative values, etc.)? Are you accepting only variables that are statistically significant? Are you rejecting variables that are not statistically significant?
7. Could the paper be better?
These seven items/questions constitute the rubric that will be used to grade your paper. Make sure you have an abstract (see how to write on in the Moodle resources), you know what youve found, and you write it in a way that others can read and understand it.
EXAMPLE OF REGRESSION PAPER
TITLE:Factors Influencing Median Household Income per Indiana County, 2011
ABSTRACT: This study examines the relationship between a number of variables for each of the 92 Indiana counties and the median household income. Data for 2011 was used, the most recent year for which all data was available and linear regression was employed. Statistical significance was found for: the unemployment rate of the county, the percentage of residents over the age of 25 with a bachelors degree or greater, the percentage of the population between the ages of 25 and 64, the number of housing units per square mile, and the population per square mile. The findings identify the relationship between these variables and median household income and suggest that focusing on attracting more individuals between 25 and 64 can have a greater impact than concentrating on education.
KEYWORDS:Indiana, regression, county, unemployment, educational attainment level
There are ninety-two counties in Indiana and the median household income varies greatly. Much of the state has been dependent on manufacturing in the past, and it still is the biggest sector of the states economy (National Association of Manufacturers). As many other locations struggle to transition from such dependence on a single sector (Heaton, 2005), a number of Indiana counties have been hit hard and seen such things as unemployment rise and brain drain cause many educated individuals to move away to follow the jobs elsewhere.
The purpose of this study is to look for variables that affect the median household income on a county by county basis. Since the well-being of a county can be tied to the tax revenue it collects, it stands to reason that the more income households earn, the more tax revenue can be collected and used to maintain public facilities/infrastructure/projects and the more likely that county is to attract new residents.
While a plethora of literature exists that focuses on educating the undereducated to make their earning potential more valuable over the span of their lifetime, none could be found identifying the impact that their earning potential has on a county. Similarly, there is a surfeit of articles and opinion pieces that one can easily find on the reasons why unemployment is undesirable, but nothing quantitatively tying it to the median household income for the counties of a state.
The purpose of this study is to identify factors that contribute to the median household income within the counties of Indiana.
The expected equation is:
MHI = a – B1UR + B 2BD + B 3PP – B 4HU + B 5PS
Where a is equal to the intercept, while coefficient B is equal to the magnitude of each of the variables and may take on any value. MHI is the median household income for each county in 2011. UR is the 2011 annual average unemployment rate for the county expressed as a percentage of the workforce. BD is the percentage of the county residents possessing a bachelors degree, graduate degree, or professional degree. PP is percentage of the population within the county that is between the ages of twenty-five and sixty-four. HU is the average number of housing units per square mile of the county per the 2010 census. PS is the average population per square mile of the county per the 2010 census. With continuous independent variables (unemployment rate, percentage of population, etc.), B will indicate the value of the change in each increment.
The following sections discuss each of the five hypotheses in more detail.
Hypothesis 1 Unemployment Rate
With the income for a household being dependent upon employment, the rate of unemployment for the county has the ability to act as a negative influencer. It is possible to express the same ratio two different ways: as a percentage of the labor force that is unemployed (9%, for example) and as a percentage of the labor force that is employed (91%, for example) and the difference in which one is used will impact the direction of the influence (negative versus positive, respectively). Since the percentage is traditionally expressed in terms of unemployment rather than employment, it is the former that is used. The first hypothesis thus is:
H1: The annual average unemployment rate for the county will be a significant predictor of the median household income for residents of the county within the observed dataset.
Hypothesis 2 Bachelors Degree or Above
Education is often tied to earning potential over the lifetime of the individual thus the highest level of educational attainment by residents of a county could have the potential to influence household income. Since the highest level of attainment is being examined, only those residents 25 years of age or above are factored in and three types of degrees are considered: bachelors, graduate, and professional. The second hypothesis is:
H2: The percentage of county residents over the age of 25 with a bachelors degree or above will be a significant predictor of the median household income for residents of the county within the observed dataset.
Hypothesis 3 Percentage of Population 25 to 64 Years of Age
Prior to the age of 25, many are in school and devoting time to study rather than work. At the age of 65, many begin to contemplate and pursue retirement rather than work. Between the ages of 25 and 64, individuals have considerable earning potential and, based on that, the third hypothesis is:
H3: The percentage of county residents between the ages of 25 and 64 will be a significant predictor of the median household income for residents of the county within the observed dataset.
Hypothesis 4 Housing Units per Square Mile
Studies have shown that individuals have a preference for space of their own, with many having a dream of owning a sizable amount of land if and when they become successful. Areas which have a small numb
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