MG 315 Park University Advanced Business Statistics What Affects Graduation Grade Average Points Capstone Hi there, The final revision of the paper is due

MG 315 Park University Advanced Business Statistics What Affects Graduation Grade Average Points Capstone Hi there,

The final revision of the paper is due this week. I’ve attached a couple examples that my instructor sent to the class. Below is the feedback I received regarding the draft paper I turned in:

Add title page addressing use of your regression model. Use table of contents. Added subheads. Added purpose statement. Your regression equation has at least three independent variables. Added cited definition of variables. Your data matrix has at least 30 rows. You have columns of data, one for each variable. Good bibliography. You did follow APA style throughout, and in your written portion. Well done.

If you need me to send a copy of the draft paper you did for me just let me know. Thank you so much!!!

I also attached examples of the final paper. What variables affect foreclosure filings?
EC315
U.S. Foreclosures
Feasibility Project
Prepared for
Mr. Thomas Hiestand
Park University
Prepared by
Jessica Tipton
Park University
Jessica Tipton
Quantitative Research Methods
12 January 2009
1
What variables affect foreclosure filings?
2
Purpose Statement & Background
The United States foreclosure crisis is said to be the worst financial disaster since the
great depression. (Reinhart, 2008) John A. Tatom, Director of Research at Networks Financial
Institute at Indiana State University writes that rising foreclosure filings and excessive personal
debt have created the greatest short-term threat to the U.S. economy. This study will examine
these and other variables responsible for the rise in foreclosure filings throughout the United
States.
The dependent variable, U.S. Foreclosures Filings (US_FORCLSURE) is determined by
independent variables, High Un-employment rate (UNEPLY_RATE), high revolving debt
(REVOLV_DEBT), divorce (DIVORCE) and Death (DEATH).
The most important independent variable in this relationship is rise in unemployment
rates because the more people who lose their jobs the more likely those sample individuals will
begin the foreclosure process and eventually lose their home.
Definition of Variables & Data Description
Y = X1 + X2 + X3 + X4
US_FORCLSURE = UNEPLY_RATE + REVOLV_DEBT + DIVORCE + DEATH
•
Dependent Variable:
a. U.S. Foreclosures Filing (Y: US_FORCLSURE)- The dependent variable, U.S.
Foreclosures Filings, is used in the cause and affect relationship presented throughout
the project. The following independent variables will be used to determine the most
significant factor in U.S. foreclosure filings. This data is collected every month from
more than 2,200 counties by RealtyTrac®. RealtyTrac® compiles this nationwide
data and reports a count of the total number of properties with at least one foreclosure
filing reported.
What variables affect foreclosure filings?
•
3
Primary Independent Variable:
a. Rising Unemployment (X1: UNEPLY_RATE)- Rising unemployment rates is the
primary independent variable because I believe that as the national employment rate
continues to rise so will the number of foreclosure filings. The data used to represent
the relationship was provided by the Bureau of Labor Statistics and collected
monthly.
The first resource that solidifies my belief comes from cnnmoney.com. Les
Christie describes in the article, “Mounting Job Losses Fueling Foreclosures,” the
correlation between unemployment rates and the rise in U.S. Foreclosures. He
explains that the more individuals lose their jobs the more the delinquency trend will
continue. (Christie, 2008)
In the article, “2008 Foreclosure Filings Set Record,” Stephanie Armour
recognizes the relationship between job loss and foreclosure filings. She clarifies that
even though interest rates decreased, the number of those filing for foreclosure
continued to rise between 2007 and 2008 resulting in an 81% increase. The former
federal deputy Housing Commissioner under President Clinton is quoted saying,
“with foreclosures continuing to rise and the economy in a downward spiral, it’s not
surprising you see increased foreclosure because of increased unemployment.”
(Armour, 2009)
•
Independent Variables:
a. Divorce (X2: DIVORCE)- Divorce is another leading cause in the number of
foreclosure filings, if a marriage is dissolved then there is less revolving revenue for
the family and they then run the risk of foreclosure. This data was compiled through
the Center for Disease Control and Prevention & National Center for Health
What variables affect foreclosure filings?
4
Statistics. These government agencies use a series of surveys to compile monthly data
on divorce records. These records are compiled monthly and are represented by the
thousands.
b. Death (X3:DEATH)- The death statistics used for this project have been recorded by
Center for Disease Control and Prevention & National Center for Health Statistics.
Surveys are used to obtain and compile monthly data. These records are assembled
monthly and are denoted by the thousands. These statistics are important as if a
primary spouse dies then the family is at risk of becoming delinquent in his/her
mortgage payments.
c. Consumer Credit Outstanding (X4:REVOLV_DEBT) – When individuals live
beyond his/her means the amount of money placed in a revolving credit card becomes
more than is manageable. The minimum balance that was once reasonable becomes
unrealistic and the family is then susceptible to foreclosure. These debts could include
from car loans, credit cards and student loans. This data is compiled by the Federal
Reserve and has been collected on a monthly basis from January 1968 to November
2008. For this project, the investigation will include the data from June 1006 to
November 2008.
Presentation and Interpretation of Results
Estimated (prediction) equation
The results are:
Y? = -1,229,444.4414 + 15853.9372 X1 + 1.4785 X2 + 2.8838X3 -1.0534 X4
What variables affect foreclosure filings?
5
Identify and Interpret the Adjusted R2
An adjusted coefficient of multiple determination is used to “balance the effect that the
number of independent variables has on the coefficient of multiple determination.” (Lind,
Marchal W., and Wathen, S., 2008, p. 449) The adjusted R2 in this project’s model of 0.856 (see
Appendix B) signifies a strong association between the independent variables of unemployment
rates, outstanding consumer credit, number of divorces, and number of deaths and dependent
variable of U.S. foreclosure rates.
Identify and Interpret the F Test
H0 : ?1 = ? 2 = ? 3 = ? 4 = 0
H1 : Not all ? i ‘ s are 0
The p-value is 1.01E-07 or .000000101. (see Appendix B)
Using a .05 significance level, the p-value is significantly smaller than the significance
level. Since the p-value is smaller than the significance level, H0 is rejected and accept the
alternate hypothesis of not all independent variables are 0. The rejection of H0 means at least one
regression coefficient is not 0 and therefore, some of the independent variables do have the
ability to explain the variation in the dependent variable, U.S. Foreclosure Filings.
Identify and Interpret the t Tests for Each of the Coefficients
Un-Employment Rates (UNEPLY_RATE)
Ho: ?1 = 0
H1: ?1 does not equal 0
Let ? = 0.05
Testing at the .05 significance level:
N – (k + 1) = 22 – (4 + 1) = 17 degrees of freedom
What variables affect foreclosure filings?
6
Reject H0 if t > 2.110 or t < -2.110 t= b1 ? 0 15,853.9372 = = 0.955 Sb1 16,597.6668 Accept Ho at the .05 significance level as, 0.955 is greater than the critical value, 2.110. Accept H0 using the p value approach as well because the p value for UNEPLY_RATE is .3529 (see Appendix B) and greater than the significance level of .05. There is little evidence that the null hypothesis is not true. Consumer Credit Outstanding (REVOLV_DEBT) Ho: ? 2 = 0 H1: ? 2 does not equal 0 Let ? = 0.05 Testing at the .05 significance level: N – (k + 1) = 22 – (4 + 1) = 17 degrees of freedom Reject H0 if t > 2.110 or t < -2.110 t= b2 ? 0 1.4785 = = 10.66 Sb2 0.1387 Reject Ho at the .05 significance level as, 10.66 is greater than the significance level of 2.110. Reject H0 using the p value approach as well because the p value for REVOLV_DEBT is 6.00E09 (see Appendix B) and less than the .05 significance level. A p-value of 6.00E-09 indicates that there is little evidence that the null hypothesis is true. Divorce Rate (DIVORCE) Ho: ? 3 = 0 H1: ? 3 does not equal 0 What variables affect foreclosure filings? Let ? = 0.05 Testing at the .05 significance level: N – (k + 1) = 22 – (4 + 1) = 17 degrees of freedom Reject H0 if t > 2.110 or t < -2.110 t= b3 ? 0 2.8838 = = 2.27 S b3 1.2698 Reject Ho at the .05 significance level as, 2.27 is greater than the significance level of 2.110. Reject H0 using the p value approach as well because the p value for DIVORCE is .0364 (see Appendix B) and less than the .05 significance level. A p-value of .0364 indicates that there is strong evidence that the null hypothesis is not true. Deaths (DEATH) Ho: ? 4 = 0 H1: ? 4 does not equal 0 Let ? = 0.05 Testing at the .05 significance level: N – (k + 1) = 22 – (4 + 1) = 17 degrees of freedom Reject H0 if t > 2.110 or t < -2.110 t= b4 ? 0 - 1.0535 = = ?2.33 Sb4 0.4530 Reject Ho at the .05 significance level as, -2.33 is less than the significance level of -2.110. Reject H0 using the p value approach as well because the p value for DIVORCE is .0327 (see Appendix B) and less than the .05 significance level. A p-value of .0327 indicates that there is strong evidence that the null hypothesis is not true. 7 What variables affect foreclosure filings? 8 Variable With the Greatest Significance on U.S. Foreclosures Filing (US_FORCLSURE) The independent variable that has the greatest significance on dependent variable, U.S. foreclosure filing rates is Consumer Credit Outstanding (REVOLV_DEBT). Analysis of Multicollinearity of the Independent Variables Generated Correlation Matrix Correlation Matrix U.S. Foreclosures Filings U.S. Foreclosures Filings 1.000 Un-Employment Rates-% .288 Consumer Credit Outstanding -Millions .900 Divorce Rate-Thousands -.148 Deaths-Thousands .262 Un-Employment Rates-% Consumer Credit Outstanding -Millions Divorce Rate-Thousands Deaths-Thousands 1.000 .393 -.501 .489 1.000 -.359 .479 1.000 -.248 1.000 22 sample size Correlation Matrix “Multicollinearity exists when independent variables are correlated.” (Lind et al., 2008, p. 461) These correlated variables make it difficult to make conclusions about their individual effects on the dependent variable and the individual regression coefficients. (Lind et al., 2008, p.461) Highly Correlated Independent Variables and Indications of Multicollinearity There is no multicollinearity among the independent variables shown in the correlation matrix. All correlation values fall inside the normal indicators, -.70 and .70. The most highly correlated independent variables are that of the divorce rates and un-employment rates. These variables are inversely related at a level of -.501. Indicating that the higher the divorce rate the lower the unemployment rate. What variables affect foreclosure filings? 9 References Research: Armour, S. (2009, January 15). 2008 foreclosure filings set record. USA TODAY, p. B.1. Retrieved January 21, 2009, from ProQuest National Newspapers Premier database. (Document ID: 1627331121). Christie, L. (2008 November 7). Mounting job losses fueling foreclosures. CNNMoney.com, Retrieved January 21, 2009, from http://money.cnn.com/2008/11/04/real_estate/job_losses_fuel_foreclosure/index.htm?pos tversion=2008110705 Lind, D., Marchal W., and Wathen, S. Basic Statistics for Business Economics 6th ed. McGrawHill Irwin, New York, NY. Copyright 2008. Reinhart, V. (2008, March 26). Our Overextended Fed. Wall Street Journal (Eastern Edition), p. A.15. Retrieved February 3, 2009, from ABI/INFORM Global database. (Document ID: 1451528761). Tatom, J. (2008, July). The U.S. Foreclosure Crisis: A Two-Pronged Assault on the U.S. Economy. Networks Financial Institute at Indiana State University, Retrieved February 3, 2009, from http://mpra.ub.uni-muenchen.de/9787/1/2008-WP-10_Tatom.pdf Data: ? Dependent Variable: o U.S. Foreclosure Filings-? RealtyTrac. (2009). News and Trends Center, RealtyTrac Reports. Retrieved January 20, 2009, from http://www.realtytrac.com/News-Trends/ What variables affect foreclosure filings? 10 ? Primary Independent Variable: o Rising Unemployment-? Bureau Of Labor Statistics. (2009). National Unemployment Rate, BLS Statistics on Unemployment. Retrieved January 20, 2009, from http://www.bls.gov/bls/unemployment.htm ? Independent Variable: o Divorce-? National Center for Health Statistics. (2009). Births, Marriages, Divorces, and Deaths, National Vital Statistics Reports. Retrieved January 21, 2009, from http://www.cdc.gov/nchs/products/pubs/pubd/nvsr/nvsr.htm#vol57 o Death? National Center for Health Statistics. (2009). Births, Marriages, Divorces, and Deaths, National Vital Statistics Reports. Retrieved January 21, 2009, from http://www.cdc.gov/nchs/data/nvsr/nvsr57/nvsr57_06.htm#tables ? National Center for Health Statistics. (2009). Births, Marriages, Divorces, and Deaths, National Vital Statistics Reports. Retrieved January 21, 2009, from http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_21.htm#tables o Consumer Credit Outstanding? Federal Reserve. (2009, January 8). Consumer Credit Outstanding: Revolving, Federal Reserve Statistical Release. Retrieved January 20, 2009, from http://www.federalreserve.gov/releases/g19/hist/cc_hist_r.html What variables affect foreclosure filings? APPENDIX A The following data will be used to conduct the regression testing: Dependent Variable U.S. Foreclosures Filings 233,089 223,651 233,001 230,874 201,950 224,451 223,538 243,947 179,599 164,644 176,137 147,708 149,150 130,786 130,511 109,652 120,334 115,568 112,210 115, 292 92, 845 88,195 Independent Variables UnYear & Employment Month Rates-% Mar Feb Jan Dec-07 Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan Dec-06 Nov Oct Sep Aug Jul Jun 5.2 5.2 5.4 4.8 4.5 4.4 4.5 4.6 4.9 4.7 4.3 4.3 4.5 4.9 5.0 4.6 4.3 4.1 4.4 4.6 5.0 4.8 Consumer Credit Outstanding -Millions 943237.94 948758.73 957891.35 969597.16 944671.83 927240.08 921014.07 916740.55 903894.02 897339.98 890146.48 881472.71 875567.27 878806.30 887862.99 902316.16 878104.68 861350.83 857915.20 854817.60 844837.62 841313.05 Divorce RateThousands DeathsThousands 67,405 66,743 68,868 67,876 66,399 76,696 67,854 74,754 69,463 69,514 76,716 74,559 73,974 65,253 70,166 69,021 68,641 74,365 71,931 72,004 64,798 79,664 221,000 212,000 222,000 214,000 199,000 198,000 187,000 191,000 194,000 188,000 199,000 203,000 216,000 204,000 222,000 214,000 194,000 200,000 190,000 193,000 192,000 188,000 11 What variables affect foreclosure filings? 12 Appendix B Regression Analysis R² Adjusted R² R Std. Error 0.883 0.856 0.940 20079.976 n 22 k 4 Dep. Var. U.S. Foreclosures Filings ANOVA table Source Regression Residual Total SS 51,843,399,576.0609 6,854,492,706.3028 58,697,892,282.3636 Regression output variables Intercept Un-Employment Rates-% Consumer Credit Outstanding -Millions Divorce Rate-Thousands Deaths-Thousands coefficients -1,229,444.4414 15,853.9372 1.4785 2.8838 -1.0535 df 4 17 21 std. error 179,151.2879 16,597.6668 0.1387 1.2698 0.4530 MS 12,960,849,894.0152 403,205,453.3119 F 32.14 t (df=17) -6.863 0.955 10.662 2.271 -2.326 p-value 2.75E-06 .3529 6.00E-09 .0364 .0327 p-value 1.01E-07 confidence interval 95% lower 95% upper -1,607,420.6159 -851,468.2669 -19,164.0783 50,871.9528 1.1859 1.7710 0.2048 5.5628 -2.0092 -0.0977 Amy Copeland Term Project Report Term Project Report Do Overall Health, Age, Race, and Gender Effect Heart Disease? Amy Copeland EC 315 DL Professor Hiestand Summer 2008 Does Overall Health Effect Heart Disease? Amy Copeland Term Project Report Do Overall Health, Age, Race, and Gender Effect Heart Disease? Background Studies have shown there are several contributors to heart disease. Heart disease (HD) is determined by physical inactivity, obesity/overweight and smoking. The most important factor is physical inactivity because it is the key to good health and a person has control over this. Therefore, if a person maintains a healthy lifestyle, they will be less likely to develop heart disease. This study will determine the effect of overall health, age, race and gender on heart disease. The model (less constants and coefficients) is: HD = physical inactivity + obesity + smoking According to the National Heart, Lung, and Blood Institute, “Each year, in the United States alone, cigarettes are responsible for up to a third of all deaths from heart disease, according to the American Heart Association. Of the approximately 440,000 annual deaths caused by smoking each year, about 180,000 are related to heart disease and another 160,000 are due to lung cancer. Just a few smokes a day can double your risk for a heart attack, and more than a pack a day could raise the risk fifteenfold, according to a 1998 report in Postgraduate Medicine.” (2008) Overall health, to include exercise, diet and smoking is a main contributor of heart disease. Obesity, high cholesterol, diabetes and physical inactivity are all contributors to not being overall healthy and therefore heart disease. Obesity is now recognized as a major risk factor for coronary heart disease, which can lead to heart attack. (American Heart Association, 2008) The gender of a person can determine their risk of heart disease, as well. The American Heart Association reports, “The relative risk of coronary heart disease associated with physical Amy Copeland Term Project Report inactivity ranges from 1.5 to 2.4, an increase in risk comparable to that for high blood cholesterol, high blood pressure or cigarette smoking.” (2008) The relationship between heart disease, physical inactivity, obesity and smoking is evident. All individuals, not only those at a higher risk, should improve their overall health in order to decrease their risk of heart disease. (U.S. National Library of Medicine, 2008) Individuals with a higher risk of heart disease due to gender, race and age should be vigilant about the signs and symptoms of heart disease and heart attacks. Presentation and Interpretation of Results Estimated (prediction) equation: The results are: _ Y = -372.1648 -1.8728 X1 - 8.8926 X2 + 2.3332 X3 Define “adjusted R2.” Adjusted coefficient of determination (R2) is used to balance the effect that the number of independent variables has on the coefficient of multiple determination. (Lind, 2008) The adjusted R2 in this project’s model of 0.494 signifies a moderate association between the independent variables of physical inactivity, obesity and smoking and the dependent variable of heart disease. Fifty percent of the people with heart disease (dependent variable) have one or more of the independent variables. Amy Copeland Term Project Report Identify and interpret the F test: F test null hypothesis: F test alternate hypothesis: The p-value is 0001. H0: B1 = B2 = B3 = B4 H1: Not all the B1’s are 0 Using a .05 significance level, the p-value is significantly smaller than the significance level. Since the p-value is smaller than the significance level, H0 is rejected and the alternate hypothesis of not all independent variables are 0 cannot be accepted. Identify and interpret the t tests for each of the coefficients: Physical Inactivity: The negative number 1.8728 was surprising as one would expect the number to be higher to indicate that the greater number of people who are physically inactive would contribute to the number of people with heart disease. H0: B1 equals 0 H1: B1 does not equal 0 Test at the .05 significance level and is a two tailed test. N – (k + 1) = 50 – (4 + 1) = 45 degrees of freedom The critical value is 2.014 (Lind, 2008) Decision rule is to reject H0 if the computed value of t is less than -2.014 or greater than 2.014. The t value is -0.695 therefore, accept H0. The t value is in within the acceptable numbers, therefore this variable is 0. Using the p-value approach, the null hypothesis for t test is rejected because the pvalue for the physical inactivity variable of 0.4930 far falls below the significance level of .05. The p-value of 0.4930 signifies that there is minimal evidence that the null hypothesis is not true. Amy Copeland Term Project Report Obesity: The number for obesity (8.8926) is not surprising. One would imagine and has been told time and time again that there is a link between obesity and heart disease. H0: B1 equals 0 H1: B1 does not equal 0 Test at the .05 ... Purchase answer to see full attachment