project in econ 256

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Question Description

i will pot all the Checkpoints that i did in the attachment





ECON/MGMT 256 Final Analysis Report

For the final project please take your work and findings from Checkpoints 1-5 and write a cohesive final report. The final paper should be between 5-7 pages in length and should incorporate the (corrected) work that you have previously done. It is not necessary that you find significant relationships, but it IS necessary that you understand your model and analyses. Specifically, be sure to address the following questions/topics in your paper.

  1. What is your question of interest?
  2. Discuss your theory and/or model that you explored
  3. Discuss the data source(s) that you used for your modela. What is/are the dependent variables?b. What is/are the independent variables?
  4. Discuss the models that you ran (Checkpoints 2-5. For all models, identify the variablesof interest, discuss the significance of the overall model, and discuss the coefficients and their significance. Include your summary tables, but do NOT include your raw data output. Be sure to address what you did via the following analyses:a. Simple linear regression
    b. Multiple linear regression
    c. Multiple linear regression with one or more dummy Independent Variables d. Multiple linear regression with a binary Dependent Variable (logit model)
  5. Summarize your overall findings and conclusions.
    a. What did you learn about your theory/model?
    b. What recommendations would you make for any changes to your model, data,or analytical approach?

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1 Running Head: Check Point 3 Check Point 3 Student name: Institutional affiliation: 2 Check Point 3 CHECK POINT 3 Proposed Model with three independent variables: Consider the figure below: Proposed Federal Tax Model Corporate Income taxes Federal Tax Income Individual Income Taxes Payroll Taxes According to the above federal tax revenue model, it follows that: • Dependent variable: The federal tax income • Independent variables: Individual Income taxes, corporate income taxes and Payroll Taxes 3 Check Point 3 Multiple Regression: Consider the following information: Year Federal Income Corporation Taxes (in Trillion) Income Taxes 2012 2013 2014 2015 2016 2017 $2.45 $2.77 $3.02 $3.25 $3.27 $3.32 Individual Payroll Taxes Income Taxes 0.2423 0.2735 0.3207 0.3438 0.3 0.297 0.8453 0.9478 1.0239 1.0653 1.115 1.162 1.1322 1.3164 1.3946 1.5408 1.546 1.587 For this regression analysis, federal income tax is the dependent variable (Y) while the Corporation Income tax, Individual Income tax, and payroll taxes are the independent variable (X). The table below shows the output summary when the independent variable is regress on the dependent variable: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.998970662 0.997942384 0.994855961 0.024690139 6 ANOVA df Regression Residual Total Intercept Corporation Income Taxes Payroll Taxes Individual Income Taxes 3 2 5 SS MS F Significance F 0.591314127 0.19710471 323.3329 0.00308484 0.001219206 0.0006096 0.592533333 Coefficients Standard Error -0.001753276 0.130430366 1.468436156 0.581100873 1.100783847 0.575329378 1.021558075 0.4345449 t Stat -0.01344224 2.5269901 1.91331069 2.35086886 P-value 0.990495 0.127361 0.195829 0.143101 Lower 95% -0.56294985 -1.0318391 -1.37465867 -0.84813773 Upper 95% 0.559443294 3.968711415 3.576226364 2.891253876 Lower 95.0% Upper 95.0% -0.562949847 0.559443294 -1.031839104 3.968711415 -1.37465867 3.576226364 -0.848137725 2.891253876 4 Check Point 3 RESIDUAL OUTPUT Predicted Federal Income Taxes (in Observation Trillion) Residuals 1 2.441149443 0.008850557 2 2.787965993 -0.017965993 3 3.020931672 -0.000931672 4 3.249776789 0.000223211 5 3.245480345 0.024519655 6 3.334695758 -0.014695758 R-squared and significance Model: According to this table, the value of R-squared = 0.9990 or 99.9%. This value implies that 99.9% of the data collected regarding the federal income tax, corporation income tax, payroll tax, and individual income taxes will fall on the fitted regression line. In addition, this high R2 also indicates that P value / high R2 combination will a small value indicating the independent variables have a direct proportionality with the dependent variable. This implies that increase in corporation income tax; payroll taxes and individual income taxes will cause an increase in the gross federal income tax. On the other hand, the significance of this model is 0.0031. This implies that the model is positively significant. Slope Coefficients: 5 Check Point 3 According multiple regression table, the slope coefficients for the independent variables are as follows: Corporation income tax= 1.4684; this implies that the hypothesis states: for every 1 trillion increase in corporation income tax, the federal income tax increases by 1.4684 trillion. Payroll tax = 1.1008; this implies that the hypothesis states: for every 1 trillion increase in payroll tax, the federal income tax increases by 1.1008 trillion Individual income tax = 1.0216; this implies that the hypothesis states: for every 1 trillion increase in individual income tax, the federal income tax increases by 1.0216 trillion. As it is evident Corporation income tax, Payroll tax and Individual income tax have significant slope coefficients. Regression Equation: To begin, the regression equation for payroll tax is Y = a + bX. The graph below shows the regression: Where a= 0.0188 and b =2.917 Therefore, the regression equation is: 6 Check Point 3 Federal income tax = 7.7023 payroll tax + 0.7318 trillion The regression equation for individual income tax shown in graph below: Therefore, Federal income tax = 1.9619 individual income tax + 0.2284 trillion The regression equation for corporation tax shown in the graph below: Therefore, Federal income tax = 7.7023 Corporation tax + 0.7318 trillion Brief Discussion OF my Model: According to this regression analysis, there exists a strong positive relationship between the federal gross revenue collection and the individual income tax, payroll tax, and corporation tax. The fact that the three independent variables have positive slope coefficients indicates that the 7 Check Point 3 relationship is linear and that increase or decrease in any of these independent variables causes a linear effect on the federal revenue. 8 Check Point 3 References Fahrmeir, L. (2013). Regression : models, methods and applications. Berlin ; New York: Springer. Internal Revenue Service. (2012). Internal Revenue Service Data. U.S. Government Printing Office. Weisberg, S. (2005). Applied linear regression. Hoboken, N.J.: Wiley-Interscience. 1 Running Head: Check Point 1 Check Point 1 Student name: Institutional affiliation: 2 Check Point 1 Check Point 1 CHECK POINT 1 Model: The figure below shows the Federal Tax revenue model: Federal Tax Revenue Model Excise, Estate, and Other taxes Corporate Income taxes Federal Tax Income Income Taxes Payroll Taxes According to the above federal tax revenue model, it follows that: • Dependent variable: The federal tax income is the dependent variable because the amount of its collection depends on the income taxes, patrol taxes, corporate income taxes, excise, estate and other taxes. • Independent variables: Income taxes and corporate income taxes are the independent variables. • Moderator variables: The payroll taxes, excise, estate and other taxes are the moderator variables because they determine the relationship between income taxes and federal taxes 3 Check Point 1 Model Description: My model involves investigating the federal tax income. The research will determine the relationship between the amount of federal tax income and the payroll taxes, income taxes, corporate income taxes, excise, estate and other taxes in the United States of America. The dependent variable will be the federal tax income while the independent variable will be the corporate income taxes and income taxes. Moreover, the moderate variables will be payroll taxes, excise, estate and other taxes. Data source description: In my research, I will use previous research studies regarding the federal government, with most of the information being from the US government Website. Moreover, I will also make use of financial websites such as income tax department to collect high quality information for the research. In addition, I will contact the US finance office to get detailed information about annual federal income taxes, total payroll taxes, corporate income taxes, and other taxes. I believe that my research topic has been previously well researched and vested and am optimistic that I will get the right information. 4 Check Point 1 References Fahrmeir, L. (2013). Regression : models, methods and applications. Berlin ; New York: Springer. Internal Revenue Service. (2012). Internal Revenue Service Data. U.S. Government Printing Office. Weisberg, S. (2005). Applied linear regression. Hoboken, N.J.: Wiley-Interscience. 1 Running Head: CHECK POINT 4 Check Point 4 Student name: Institutional affiliation: Date Submitted: 2 CHECK POINT 4 CHECK POINT 4 For this checkpoint, I will pick the federal income taxes rates as the categorical independent variable. The nominal federal tax rate in the USA is a flat rate of 21%. However, stated and local taxes often vary by policy. As such, my model would show the task rates from 2012 to 2017 as it varies. Recode of the categorical independent variable, Tax Rates. (Dummy Coding): Federal Income Corporation Payroll Individual Year Taxes(in Trillion) Income Taxes Taxes Income Taxes Tax Rates 2012 2.45 0.2423 0.8453 1.1322 2013 2.77 0.2735 0.9478 1.3164 2014 3.02 0.3207 1.0239 1.3946 2015 3.25 0.3438 1.0653 1.5408 2016 3.27 0.3 1.115 1.546 2017 3.32 0.297 1.162 1.587 Consider the following information: For this regression dummy coding, the tax rate values are recoded to: 1 -> 1.0 Else -> 0.0 TaxRates into Rates (Tax Rates) Old Value New Value Value Label 1 2 1 2 1 2 1 2 1 1 2 1 3 CHECK POINT 4 RECODE TaxRates ('1'=1) (ELSE=0) INTO Rates. VARIABLE LABELS Rates 'Rates'. The tables below represent the output values of the logit model: Convergence Informationa,b Maximum Number of 20 Iterations Converge Tolerance .00100 Final Maximum Absolute 3.35851E-5c Difference Final Maximum Relative 8.31506E-5 Difference Number of Iterations 5 a. Model: Multinomial Logit b. Design: Constant + Year + Year * Rates c. The iteration converged because the maximum absolute changes of parameter estimates is less than the specified convergence criterion. Cell Counts and Residualsa,b Observed % Count % Residual Standardized Adjusted Residual Residual Rates Year .00 2012 .500 10.0% .500 10.0% .000 .000 . .000 2013 1.500 30.0% 1.500 30.0% .000 .000 . .000 2014 .500 10.0% .500 10.0% .000 .000 . .000 2015 .500 10.0% .500 10.0% .000 .000 . .000 2016 1.500 30.0% 1.500 30.0% .000 .000 .000 .000 2017 .500 10.0% .500 10.0% .000 .000 . .000 2012 1.500 21.4% 1.500 21.4% .000 .000 .000 .000 2013 .500 7.1% .500 7.1% .000 .000 . .000 1.00 Count Expected Deviance 4 CHECK POINT 4 2014 1.500 21.4% 1.500 21.4% .000 .000 . .000 2015 1.500 21.4% 1.500 21.4% .000 .000 . .000 2016 .500 7.1% .500 7.1% .000 .000 . .000 2017 1.500 21.4% 1.500 21.4% .000 .000 .000 .000 a. Model: Multinomial Logit b. Design: Constant + Year + Year * Rates Logit Regression Equation: logistic regression year with payroll taxes & individual income taxes/categorical = rates 5 CHECK POINT 4 References Fahrmeir, L. (2013). Regression : models, methods and applications. Berlin ; New York: Springer. Internal Revenue Service. (2012). Internal Revenue Service Data. U.S. Government Printing Office. Weisberg, S. (2005). Applied linear regression. Hoboken, N.J.: Wiley-Interscience. 1 Running Head: CHECK POINT 5 Check Point 5 Student name: Institutional affiliation: Date Submitted: 2 CHECK POINT 5 Check Point 5 CHECK POINT 5 Dichotomous Dependent Variable: For this checkpoint, I created a dichotomous dependent variable, ‘Fully Paid’ (Yes or No). This would show weather the taxes are fully paid in a certain year or not. The dichotomous variable is as shown in the graph below: 3 CHECK POINT 5 Logit Model: Cell Counts and Residualsa,b Observed Paid Count Expected % Count % Residual Standardized Adjusted Residual Residual Deviance 1.00 4.500 64.3% 4.500 64.3% .000 .000 . .000 2.00 2.500 35.7% 2.500 35.7% .000 .000 .000 .000 a. Model: Multinomial b. Design: Constant + Paid Cell Counts and Residualsa,b Observed 1.500 75.0% 1.500 75.0% .000 .000 . .000 2.00 .500 25.0% .500 25.0% .000 .000 .000 .000 1.00 1.500 75.0% 1.500 75.0% .000 .000 . .000 2.00 .500 25.0% .500 25.0% .000 .000 .000 .000 1.00 1.500 75.0% 1.500 75.0% .000 .000 . .000 2.00 .500 25.0% .500 25.0% .000 .000 .000 .000 1.00 .500 25.0% .500 25.0% .000 .000 .000 .000 2.00 1.500 75.0% 1.500 75.0% .000 .000 . .000 1.00 1.500 75.0% 1.500 75.0% .000 .000 . .000 2.00 .500 25.0% .500 25.0% .000 .000 .000 .000 1.00 .500 25.0% .500 25.0% .000 .000 .000 .000 2.00 1.500 75.0% 1.500 75.0% .000 .000 . .000 2017 a. Model: Multinomial Logit b. Design: Constant + Paid + Paid * Year Residual Residual 1.00 2016 % Residual 2012 2015 Count Adjusted Paid 2014 % Standardized Year 2013 Count Expected Deviance 4 CHECK POINT 5 Coefficientsb,c Federal Income Taxes(in Corporation Individual Year Paid Trillion)a Income Taxesa Payroll Taxesa Income Taxesa 2012 1.00 2.45000000000 .242300000000 .845300000000 1.13220000000 0000 000 000 0000 .000000000000 .000000000000 .000000000000 .000000000000 000 000 000 000 2.77000000000 .273500000000 .947800000000 1.31640000000 0000 000 000 0000 .000000000000 .000000000000 .000000000000 .000000000000 000 000 000 000 3.02000000000 .320700000000 1.02390000000 1.39460000000 0000 000 0000 0000 .000000000000 .000000000000 .000000000000 .000000000000 000 000 000 000 .000000000000 .000000000000 .000000000000 .000000000000 000 000 000 000 3.25000000000 .343800000000 1.06530000000 1.54080000000 0000 000 0000 0000 3.27000000000 .300000000000 1.11500000000 1.54600000000 0000 000 0000 0000 .000000000000 .000000000000 .000000000000 .000000000000 000 000 000 000 .000000000000 .000000000000 .000000000000 .000000000000 000 000 000 000 3.32000000000 .297000000000 1.16200000000 1.58700000000 0000 000 0000 0000 2.00 2013 1.00 2.00 2014 1.00 2.00 2015 1.00 2.00 2016 1.00 2.00 2017 1.00 2.00 a. Sum of the coefficients is not zero for some combinations of levels of independent factors. The generalized log-odds ratio is not computed. b. Model: Multinomial Logit c. Design: Constant + Paid + Paid * Year 5 CHECK POINT 5 Logistic Function: Logit function is the algorithm of odds: logit(x) = log(x / (1 – x)) Below is a plot representing the logit function: 6 CHECK POINT 5 References Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical Care (London, England), 9(1), 112-118. http://dx.doi.org/ 10.1186/cc3045 Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York, NY: John Wiley & Sons Inc. Peng, C. J., & So, T. H. (2002). Logistic regression analysis and reporting: A primer. Understanding Statistics, 1(1), 31-70. ...
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Zencchen
School: Rice University

I have completed the final repo...

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Anonymous
Thank you! Reasonably priced given the quality not just of the tutors but the moderators too. They were helpful and accommodating given my needs.

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