You must estimate your model using generalized linear models (GLM) methods. Do NOT estimate your model by OLS. For example, do not use Stata’s “regress” command or R’s “lm” command.

A. Introduction
The main question(s) you will attempt to answer any background information

B. Data
Define what your response variable is, give its name as used in your regression, and tell its units of measurement.
For each of your independent variables: define what it is, give its name as used in your regression, and tell its units of measurement.
**You must have multiple independent variables to adequately explain your dependent variable.**
List your data sources.
Use the format below in this Data section ( The bolded lines are examples)
RESPONSE VARIABLE (name in regression noted in parentheses)• Team's Total Number of Regular Season Wins (WINS) – Each team's wins in the given time frame
EXPLANATORY VARIABLES (name in regression noted in parentheses)• Team Total Salary (Salary) - Total payroll measured in dollars.• Team Batting Average (AVG) - Total number of hits divided by total number of at bats• etc.DATA SOURCES• data source #1 • data source #2 • etc.

C.Plots of dependent variable and independent variables.
You should look at the plots of the response variable (Y) and each independent variable(X) to see, among other things, the shape of Y and if there appears to be a connection between Y and X at all. Sometimes a plot will also tell you if the relationship between Yand X is linear or not. So, you must plot Y and X1 (your first X) in one graph, then plot Y and X2 (second X) in another graph, etc.

D.Model specification and Regression Estimation Results
Estimation results for final model (copy and paste from software results)NOTE: these results must be in Courier or Consolas font so the columns in the regression output will line up properly

E. Checks of the adequacy of the model. See “3. Checking the adequacy of the model” above.

F. Inference and Interpretations of every one of your model'ssignificant coefficient estimates. For example, when BOTH your response and your explanatory variable are continuous then it goes like this: “when X changes by one of its units Y rises (or falls) by . . .” Also, state how your results answer the question(s) you posed as the reason(s) for doing this project

G. Summary

H. Add to your report an appendix that contains the code that you used.
Also identify the application that the code is for (e.g. R, Stata, SAS, etc.)

You must estimate your model using generalized linear models (GLM)...