SEVERAL MULTIPLE REGRESSION RESULTS: -
Regression of average hourly earnings on education, gender, age, experience as follows:-

Dependant variable: average hourly earnings (in dollars)
Repressors’ (Independent variables) (1) (2) (3) (3-2)
Education 6.865** 8.32** 8.625**
SE (.1783) (.2186) (.2154)
T-value (38.48) (38.07) (40.04)
Gender -3.1578** -3.095**
SE (.1803) (0.1802)
T-value (-17.50) (-17.18)
Age 0.4392**
SE (.0305)
T-value (14.38)
Experience 0.7810** 1.49**
SE (0.1564) (0.1451)
T-value (4.99) (10.25)
Experience^2 -0.046** -0.047**
SE (0.00714) (0.0066)
T-value (-6.47) (-7.30)
Intercept 1.8838* 14.332** 3.038** 4.951**
SE (0.92) (0.8069) (0.7994) (0.7929)
T-value (2.046) (17.76) (3.80) (6.24)

Summary of Statistics
SER 7.88 8.703 8.007 7.86
R2 0.1899 0.012 0.1644 0.1943
N 7986 7986 7986 7986

Standard errors are in parenthesis. **Significant at 1% level of significance. *Significant at 5% level of significance.

4)
LNAHEi= 1.856 + 0.4052bachelori - 0.1804femalei + 0.024agei
SE (0.0534) (0.0103) (0.0105) (0.0018)
T-value 34.79 39.19 -17.26 13.81
SER = 0.4571 R2 = 0.1924

PURPOSE: -
Determining the workers earnings is the purpose of this project. Here we focus mainly on average hourly earnings for individuals. There are some variable factors, which involved in average hourly earnings such as years of experience, years of education, gender and age.
SUMMARY OF CPS08 DATA: -
Each month the Bureau Of Labor Statistics in U.S Department conducts a survey, which provides data on labor force, level of employment, unemployment and earnings on the basis of a randomly taken sample from U.S household. According to this approx. 65,000 U.S households are surveyed each month. CPS08 contains data for full time workers, which are employed more than 35 hours a week for at least 48 weeks in previous year. The time horizon is from Jan 2004 to Dec 2004.
There are some key factors needed to describe such as gender and education: -
FRMALE use 1 if female; use 0 if male
AHE: Average Hourly Earnings
BACHELOR: 1 if worker has a bachelor’s degree;0 if worker has high school degree.

REGRESSION: -
As we are using regression for the project. Regression analysis is a statistical technique for estimating the relationships among variables for the purpose of predicting future values. Dependent variable is AHE and independent variables are Age, Education, Experience and Gender.
In regression analysis, data on dependent and independent variables is plotted on a scatter graph or diagram and trends are indicated through a line of best fit. When solving for dependent variable, one independent variable may change but other has to be remain the same.
Build a model to forecast the AHE of workers based on bachelor, female and age.
Equation of this model is: -
AHEI=1.8838+6.865BACHELORI-3.1578FEMALEI+. 4392AGEi
Forecast AHE for 30-year-old female worker with a bachelor degree: -
AHEI=1.8838+6.865(1)-3.1578(1)+. 4392(30)=$18.767
According to this model female