Question

                                  REPORT 1:
Need this solved using R or SAS
Hourly Wage and Working Hours

The following data are from a national sample of 6000 households with a male head
earning less than $15,000 annually in 1966. The households were classified into     
39 demographic groups (e.g. factory workers, farmers, school teachers, etc.)
Each row in the spreadsheet below corresponds to one demographic group.  Hence, 
the spreadsheet contains 39 rows.

The study was undertaken in the context of proposals for a guaranteed annual wage 
(negative income tax).  At issue was the response of labor supply (number of hours) 
to increasing hourly wages. Does the number of labor hours increase or decrease with
wage rates?  What other factors are relevant in predicting labor hours?

Number of cases: 39 
Variable Names:
   1. HRS: Average hours worked during the year
   2. WAGE: Average hourly wage ($)
   3. ERSP: Average yearly earnings of spouse ($)
   4. ERNO: Average yearly earnings of other family members ($)
   5. NEIN: Average yearly non-earned income
   6. ASSET: Average family asset holdings (Bank account, etc.) ($)
   7. AGE: Average age of respondent
   8. DEP: Average number of dependents
   9. RACE: Percent of white respondents
  10. SCHOOL: Average highest grade of school completed 

The Data:

HRS     WAGE     ERSP    ERNO   NEIN   ASSET   AGE     DEP      RACE    SCHOOL

2157    2.905    1121    291    380    7250    38.5    2.340    32.1    10.5
2174    2.970    1128    301    398    7744    39.3    2.335    31.2    10.5
2062    2.350    1214    326    185    3068    40.1    2.851    .       8.9
2111    2.511    1203    49     117    1632    22.4    1.159    27.5    11.5
2134    2.791    1013    594    730    12710   57.7    1.229    32.5    8.8
2185    3.040    1135    287    382    7706    38.6    2.602    31.4    10.7
2210    3.222    1100    295    474    9338    39.0    2.187    10.1    11.2
2105    2.493    1180    310    255    4730    39.9    2.616    71.1    9.3
2267    2.838    1298    252    431    8317    38.9    2.024    9.7     11.1
2205    2.356    885     264    373    6789    38.8    2.662    25.2    9.5
2121    2.922    1251    328    312    5907    39.8    2.287    51.1    10.3
2109    2.499    1207    347    271    5069    39.7    3.193    .       8.9
2108    2.796    1036    300    259    4614    38.2    2.040    .       9.2
2047    2.453    1213    297    139    1987    40.3    2.545    .       9.1
2174    3.582    1141    414    498    10239   40.0    2.064    .       11.7
2067    2.909    1805    290    239    4439    39.1    2.301    .       10.5
2159    2.511    1075    289    308    5621    39.3    2.486    43.6    9.5
2257    2.516    1093    176    392    7293    37.9    2.042    .       10.1
1985    1.423    553     381    146    1866    40.6    3.833    .       6.6
2184    3.636    1091    291    560    11240   39.1    2.328    13.6    11.6
2084    2.983    1327    331    296    5653    39.8    2.208    58.4    10.2
2051    2.573    1194    279    172    2806    40.0    2.362    77.9    9.1
2127    3.262    1226    314    408    8042    39.5    2.259    39.2    10.8
2102    3.234    1188    414    352    7557    39.8    2.019    29.8    10.7
2098    2.280    973     364    272    4400    40.6    2.661    53.6    8.4
2042    2.304    1085    328    140    1739    41.8    2.444    83.1    8.2
2181    2.912    1072    304    383    7340    39.0    2.337    30.2    10.2
2186    3.015    1122    30     352    7292    37.2    2.046    29.5    10.9
2108    2.786    1757    .      506    9658    43.4    .        32.6    10.2
2188    3.010    990     366    374    7325    38.4    2.847    30.9    10.6
2203    3.273    .       .      430    8221    38.2    2.324    22.1    11.0
2077    1.901    350     209    95     1370    37.4    4.158    61.3    8.2
2196    3.009    947     294    342    6888    37.5    3.047    31.8    10.6
2093    1.899    342     311    120    1425    37.5    4.512    62.8    8.1
2173    2.959    1116    296    387    7625    39.2    2.342    31.0    10.5
2179    2.971    1128    312    397    7779    39.4    2.341    31.2    10.5
2200    2.980    1126    204    393    7885    39.2    2.341    31.0    10.6
2052    2.630    .       .      154    3331    40.5    .        45.8    10.3
2197    3.413    1078    300    512    10450   39.1    2.297    15.5    11.3



Instructions:

Conduct an analysis of the response of labor supply (number of hours) 
to increasing hourly wages. Do labor hours increase or decrease with wage 
rates?  What other factors affect the number of hours that people work?
What variables are correlated with each other?  How will this affect your model?
Are there potentially important variables missing from the data set?

   1. Find the best fitting simple linear regression between HRS (Y) and WAGE (X).
      You may consider transformations of variables to uncover linear relationships.

   2. Find the best multiple regression model that you think describes the 
      relationship between HRS and the other variables in the study.

Answer 1

solution:

consider the following given data is a

lvr2plot

   is the leverage against residual squared plot. The upper left corner of the plot will be points that are high in leverage and the lower right corner will be points that are high in the absolute of residuals. The upper right portion will be those points that are both high in leverage and in the absolute of residuals. There is one point in this plot that stands out so much differently from any other point

lvr2plot, ml(subject):