U.S. Bureau of Labor Statistics: National Longitudinal Surveys (NLS) The U.S. De

Question

U.S. Bureau of Labor Statistics: National Longitudinal Surveys (NLS)

The U.S. Department of Labor conducts National Longitudinal Surveys (NLS) of a nationally representative sample of over ten thousand men and women over time. The excel file, Labor Market, is a subset data of mentioned survey that contains only 200 observations. Using this dataset answer the below questions and write a managerial report. Variable descriptions are as follows;

Wage wage, (ln(wage/gnp deflator)) age in current year
Age age in current year
Exper total work experience
Educ current grade completed

Weeks_ue weeks unemployed last year

Weeks_work weeks worked last year

Tenure job tenure, in years

Managerial Report

1. Use methods of descriptive statistics to summarize the data. Comment on the findings.

2. Develop an estimated regression equation that can be used to predict Wage using Age, Exper, Educ, Weeks_ue, Weeks_work, and Tenure. Write out the results in equation form and discuss your findings.

3. Starting with the estimated regression equation developed in part (2), delete any independent variables that are not significant and develop a new estimated regression equation that can be used to predict Wage. Use = .05.

4. (Use the estimated regression equation developed in part (3)) Mrs. Y wants to hire a new manager. She is considering two candidates with the same characteristics that you have found in part (3), except one has nine years and the other eighteen years of education. What wage difference can she expect between the two candidates? What is a 95% interval estimate for this difference?

5. Develop an estimated regression equation that can be used to predict Wage using only Exper and Educ in the data provided. The tenth individual in the sample has Exper = 8.6 and Educ = 12. Find the predicted wage for this individual from the regression line.

6. (Use the estimated regression equation developed in part (5)), the actual wage of the tenth individual in the sample was $1.86342. Find the residual for this individual. Does it suggest that the person underpaid or overpaid for the job?

obs Wage Age Exper Educ Weeks_ue Weeks_work Tenure 1 1.589977 20 2.25641 12 0 51 0.91667 2 1.778681 25 3.77564 12 0 52 1.5 3 2.551715 28 5.29487 12 0 75 1.83333 4 2.614172 33 7.16026 12 0 97 1.91667 5 2.536374 35 8.98718 12 0 95 3.91667 6 2.462927 37 10.33333 12 0 70 5.33333 7 1.360348 19 0.71154 12 19 13 0.25 8 1.726721 25 3.21154 12 0 52 2.66667 9 1.68991 26 4.21154 12 0 52 3.66667 10 1.863417 31 8.58333 12 12 37 8.58333 11 1.789367 33 10.17949 12 4 83 1.83333 12 1.856449 37 13.62179 12 0 75 5.25 13 1.54742 25 3.44231 12 0 53 1.41667 14 1.607294 26 4.44231 12 0 52 2.41667 15 1.597267 27 5.38462 12 0 49 3.33333 16 1.622841 31 6.94231 12 0 52 2.41667 17 1.566635 32 7.98077 12 0 54 3.41667 18 1.614229 37 12.61539 12 0 45 8.33333 19 1.525765 41 16.34615 12 14 90 0.41667 20 2.2885 24 2.25 17 0 55 1.41667 21 2.375578 25 3.19231 17 0 49 2.41667 22 2.413923 26 4.21154 17 0 53 3.41667 23 1.476236 34 5.69231 17 0 16 0.33333 24 1.515855 37 8.38462 17 0 42 3.41667 25 1.919034 41 12.03846 17 0 86 0.33333 26 2.200974 43 13.21154 17 0 61 1.75 27 1.820858 24 3.07692 12 0 56 1.91667 28 1.858522 25 4.03846 12 0 50 2.91667 29 1.979301 26 5.03846 12 0 52 0.66667 30 1.990412 27 6.03846 12 0 52 1.66667 31 1.937521 31 7.57692 12 0 52 1.75 32 1.847272 36 10.73077 12 0 72 3.33333 33 1.518572 24 1.38461 12 0 18 0.41667 34 1.607294 25 2.44231 12 0 55 1.41667 35 1.809742 26 3.38462 12 0 49 2.33333 36 1.96311 30 6.41667 12 0 52 6.41667 37 1.982733 31 7.4359 12 0 53 7.41667 38 1.846798 33 9.41667 12 0 78 9.41667 39 1.919913 36 12.41667 12 0 42 12.41667 40 2.089854 42 17.82051 12 0 73 17.75 41 1.539446 21 3.05769 12 0 55 2.75 42 1.49388 22 4 12 0 49 3.75 43 1.454573 23 5.01923 12 0 53 4.75 44 0.473342 37 5.90385 12 0 42 0.83333 45 1.532477 39 7.32692 12 0 74 2.25 46 1.472237 18 0.80769 12 0 34 0.66667 47 1.717023 19 1.76923 12 0 50 1.58333 48 1.747242 20 2.65385 12 0 46 2.5 49 1.799792 21 3.58333 12 0 31 3.58333 50 2.091364 25 7.58333 12 0 39 7.58333 51 2.114099 26 8.64103 12 0 55 8.58333 52 2.34858 31 13.58333 12 0 52 13.5 53 2.350531 37 19.04487 12 0 76 19 54 1.461484 18 1.45513 10 0 54 1.41667 55 1.611663 19 2.45513 10 0 52 2.41667 56 1.321256 25 3.55769 10 49 3 0 57 1.275272 28 3.96154 10 0 17 0.41667 58 0.797449 30 5 10 0 54 0.08333 59 2.845265 40 15.55769 18 0 61 14.16667 60 1.954463 23 3 14 0 51 1.41667 61 1.86433 25 3.86538 14 2 11 0.16667 62 2.125834 30 5.51923 14 1 47 0.91667 63 2.667868 35 9.36538 14 0 52 2 64 2.57137 40 14.25 14 0 77 5.5 65 2.047121 40 7.38462 15 0 69 2.25 66 2.205023 28 6.55769 16 0 52 2 67 2.383516 31 8.76923 16 1 64 0.08333 68 2.485547 34 10.96154 16 0 16 3 69 2.307946 36 12.69231 16 0 90 5 70 2.813745 38 14.46154 16 0 92 7 71 2.82111 39 15.76923 16 0 68 8.41667 72 2.043247 21 0.51923 15 0 27 0.5 73 2.011328 23 2.34615 15 0 51 1.66667 74 2.087474 24 3.30769 15 0 50 2.83333 75 1.93536 28 5.30769 15 0 52 2.41667 76 2.01862 29 6.32692 15 0 53 3.41667 77 2.171384 34 11.09615 15 0 52 8.41667 78 2.739748 40 16.38461 15 0 67 13.83333 79 2.000029 21 0.51923 15 0 27 0.5 80 1.954463 22 1.46154 15 0 49 1.41667 81 2.083182 23 2.33333 15 0 26 2.33333 82 2.647089 24 3.41667 15 0 54 3.41667 83 2.039102 28 6.41667 15 0 8 6.41667 84 2.372024 38 8.99359 15 0 76 3.33333 85 1.237874 28 1.51923 15 0 40 0.91667 86 1.655079 40 7.75 15 0 72 7.75 87 2.01878 21 0.5 15 0 19 0.5 88 2.042001 22 1.55769 15 0 55 1.5 89 1.773027 23 2.42308 15 0 45 2.41667 90 1.780994 29 5.05769 15 0 43 0.91667 91 2.318559 40 14.21154 15 0 72 5.58333 92 2.01878 21 0.5 15 0 26 0.5 93 2.081666 22 1.5 15 0 52 1.5 94 2.117261 23 2.40385 15 0 47 0.33333 95 2.099896 24 3.44231 15 0 54 1.41667 96 2.10058 28 5.41667 15 0 40 5.41667 97 1.990396 29 6.49359 15 0 56 6.41667 98 1.927315 33 9.91667 15 0 80 3.58333 99 2.068818 34 10.66667 15 0 39 0.75 100 2.268184 39 15.85897 15 0 70 4.66667 101 2.208345 23 1.75 15 0 53 1.75 102 2.194083 24 2.76923 15 0 53 2.75 103 1.971228 22 1.42308 15 0 52 1.41667 104 2.042001 23 2.41667 15 0 51 2.41667 105 2.320567 25 3.4359 15 0 53 1.41667 106 2.129515 29 4.87821 15 0 36 2.08333 107 2.056841 30 5.85897 15 0 51 3.08333 108 2.203645 41 15.01282 15 0 72 3 109 1.854699 30 6.38462 15 0 32 1.66667 110 1.873795 36 9.96154 15 0 54 2.58333 111 2.085862 42 15.34616 15 0 72 8.08333 112 2.000029 23 2.6218 14 0 54 1.66667 113 2.099055 25 3.50641 14 0 43 0.75 114 2.038808 26 4.50641 14 0 52 1.83333 115 1.806148 30 5.27564 14 0 10 0.33333 116 1.857954 31 6.35256 14 0 56 1.41667 117 2.119687 36 11.12179 14 0 52 0 118 2.406495 41 16.46795 14 0 70 3.41667 119 2.043247 24 2.53846 14 0 54 2.5 120 2.109713 30 6.76923 14 0 52 3.41667 121 2.115784 32 7.80769 14 0 54 4.5 122 2.311193 36 12.57692 14 0 52 0 123 2.259815 42 17.75 14 0 61 5.41667 124 2.043247 25 2.53846 14 0 54 2.5 125 2.122488 26 3.51923 14 0 51 3.5 126 1.15374 38 10.44231 14 0 51 3.16667 127 2.168054 24 2.45513 14 0 54 2.33333 128 2.122488 25 3.41667 14 0 50 3.33333 129 2.111764 22 2.28846 13 0 49 0.91667 130 1.673709 23 3.36538 13 0 56 2 131 1.57679 21 3.74359 8 5 26 0.58333 132 1.677365 26 4.39744 8 0 5 0.08333 133 1.410987 31 4.95513 8 0 29 0.58333 134 1.382926 32 5.87821 8 2 48 1.5 135 1.131402 29 2.85897 11 37 61 1.16667 136 1.61747 21 5.64744 12 0 54 1.16667 137 2.262825 25 7.58974 12 3 49 0.66667 138 2.321949 26 8.58974 12 0 52 1.66667 139 1.49388 26 4.61538 12 0 58 2.08333 140 1.598673 27 5.5 12 0 46 3.08333 141 1.321256 32 7.5 12 0 52 2.33333 142 2.343061 39 3.39744 16 0 52 2.5 143 2.281429 44 8.83974 16 0 75 7.91667 144 1.75239 20 1.13461 14 0 43 0.75 145 1.196955 22 1.60897 12 10 36 0.75 146 1.633641 23 2.74359 12 0 59 1.83333 147 1.896048 24 3.6859 12 0 49 2.83333 148 1.739161 29 5.6859 12 0 20 2.5 149 1.559205 30 7.01923 17 0 51 5.33333 150 1.817677 32 7.53846 17 0 27 5.75 151 2.666665 30 5.64744 18 30 0 3.16667 152 2.476491 31 6.10897 18 32 24 0.41667 153 2.95308 36 10.83974 18 0 50 3.5 154 3.348382 41 16.20513 18 0 71 3.5 155 1.645193 19 1.36539 12 5 45 0.91667 156 1.826109 20 2.38462 12 0 53 1.91667 157 2.183301 24 5.83333 12 0 52 5.83333 158 2.21354 25 6.91667 12 0 0 6.91667 159 2.209466 27 8.83333 12 0 94 8.83333 160 2.261763 29 10.91667 12 0 72 10.91667 161 2.335653 30 11.97436 12 0 55 11.91667 162 2.315048 34 15.83333 12 0 96 15.83333 163 2.320934 35 17.25 12 0 71 17.25 164 1.115643 44 5.54487 18 0 60 2.91667 165 1.69282 34 7.41026 12 0 38 0.41667 166 1.260325 35 8.73718 12 0 69 0.75 167 1.854256 23 2.38462 12 0 56 1.66667 168 1.848051 24 3.42308 12 0 54 2.66667 169 1.569256 36 5.40385 12 0 51 2.5 170 1.812986 31 5.03846 16 0 54 2.08333 171 2.154093 33 6.03846 16 0 52 4 172 1.602734 20 2.09615 12 0 46 1 173 1.598673 21 3.19231 12 0 57 0.41667 174 1.250366 27 3.73077 12 0 21 0.41667 175 1.821318 31 5.78846 12 0 68 0.25 176 1.488025 38 10.26923 12 1 69 3.25 177 1.507655 19 2.05769 12 0 49 1.08333 178 1.386294 28 3.35256 12 5 1 1.08333 179 1.615858 40 10.39744 12 5 38 0.75 180 1.687865 26 2.96154 12 0 55 2 181 1.676201 27 3.96154 12 0 52 3.08333 182 1.677717 28 4.92308 12 0 50 4 183 1.65669 29 5.98077 12 0 55 5.08333 184 1.289586 34 7.58333 12 0 26 0.41667 185 2.350875 28 1.42308 12 0 41 0.66667 186 1.301507 25 1.63461 12 0 39 0.75 187 1.79126 31 4.13462 12 0 40 3.5 188 1.838153 32 5.05769 12 0 48 4.41667 189 0.753636 34 6.90385 12 0 96 6.41667 190 1.459441 37 9.53846 12 0 51 1.83333 191 2.049209 38 10.58333 13 0 53 10.58333 192 1.94088 42 11.42949 13 0 44 0.33333 193 1.099209 36 4.75 12 1 87 0.33333 194 1.607104 18 1.51923 12 0 53 1.5 195 1.744638 19 2.59615 12 0 56 2.58333 196 1.321256 25 5.57692 12 0 17 0.5 197 1.321042 19 4.5 12 0 48 1.41667 198 1.316365 20 5.51923 12 0 53 2.41667 199 1.452467 24 6.46154 12 0 0 1.25 200 1.275363 31 2.11538 10 32 20 0.33333

Explanation / Answer

1. Descriptive statistics:

Comments:

2. Multiple linear regression equation:

Wage=0.744-0.03*Age +0.06*Exper +0.115*Educ + 0.002*weeks_work+0.014*Tenure

3. After removal of insignificant variables from above:

here all the variables are significant at 5% level.

Equation is:

Wage=0.876 -.034*Age+0.073*Exper + 0.12*Educ

4. The difference in wage will be 1.15 units since Educ has coefficient of 0.115 & there is a difference of 10 units between 9 & 19 yrs.

95% CI:

5. regression eq:

Wage=0.37+0.032*Exper + 0.099*Educ

for individual in the sample has Exper = 8.6 and Educ = 12. Find the predicted wage = 0.37+0.032*8.6+ 0.099*12 = 1.832

6. He is overpaid of $0.03 (since actual wage is $1.86 & predicted is $1.83

Wage Age Exper Educ Weeks_ue Weeks_work Tenure Mean 1.895121 29.17 6.3925 13.325 1.35 50.735 3.2625 Standard Error 0.029831 0.469047 0.303086 0.142119 0.428023 1.346753 0.24442 Median 1.907541 28 5.458335 12 0 52 2.33333 Mode 1.321256 25 4.21154 12 0 52 1.41667 Standard Deviation 0.421879 6.633333 4.286279 2.009869 6.053156 19.04596 3.456616 Sample Variance 0.177982 44.00111 18.37219 4.039573 36.6407 362.7485 11.9482 Kurtosis 0.721017 -0.86314 0.213951 0.007958 33.02937 0.866915 5.580846 Skewness 0.011647 0.356254 0.958211 0.244045 5.578581 -0.29337 2.222033 Range 2.87504 26 18.54487 10 49 97 19 Minimum 0.473342 18 0.5 8 0 0 0 Maximum 3.348382 44 19.04487 18 49 97 19 Sum 379.0242 5834 1278.5 2665 270 10147 652.5001 Count 200 200 200 200 200 200 200

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