Question
I have uploaded data set (school teachers.xls) of the mean salary of school teachers for all states in the USA. I have also categorized the
- I have uploaded data set (school teachers.xls) of the mean salary of school teachers for all states in the USA. I have also categorized the entire USA into 3 regions: region 1 (Northeast and North central states), region 2 (Sothern states), and region 3 (Western states). Since there are 3 categories (region 1, region 2, and region 3), so there must be 2 dummy variables here.
1 for region 1
=0 for region 2 and region 3
1 for region 2
=0 for region 1 and region 3.
Therefore both dummy variables are zero for region 3. And thus region 3 is our base category.
Here annual salary is the dependent variable and per student (pupil) spending (pps) is the independent variable.
- Formulate a dummy variable regression model of the annual salary of school teachers with government spending as the regressor to see which region has the highest salary (or second highest and lowest) when per student government spending is zero. (10)
- Estimate the regression model you developed in part (a) and interpret the results. (10)
- Now reformulate the regression model in part (a) to see if the rate of increase in salary is different across different regions with the increase in government spending. (10)
Estimate the regression model you developed in part (c) and interpret your results. (10)
| State | Pay | PPS | AREA | D1 | D2 |
| ME | 19583 | 3346 | 1 | 1 | 0 |
| NH | 20263 | 3114 | 1 | 1 | 0 |
| VT | 20325 | 3554 | 1 | 1 | 0 |
| MA | 26800 | 4642 | 1 | 1 | 0 |
| RI | 29470 | 4669 | 1 | 1 | 0 |
| CT | 26610 | 4888 | 1 | 1 | 0 |
| NY | 30678 | 5710 | 1 | 1 | 0 |
| NJ | 27170 | 5536 | 1 | 1 | 0 |
| PA | 25853 | 4168 | 1 | 1 | 0 |
| OH | 24500 | 3547 | 1 | 1 | 0 |
| IN | 24274 | 3159 | 1 | 1 | 0 |
| IL | 27170 | 3621 | 1 | 1 | 0 |
| MI | 30168 | 3782 | 1 | 1 | 0 |
| WI | 26525 | 4247 | 1 | 1 | 0 |
| MN | 27360 | 3982 | 1 | 1 | 0 |
| IA | 21690 | 3568 | 1 | 1 | 0 |
| MO | 21974 | 3155 | 1 | 1 | 0 |
| ND | 20816 | 3059 | 1 | 1 | 0 |
| SD | 18095 | 2967 | 1 | 1 | 0 |
| NE | 20939 | 3285 | 1 | 1 | 0 |
| KS | 22644 | 3914 | 1 | 1 | 0 |
| DE | 24624 | 4517 | 2 | 0 | 1 |
| MD | 27186 | 4349 | 2 | 0 | 1 |
| DC | 33990 | 5020 | 2 | 0 | 1 |
| VA | 23382 | 3594 | 2 | 0 | 1 |
| WV | 20627 | 2821 | 2 | 0 | 1 |
| NC | 22795 | 3366 | 2 | 0 | 1 |
| SC | 21570 | 2920 | 2 | 0 | 1 |
| GA | 22080 | 2980 | 2 | 0 | 1 |
| FL | 22250 | 3731 | 2 | 0 | 1 |
| KY | 20940 | 2853 | 2 | 0 | 1 |
| TN | 21800 | 2533 | 2 | 0 | 1 |
| AL | 22934 | 2729 | 2 | 0 | 1 |
| MS | 18443 | 2305 | 2 | 0 | 1 |
| AR | 19538 | 2642 | 2 | 0 | 1 |
| LA | 20460 | 3124 | 2 | 0 | 1 |
| OK | 21419 | 2752 | 2 | 0 | 1 |
| TX | 25160 | 3429 | 2 | 0 | 1 |
| MT | 22482 | 3947 | 3 | 0 | 0 |
| ID | 20969 | 2509 | 3 | 0 | 0 |
| WY | 27224 | 5440 | 3 | 0 | 0 |
| CO | 25892 | 4042 | 3 | 0 | 0 |
| NM | 22644 | 3402 | 3 | 0 | 0 |
| AZ | 24640 | 2829 | 3 | 0 | 0 |
| UT | 22341 | 2297 | 3 | 0 | 0 |
| NV | 25610 | 2932 | 3 | 0 | 0 |
| WA | 26015 | 3705 | 3 | 0 | 0 |
| OR | 25788 | 4123 | 3 | 0 | 0 |
| CA | 29132 | 3608 | 3 | 0 | 0 |
| AK | 41480 | 8349 | 3 | 0 | 0 |
| HA | 25845 | 3766 | 3 | 0 | 0 |
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Statistics For Engineers And Scientists
Authors: William Navidi
3rd Edition
73376345, 978-0077417581, 77417585, 73376337, 978-0073376332
