Characteristic |
Beta |
95% CI 1 |
|---|---|---|
| (Intercept) | 69 | 67, 71 |
| gender | ||
| gendermale | 5.3 | 3.5, 7.0 |
| test_prep | ||
| test_prepnone | -5.5 | -7.4, -3.6 |
| parent_education_lvl | ||
| parent_education_lvlBachelorsDegree | 1.5 | -1.7, 4.7 |
| parent_education_lvlHighSchool | -5.5 | -8.3, -2.6 |
| parent_education_lvlMastersDegree | 2.5 | -1.6, 6.5 |
| parent_education_lvlSomeCollege | -0.61 | -3.3, 2.0 |
| parent_education_lvlSomeHighSchool | -4.8 | -7.7, -2.0 |
| 1
CI = Credible Interval |
||
Model
\[ math_i = \beta_0 + \beta_1male_i + \beta_2none_i + \beta_3BachelorsDegree_i + \beta_4HighSchool_i + \beta_5MastersDegree_i + \beta_6SomeCollege_i + \beta_7SomeHighSchool_i + \epsilon_i \]
This table shows how students’ math exam scores are affected by many factors. The intercept represents the average math exam score of a female student who completed the test preparation and who’s parents have an associate’s degree. The “gendermale” shows that the intercept is increased by about 5.3 if the student is a male. This also applies to the “testprep_none” and the “parent_education_lvl”.
\[ reading_i = \beta_0 + \beta_1male_i + \beta_2none_i + \beta_3BachelorsDegree_i + \beta_4HighSchool_i + \beta_5MastersDegree_i + \beta_6SomeCollege_i + \beta_7SomeHighSchool_i + \epsilon_i \]
Characteristic |
Beta |
95% CI 1 |
|---|---|---|
| (Intercept) | 79 | 77, 81 |
| gender | ||
| gendermale | -6.9 | -8.5, -5.3 |
| test_prep | ||
| test_prepnone | -7.3 | -9.1, -5.5 |
| parent_education_lvl | ||
| parent_education_lvlBachelorsDegree | 1.9 | -1.2, 4.8 |
| parent_education_lvlHighSchool | -5.3 | -8.0, -2.7 |
| parent_education_lvlMastersDegree | 4.1 | 0.33, 7.8 |
| parent_education_lvlSomeCollege | -1.3 | -3.7, 1.2 |
| parent_education_lvlSomeHighSchool | -4.3 | -7.0, -1.7 |
| 1
CI = Credible Interval |
||
This table shows how students’ reading exam scores are affected by many factors. The intercept represents the average reading exam score of a female student who completed the test preparation and who’s parents have an associate’s degree. The “gendermale” shows that the intercept is decreased by about 6.9 if the student is a male. This also applies to the “testprep_none” and the “parent_education_lvl”.
\[ writing_i = \beta_0 + \beta_1male_i + \beta_2none_i + \beta_3BachelorsDegree_i + \beta_4HighSchool_i + \beta_5MastersDegree_i + \beta_6SomeCollege_i + \beta_7SomeHighSchool_i + \epsilon_i \]
Characteristic |
Beta |
95% CI 1 |
|---|---|---|
| (Intercept) | 80 | 78, 82 |
| gender | ||
| gendermale | -8.9 | -10, -7.3 |
| test_prep | ||
| test_prepnone | -9.9 | -12, -8.1 |
| parent_education_lvl | ||
| parent_education_lvlBachelorsDegree | 3.2 | 0.18, 6.2 |
| parent_education_lvlHighSchool | -6.2 | -8.8, -3.6 |
| parent_education_lvlMastersDegree | 5.3 | 1.5, 9.1 |
| parent_education_lvlSomeCollege | -0.73 | -3.2, 1.7 |
| parent_education_lvlSomeHighSchool | -5.5 | -8.1, -2.9 |
| 1
CI = Credible Interval |
||
This table shows how students’ writing exam scores are affected by many factors. The intercept represents the average writing exam score of a female student who completed the test preparation and who’s parents have an associate’s degree. The “gendermale” shows that the intercept is decreased by about 8.9 if the student is a male. This also applies to the “testprep_none” and the “parent_education_lvl”.