A random sample of at-risk first-grade students participated in a one-year reading program designed to enhance their competency in the area of reading. These at-risk students were falling behind in the classroom instruction in the first grade and were identified by teacher recommendation and reading assessment prior to the instruction in reading in the first grade. At the beginning (in the fall), students were assessed using several reading tests, among them is Writing Vocabulary Test. At the end of the first grade (in the spring), students were assess using several reading tests again. Students who had similar scores as their non-at risk classmates were classified into DISCONTINUE category, meaning that they succeeded the reading program. The other students were classified into RECOMMEND category, which means that further help is needed to enhance these students' reading.

The STATUS dataset contains the following variables:

lunch - a measure of student's social economic status (1=free lunch; 2=pay a reduced amount for lunch; 3=pay a full amount for lunch).

fowt - score on writing vocabulary test measured in fall

status - whether a student was discontinued or recommended for further help (1=discontinued; 0=recommend)

1.Run univariate logistic regression model to predict STATUS using LUNCH. Please report OR, 95% CI and P-value and interpret the result.

2.Run univariate logistic regression model to predict STATUS using FOWT. Please report OR, 95% CI and P-value and interpret the result.

3.Run Multivariate logistic regression model to predict STATUS using both LUNCH and FOWT. Please report OR, 95% CI and P-value, evaluate overall goodness-fit of the model and interpret the result.