

Cristina Rivera
Use the TEDS2016 dataset to run a multiple regression model. Access the data set using the following codes


(Adapted from Stackoverflow examples) (Objectives: Use plotly, reshape packages, interactive visualization)
Call:
lm(formula = Petal.Length ~ 0 + Sepal.Length + Sepal.Width, data = iris)
Residuals:
Min 1Q Median 3Q Max
-1.70623 -0.51867 -0.08334 0.49844 1.93093
Coefficients:
Estimate Std. Error t value Pr(>|t|)
Sepal.Length 1.56030 0.04557 34.24 <2e-16 ***
Sepal.Width -1.74570 0.08709 -20.05 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6869 on 148 degrees of freedom
Multiple R-squared: 0.973, Adjusted R-squared: 0.9726
F-statistic: 2663 on 2 and 148 DF, p-value: < 2.2e-16
a. Age
b. Education
c. Income


The problem it looks like with my variables is that it doesn’t seem like plotting a linear regression is enough and there might need to be a multinomial logistic regression. Since some of the variables are discrete, a simple regression might not be enough.