This week we used the magic of the ggplot package to tell a story of our data set. As usual, a research question was given along with a hypothesis. Different themes were added to our ggplot output for beautification purposes while type of geom() we used was used was specially crafted to our data set and question.

This week we focused on Count, ordinal, multinomial, time-to-event & ordinal models. We chose the best model for our analysis based on our variables and research question. These models were used to prove/disprove our hypothesis.

For this week's assignment, we utilised different models such as ecological regression, pooling & complete pooling etc. to highlight multilevel analysis in our data set. We also learned how to avoid ecological fallacy by using the right models to tell our story.

This week we focused on Count, ordinal, multinomial, time-to-event & ordinal models. We chose the best model for our analysis based on our variables and research question. These models were used to prove/disprove our hypothesis.

Use of the Zelig package & syntax to highlight estimation, mean and probability of a dichotomous variable on with respect to independent variables.

Pre-existing data highlighting logistic regression that was given in class was asked to be re-created from the Zelig 4 syntax to the Zelig 5 syntax.

Interaction Variables

Binary Dependent Variable models. Exploring various models using different tests to get the best model that answers our research question.

This homework introduces the use of the Maximum Likelihood Estimation (MLE) and the GLM (Generalized Linear Model) in interpreting discrete and continuous variables. This is essentially a continuation of ANOVA but using different models to yield better results.

Wrangling data in R using mainly the dplyr package.

To get acclimated with R Markdown and its syntax.

The final analysis of Soc. 333 to sum up all that we have learned when programming using R. Various statistical analyses were used to show significance and association among certain variables of choice.

Final analysis draft- compilation of everything we have learned in Soc. 333 with re-coding, analyzing and describing data. We learned how to choose variables & predict their significance and association using T-test, Chi Square, CL Theorem and etc. The draft was worked on for a period of time and the final analysis with any major corrections were submitted on 12/18/18.

Questions formulated into 3 categories affecting adolescents after exploring data set YRBSS

Test Central Limit Theorem & Compare Sampling Distributions

T-tests

Chi-Squared Test for Independence

Bar Charts and Comparing Frequencies of Categorical Variables

Means and Distributions

Piping Commands together

Wrangling Data with dplyr

Replicating Voter Analysis in R

Utilizing R Markdown in R Studio for the first time