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1) MY SAS VISUALIZATION PROGRAM

/* DATA VISUALIZATION - UNIVARIATE AND BIVARIATE PLOTS / / Developed by: [Your Name] / / Date: [Current Date] */

/* Clear work environment */ proc datasets library=work nolist kill; quit;

/* Create dataset - STUDENTS */ data students; input ID Age Gender $ Course $ Grade Attendance; datalines; 1 20 M Mathematics 85 90 2 22 F Biology 78 85 3 21 F History 92 95 4 19 M Mathematics 76 80 5 23 M Biology 88 92 6 20 F Mathematics 95 98 7 22 M History 82 88 8 21 F Biology 79 83 9 20 M Mathematics 91 94 10 24 F History 84 87 11 19 M Biology . 75 12 22 F Mathematics 87 91 13 21 M History 93 96 14 20 F Biology 80 82 15 23 M Mathematics 89 93 ; run;

/* STEP 1: UNIVARIATE PLOTS - Examine center and spread */

/* Histogram for Age (Numerical Variable) */ proc sgplot data=students; histogram Age / binwidth=1 fillattrs=(color=steelblue) transparency=0.5; density Age / lineattrs=(color=red thickness=2); title "Univariate Analysis: Age Distribution"; title2 "Histogram with Density Curve"; run;

/* Bar chart for Gender (Categorical Variable) */ proc sgplot data=students; vbar Gender / fillattrs=(color=lightgreen) dataskin=crisp; title "Univariate Analysis: Gender Distribution"; title2 "Bar Chart of Categorical Variable"; run;

/* Bar chart for Course (Categorical Variable) */ proc sgplot data=students; vbar Course / fillattrs=(color=coral) dataskin=crisp; title "Univariate Analysis: Course Distribution"; title2 "Bar Chart of Academic Programs"; run;

/* Box plot for Grade (Numerical Variable - Center and Spread) */ proc sgplot data=students; vbox Grade / fillattrs=(color=lightpurple) dataskin=crisp; title "Univariate Analysis: Grade Distribution"; title2 "Box Plot Showing Center and Spread"; run;

/* STEP 2: BIVARIATE PLOTS - Association between variables */

/* Scatter plot: Age vs Grade */ proc sgplot data=students; scatter x=Age y=Grade / markerattrs=(color=blue size=10px symbol=circlefilled); reg x=Age y=Grade / lineattrs=(color=red thickness=2); title "Bivariate Analysis: Age vs Grade"; title2 "Scatter Plot with Regression Line"; xaxis label="Age (Years)"; yaxis label="Grade (Points)"; run;

/* Grouped bar chart: Course vs Average Grade */ proc sgplot data=students; vbar Course / response=Grade group=Gender groupdisplay=cluster stat=mean dataskin=crisp; title "Bivariate Analysis: Average Grade by Course and Gender"; title2 "Grouped Bar Chart - Categorical Association"; yaxis label="Average Grade"; run;

/* Box plot: Grade distribution by Course */ proc sgplot data=students; vbox Grade / category=Course group=Gender groupdisplay=cluster fillattrs=(color=lightblue) dataskin=crisp; title "Bivariate Analysis: Grade Distribution by Course and Gender"; title2 "Grouped Box Plots - Center and Spread Comparison"; run;

/* Additional bivariate plot: Attendance vs Grade */ proc sgplot data=students; scatter x=Attendance y=Grade / group=Course markerattrs=(size=10px) transparency=0.3; ellipse x=Attendance y=Grade / alpha=0.2 type=mean; title "Bivariate Analysis: Attendance vs Grade by Course"; title2 "Scatter Plot with Confidence Ellipses"; xaxis label="Attendance Rate (%)"; yaxis label="Grade (Points)"; run;

2) UNIVARIATE PLOTS CREATED

Histogram - Age Distribution:

Shows the frequency distribution of student ages

Includes density curve for normal distribution reference

Center: Around 21 years | Spread: 19-24 years range

Bar Chart - Gender Distribution:

Visual representation of gender proportions

Clear comparison between male and female counts

Bar Chart - Course Distribution:

Shows popularity of different academic programs

Mathematics appears most frequent

Box Plot - Grade Distribution:

Displays center (median), spread (IQR), and potential outliers

Shows the five-number summary visually

3) BIVARIATE PLOTS CREATED

Scatter Plot - Age vs Grade:

Examines relationship between age and academic performance

Includes regression line to show trend

Reveals any correlation between these variables

Grouped Bar Chart - Course vs Grade by Gender:

Compares average grades across different courses

Shows gender differences within each course

Reveals interaction between categorical variables

Grouped Box Plots - Grade by Course and Gender:

Compares grade distributions across multiple categories

Shows center, spread, and variability for each group

Visualizes potential differences in academic performance

Scatter Plot - Attendance vs Grade by Course:

Examines relationship between attendance and grades

Includes confidence ellipses for each course

Shows clustering patterns by academic program

4) GRAPHICAL ANALYSIS DESCRIPTION

Univariate Plot Insights: The Age histogram shows a relatively symmetric distribution centered around 21 years, with most students aged 19-22. The Gender bar chart confirms nearly equal distribution between males and females. Course distribution reveals Mathematics as the most popular program (40%), while the Grade box plot shows good spread with median around 85-87 points and one potential missing value.

Bivariate Relationship Insights: The Age vs Grade scatter plot shows no strong linear relationship, suggesting age doesn't strongly predict academic performance in this sample. The Course vs Grade analysis reveals interesting patterns - Mathematics students show consistently high grades, while Biology shows more variability. Gender differences appear minimal within courses. The Attendance vs Grade plot shows a positive trend, suggesting higher attendance correlates with better grades, particularly evident in the Mathematics group.

Key Findings:

Course selection appears more influential on grades than age or gender

Attendance shows positive association with academic performance

Mathematics students demonstrate both high attendance and high grades

SAS PROGRAM FREQUENCY DISTRIBUTION ANALYSIS 1) MY SAS PROGRAM CODE

/* STEP 1: First program - Base for future analysis / / Developed by: [Your Name] / / Date: [Date] */

/* Clear work environment */ proc datasets library=work nolist kill; quit;

/* Create sample dataset - STUDENTS */ data students; input ID Age Gender $ Course $ Grade Attendance; datalines; 1 20 M Mathematics 85 90 2 22 F Biology 78 85 3 21 F History 92 95 4 19 M Mathematics 76 80 5 23 M Biology 88 92 6 20 F Mathematics 95 98 7 22 M History 82 88 8 21 F Biology 79 83 9 20 M Mathematics 91 94 10 24 F History 84 87 11 19 M Biology . 75 12 22 F Mathematics 87 91 13 21 M History 93 96 14 20 F Biology 80 82 15 23 M Mathematics 89 93 ; run;

/* STEP 2: Frequency distributions for three variables */

/* Analysis of AGE variable */ proc freq data=students; tables Age / missing; title "Frequency Distribution - STUDENT AGE"; title2 "Analysis of Discrete Quantitative Variable"; run;

/* Analysis of GENDER variable */ proc freq data=students; tables Gender / missing; title "Frequency Distribution - STUDENT GENDER"; title2 "Analysis of Nominal Qualitative Variable"; run;

/* Analysis of COURSE variable */ proc freq data=students; tables Course / missing; title "Frequency Distribution - STUDENT COURSE"; title2 "Analysis of Nominal Qualitative Variable"; run;

/* Analysis of GRADE variable (with missing data) */ proc freq data=students; tables Grade / missing; title "Frequency Distribution - STUDENT GRADE"; title2 "Analysis of Quantitative Variable with Missing Data"; run;

2) FREQUENCY DISTRIBUTION RESULTS

3) FREQUENCY DISTRIBUTION DESCRIPTION

Age Variable Analysis: The age distribution shows students range from 19 to 24 years, with the highest concentration at age 20 (26.67%). The distribution is relatively balanced across age groups, with no missing data. Most students (66.67%) are between 20-22 years old.

Gender Variable Analysis: Gender distribution is nearly balanced, with 53.33% male and 46.67% female students. The slight difference represents only one additional male student in the sample. There are no missing values in this variable.

Course Variable Analysis: Mathematics is the most popular course (40%), followed by Biology (33.33%) and History (26.67%). The distribution shows a preference for STEM fields, with no missing data recorded.

Dataset Selection

For this data analysis project, we chose the U.S. National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

NESARC is ideal as a representative sample of the U.S. adult population and provides detailed data on mental health and substance use, allowing for robust testing of comorbidity hypotheses.

Association of Interest

The focus of my research is to explore the association between Generalized Anxiety Disorder (GAD) and Nicotine Dependence.

Topic 1: Nicotine Dependence (TABLIFEDX) Topic 2: Generalized Anxiety Disorder (GENAXLIFE)

Research Question and Hypothesis

Research Question: Are individuals with a lifetime reported history of Generalized Anxiety Disorder (GAD) (GENAXLIFE) more likely to have a lifetime diagnosis of Nicotine Dependence (TABLIFEDX) than those without a history of GAD?

Clear Hypothesis:

The presence of a lifetime diagnosis of Generalized Anxiety Disorder (GENAXLIFE = Yes) will be significantly associated with an Odds Ratio greater than 1.0 for a lifetime diagnosis of Nicotine Dependence (TABLIFEDX = Yes) in the NESARC population.

Literature Review

Search Terms Used

NESARC comorbidity anxiety nicotine dependence

Generalized Anxiety Disorder Nicotine Dependence association epidemiology

Summary of Findings (Patterns and Variables)

The epidemiological literature demonstrates a strong comorbidity, indicating that:

Pattern: Individuals with anxiety disorders exhibit significantly higher rates of nicotine dependence. The self-medication mechanism is a central hypothesis, where nicotine is used to modulate or reduce the tension and hyperexcitability characteristic of GAD.

Controlling for Variables: Researchers in the fields of public health and psychiatry consistently controlled for variables such as age and sex, as they are known to influence the prevalence of both disorders.

Key Reference (Example)

James M. Zech, Tapan A. Patel, Jesse R. Cougle, et al. (2025). Prevalence and Correlates of Alcohol, Cannabis, and Tobacco Use Disorder in DSM-5 Generalized Anxiety Disorder: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions-III.

This recent NESARC-III research validates the focus on the high correlation between Tobacco Use Disorder (Dependência de Nicotina) and Generalized Anxiety Disorder, strongly supporting the proposed hypothesis involving the variables GENAXLIFE and TABLIFEDX.