Advanced Data Management and Applied Statistics in Healthcare
Master applied inferential statistics for healthcare quality work with practical SPSS-based demonstrations, real examples, and guided interpretation of analytical results.
Format: 20 hours total, divided into 5 sessions of 4 hours each, with 1 session per week.
COURSE HIGHLIGHTS
Designed for healthcare professionals who want stronger analytical decision-making skills, deeper confidence with hypothesis testing, and practical SPSS application in quality improvement and KPI analysis.
Who Should Attend
- Quality professionals
- Managers
- Doctors, dentists, pharmacists
- Nurses and allied healthcare practitioners
Prerequisites
- Bachelor’s degree in a health-related field
- Prior knowledge of descriptive statistics
- Basic SPSS data entry experience
Learning Outcomes
- Interpret p-values and statistical errors
- Apply tests for categorical and continuous data
- Build correlation, regression, and survival analysis
What You Will Study
This course links advanced inferential statistics to healthcare quality functions through hands-on application in SPSS. Participants work through practical demonstrations, real healthcare examples, and interpretation methods relevant to improvement projects and KPI analysis.
- Session 1: Introduction to inferential statistics, p-value, significance, Type I & Type II errors, and power.
- Session 2: Confidence intervals, standard error, sample size calculation, roadmap for hypothesis testing, Student & Paired T-test.
- Session 3: Mann Whitney, One Way ANOVA, Kruskal Wallis, and Chi-square test.
- Session 4: Fisher’s exact test, correlation study, and simple linear regression.
- Session 5: Multiple linear regression, logistic regression, Cox regression, and survival analysis.
Training Program Price: US$360.00
Advance your healthcare analytics capability with practical inferential statistics and SPSS application.
20 Hours | 5 Sessions | 4 Hours/Session
Ideal for healthcare professionals who want to strengthen evidence-based decision-making, quality analysis, and statistical interpretation in real practice.