Intro to Biostatistics

Unit 1 – Descriptive Statistics

Unit 2 – Probability Theory

Unit 3 – Sampling Distributions

Unit 4 – Hypothesis Testing

Unit 5 – Confidence Intervals in Biostatistics

Unit 6 – Regression Analysis

Unit 7 – Analysis of Variance (ANOVA)

Unit 8 – Experimental Design

Unit 9 – Survival Analysis

Unit 10 – Epidemiological Measures

Unit 11 – Statistical Software & Data Management

## What do you learn in Introduction to Biostatistics

Biostatistics blends biology, math, and statistics to analyze health-related data. You'll learn about study design, data collection methods, and statistical techniques used in public health research. The course covers probability theory, hypothesis testing, regression analysis, and interpreting medical literature. You'll also get hands-on experience with statistical software to crunch numbers and make sense of health trends.

## Is Introduction to Biostatistics hard?

It can be challenging, especially if you're not a math whiz. The concepts aren't rocket science, but there's a lot to wrap your head around. Some students find the statistical formulas and software intimidating at first. But don't panic - with practice and a good study routine, most people get the hang of it. The key is to stay on top of the material and not fall behind.

## Tips for taking Introduction to Biostatistics in college

1. Use [Fiveable Study Guides](https://fiveable.me/cram-mode) to help you cram 🌶️
2. Practice, practice, practice - work through lots of problem sets
3. Form a study group to tackle tricky concepts together
4. Don't just memorize formulas, understand the logic behind them
5. Get comfortable with statistical software early on
6. Relate the material to real-world health issues to make it more engaging
7. Watch "Moneyball" to see how statistics can be applied in unexpected ways
8. Read "The Immortal Life of Henrietta Lacks" for a fascinating look at ethics in medical research

## Common pre-requisites for Introduction to Biostatistics

1. Introductory Statistics: This course covers basic statistical concepts and methods. You'll learn about descriptive statistics, probability distributions, and hypothesis testing.

2. Calculus I: This class introduces differential and integral calculus. It's essential for understanding more advanced statistical concepts and mathematical modeling in biostatistics.

## Classes similar to Introduction to Biostatistics

1. Epidemiology: This course focuses on the distribution and determinants of health-related events in populations. You'll learn about disease patterns, risk factors, and how to design and interpret epidemiological studies.

2. Data Analysis for Public Health: This class teaches you how to wrangle, analyze, and visualize health data. You'll use statistical software to explore real-world public health datasets and draw meaningful conclusions.

3. Research Methods in Public Health: This course covers the fundamentals of designing and conducting public health research. You'll learn about different study designs, sampling techniques, and how to critically evaluate scientific literature.

## Majors related to Introduction to Biostatistics

1. Public Health: Focuses on promoting health and preventing disease at the population level. Students learn about epidemiology, health policy, and environmental health.

2. Biostatistics: Combines statistical theory with biological and health sciences. Students develop skills in data analysis, study design, and statistical modeling for medical research.

3. Epidemiology: Concentrates on studying patterns, causes, and effects of health conditions in populations. Students learn to investigate disease outbreaks and assess public health interventions.

4. Health Informatics: Merges healthcare, information technology, and data science. Students learn to manage and analyze health data to improve patient care and healthcare systems.

## What can you do with a degree in Introduction to Biostatistics?

1. Biostatistician: Designs studies and analyzes data for medical research. They collaborate with scientists and healthcare professionals to interpret results and draw conclusions.

2. Clinical Data Analyst: Works with healthcare providers to analyze patient data and improve clinical outcomes. They use statistical methods to identify trends and patterns in medical records.

3. Epidemiologist: Investigates patterns and causes of diseases in populations. They design and conduct studies to track public health issues and develop strategies for prevention and control.

4. Health Data Scientist: Applies advanced analytics and machine learning to large healthcare datasets. They develop predictive models and data-driven solutions to improve patient care and health outcomes.

## Introduction to Biostatistics FAQs

1. Do I need to be a math genius to succeed in this course? Not at all, but you should be comfortable with basic algebra and willing to put in the effort to learn new concepts.

2. Will we use any specific software in this class? Most likely, you'll use statistical software like R, SAS, or SPSS. Don't worry if you're new to these - you'll learn as you go.

3. How is biostatistics different from regular statistics? Biostatistics applies statistical methods specifically to biological and health-related data. You'll focus on examples and applications in medicine and public health.

Public Health

1.2 Measures of variability

1.1 Measures of central tendency

1.3 Data visualization techniques

1.4 Frequency distributions

1.5 Percentiles and quartiles

2.5 Random variables

2.1 Basic probability concepts

2.2 Probability distributions

2.3 Conditional probability

2.4 Bayes' theorem

3.1 Central Limit Theorem

3.2 Standard error

3.3 Sampling distribution of the mean

3.4 Sampling distribution of the proportion

3.5 T-distribution

4.1 Null and alternative hypotheses

4.2 Type I and Type II errors

4.3 P-values

4.4 Statistical power

4.5 One-sample tests

4.6 Two-sample tests

5.1 Confidence interval for the mean

5.2 Confidence interval for the proportion

5.4 Confidence interval for the difference between proportions

5.5 Interpreting confidence intervals

5.3 Confidence interval for the difference between means

6.1 Simple linear regression

6.2 Multiple linear regression

6.3 Logistic regression

6.4 Model diagnostics

6.5 Correlation analysis

7.2 Two-way ANOVA

7.3 Repeated measures ANOVA

7.4 Post-hoc tests

7.5 Assumptions and diagnostics

7.1 One-way ANOVA

8.1 Randomization

8.2 Blinding

8.5 Factorial designs

8.3 Control groups

8.4 Sample size determination

9.1 Kaplan-Meier estimator

9.3 Censoring

9.4 Log-rank test

9.5 Hazard ratios

9.2 Cox proportional hazards model

10.1 Incidence and prevalence

10.2 Relative risk

10.3 Odds ratio

10.4 Sensitivity and specificity

10.5 Attributable risk

11.1 Introduction to statistical software packages

11.2 Data cleaning and preprocessing

11.3 Data visualization tools

11.4 Basic programming concepts

11.5 Reproducible research practices

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