Introduction To Biostatistics

Introduction to basic Statistics with Probability and T-test and Basic Categorical Analysis with Chi-Squared

What you'll learn

Descriptive Statistics

Hypothesis Testing

Basic continuous dependent variable statistical tests

Categroical dependent variable statistical tests

Analyzing data with proportion and odds ratios

Using the Chi-Squared analysis and the Cochran Mantel Haenszel test

Showing and explaining the different sample size formulas

Requirements

Basic level of mathematics training.

A basic introduction to statistics course.

Basic level understanding of some statistical software.

No programming experience.

Description

Hello, thank you for considering this course Introduction to Biostatistics. I have made this course out of the years of college courses, webinars, and certificates that I have done over the last 22 years. I made the course at a level that a person who has had an Introduction to Statistics course should be able to start at the beginning and finish with a good education on Introductory Biostatistics. Some people say "Why teach the old stuff?" My reply is "You teach the old stuff because people might be looking for enough knowledge to do their analysis at hand and nothing more." I also think that, most of the new methods are built on the old method, and learning the old methods will help you gain a greater understanding of Biostatistical analysis.I begin the course at an introductory level : introductory probability, into intermediate probability with Bayes Formula and test validation study analysis and introduce Sensitivity and Specificity to parameters of validation studies. I cover the uses of probability in methods such as the Kaplan Meier Survival Analysis. I also make sure to cover the sample size calculation for each statistical method in the course because that will be a large part of your professional work. I briefly cover distributional probability and touch on the Empirical Rule and the T-distribution and including calculating Confidence Intervals.Next, I go into introductory research study methods, discussing rates and incidence and prevalence. I beginning to review hypothesis testing using the two-sample t-test; and then i move to the paired t-test which I discuss is a very useful method in health and clinical research. I also have two main tests, a mid-term and a final; to get you familiar with how the course quizzes in college will look. For all tests I also have a statistical software lesson showing how to do the analyses in SPSS and in R. This should make the time you actually work more productive because you won't be struggling through books or notes.From there I move to the categorical methods: Chi Squared and the Cochran Mantel Haenszel test. The Chi-Squared analysis is the workhorse of categorical Biostatistics. I explain it and do several sections on the Risk Ratio and the Odds ratio in order that you will become familiar with these estimators. The Cochran Mantel Haenszel test is the stratified analysis of more than 1 cross-sectional categorical table. It is also a very powerful method. It involves 3 variable, and I discuss the process models that have three variables and the differences and test that may indicate their presence in the data. My hope is that I will become a we, and we will learn as much as is possible about Biostatistics and improve and advance the field. I will be happy to support you after you have taken the course with whatever Biostatistical help you may need. I have continued to learn new methods well after college and I hope you will also. Thank you and good luck with your careers.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Vocabulary

Lecture 3 File Types used in Biostatistics

Lecture 4 Introduction to Free software R

Section 2: Randomization

Lecture 5 Rand 1 Randomization in R 1

Lecture 6 Rand 2 Randomization in R 2

Lecture 7 Rand 3 Randomization in R 3

Section 3: Probability

Lecture 8 Prob 1 Probability Rule

Lecture 9 Prob 2 Probability Axioms

Lecture 10 Prob 3 Probability of Two Events Ocurring - Addition Rule 1

Lecture 11 Prob 4 Probability of Two Events Ocurring - Addition Rule 2

Lecture 12 Prob 5 Conditional Probability

Lecture 13 Prob 6 Independense and The Multiplication Rule

Lecture 14 Prob 7 Start Bayes Rule

Lecture 15 Prob 8 Start Example For Bayes Rule and Short Proof of Bayes Rule

Lecture 16 Prob 9 Finish Example of Bayes Rule

Lecture 17 Prob 10 Distribution Probabilities 1

Lecture 18 Prob 11 Distribution Probabilities 2

Lecture 19 Prob 12 Distribution Probabilities 3

Lecture 20 Prob 13 Distribution Probabilities 4

Lecture 21 Prob 14 Binomial Distribution Probabilities 1

Lecture 22 Prob 15 Binomial Distribution Probabilities 2

Lecture 23 Prob 16 Binomial Example Probability Calculations and Confidence Intervals

Lecture 24 Probability Method 1 - Kaplan Meier Method 1

Lecture 25 Probability Method 1 - Kaplan Meier Method 2

Lecture 26 Probability Method 1 - Kaplan Meier Method 3

Lecture 27 Probability Method 2 - Diagnostic Test Validation 1 Confusion Table

Lecture 28 Probability Method 2 - Diagnostic Test Validation 2 Interpretation of Results

Lecture 29 Probability Method 2 - Diagnostic Test Validation 3 Interpretation of Results

Lecture 30 Probability Method 2 - Diagnostic Test Validation 4 First Test Probability

Lecture 31 Probability Method 2 - Diagnostic Test Validation 5 Why Test Twice 1

Lecture 32 Probability Method 2 - Diagnostic Test Validation 6 Why Test Twice 2

Section 4: Biostatistical Methods Inference

Lecture 33 Stats 1 - Incidence and Prevelance Rates

Lecture 34 Stats 2 - Incidence and Prevelance Studies

Lecture 35 Stats 3 - Reading Incidence and Prevelance Rates

Lecture 36 Stats 4 - Vizualizations of Incidence and Prevelance Rates

Lecture 37 Stats 5 - Practical Tips For Using Rates

Lecture 38 Stats 6 - Sampling

Lecture 39 Stats 7 - Hypothesis 7 Steps 1

Lecture 40 Stats 8 - Hypothesis 7 Steps 2

Lecture 41 Stats 9 - Hypothesis 7 Steps 3

Lecture 42 Stats 10 - Hypothesis 7 Steps 4

Lecture 43 Stats 11 - Hypothesis 7 Steps 5

Lecture 44 Stats 12 - Paired T-test Example

Lecture 45 Stats 13 - Paired T-test Hypothesis and the Difference Population

Lecture 46 Stats 14 - Paired T-test Test Statistic

Lecture 47 Stats 15 - Paired T-test Assumptions

Lecture 48 Sample Size and Research Bias 1

Lecture 49 Sample Size and Research Bias 2

Lecture 50 Sample Size and Research Bias 3

Lecture 51 Stats 16 - Paired T-test in SPSS 1

Lecture 52 Stats 17 - Paired T-test in SPSS 2

Lecture 53 Stats 18 - Paired T-test in SPSS 3

Section 5: Chi-Squared Test

Lecture 54 Chi-Squared 1

Lecture 55 Chi-Squared 2

Lecture 56 Chi-Squared 3

Lecture 57 Chi-Squared 4

Lecture 58 Chi-Squared 5

Lecture 59 Chi-Squared 6

Lecture 60 Chi-Squared 7

Lecture 61 Chi-Squared No Association

Lecture 62 Chi-Squared 8

Lecture 63 Chi-Squared 9

Lecture 64 Chi-Squared 10

Lecture 65 Chi-Squared 11

Lecture 66 Chi-Squared 12

Lecture 67 Chi-Squared 13

Lecture 68 Chi-Squared 14

Lecture 69 Chi-Squared 15

Lecture 70 Chi-Squared 16

Lecture 71 Chi-Squared 17

Section 6: Cochran-Mantel-Haenszel Test for Stratified Tables

Lecture 72 CMH part 1

Lecture 73 CMH part 2

Lecture 74 CMH part 3

Lecture 75 CMH part 4

Lecture 76 CMH part 5

Lecture 77 CMH part 6

Lecture 78 CMH part 7

Lecture 79 CMH part 8

Lecture 80 CMH part 9

Section 7: Final Part Data Cleaning Writing

Lecture 81 ETL Cleaning Data Files: Making an Analysis File part 1

Lecture 82 ETL Cleaning Data Files: Making an Analysis File part 2

Lecture 83 ETL Cleaning Data Files: Making an Analysis File part 3

Lecture 84 ETL Cleaning Data Files: Making an Analysis File part 4

Lecture 85 ETL Cleaning Data Files: Making an Analysis File part 5

Lecture 86 ETL Cleaning Data Files: Making an Analysis File part 6

Lecture 87 Lecture 81 Paired T-test in SPSS 1

Lecture 88 Lecture 82 Paired T-test in SPSS 2

Lecture 89 Lecture 83 Two Sample T-test in SPSS

Lecture 90 Lecture 84 Chi-Squared Test in SPSS

Lecture 91 Lecture 85 Chi-Squared for a Retrospective Study

Lecture 92 Lecture 87 Cochran Mantel Haenszel Analysis

College Level students looking to get degrees and create careers in Public Health or Biostatistics,College Pharmacy students, Medical School students.,Data Scientists that need more statistics training,Managers that want to be able to analyze processes