The Science of Life

As promised last time , I will cover examples of discrete and continuous probability distributions. Top: Discrete Probability Middle: Continuous Probability Bottom: Disjointed Probability Disjointed won't be covered in this course. Discrete Probability Model (example): The following table represents the probabilities of people of certain age groups living alone, living with a spouse, or living with at least one person who is not a spouse. 15-19 20-24 25-34 35-44 Alone 0.001 0.011 0.031 0.030 With Spouse 0.001 0.023 0.155 0.216 With others 0.169 0.132 0.142 0.089 Because there is a finite amount of categories which has a non-zero probability of occurring, this is considered to be a discrete probability model.  Then again, I am assuming that it is a probability model.  Remember from the first lecture th...

Probability is the foundation of everything we do in Statistics.  There are two types of probability: discrete and continuous.  Discrete probability is when there are distinct probabilities for different values (represented by bars), while continuous probability is where the probabilities change smoothly with the value (represented by a smooth curve).  This is better described by images.  These are two types of probability models and are therefor treated different. Discrete Probabilities of Dice Rolls. Continuous Probability Distribution of Pounds of Waste Produced. We have what is called Probability Models.  Probability models have a sample space (denoted by an S).  Probabilities are assigned to sample points in sample space.  For the continuous Standard Normal Distribution, the x axis is the sample point and the y axis (denoted P(E)) is the probability axis.  In the graphs above, the values we want to get a probability for is on th...