Logistic Regression: Predicting The Chances Of Coronary Heart Disease
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Logistic regression is used to predict the probability of an event occurring based on multiple predictor variables. This document discusses using logistic regression to predict the risk of coronary heart disease based on factors like smoking, cholesterol levels, body mass index, gender, age, and physical activity. It finds that smoking and high cholesterol levels are the highest risk factors for heart disease. The odds ratio is used to interpret the results, showing smokers have a 2.4 times higher risk than non-smokers. Examples estimate an inactive smoker's risk at 18% over 10 years, versus practically no risk for a healthy older man.
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Logistic Regression: Predicting The Chances Of Coronary Heart Disease
- 1. Logistic Regression Predicting the Chances of Coronary Heart Disease Multivariate Solutions
- 5. Logistic Regression Output This slide is descriptive, and shows which of the variables are most influential in determining which risk factor is most relevant when considering Coronary Heart Disease. For example, smoking and a total cholesterol level above 200 are the highest risk factors. When examining the results, the Odds-Ratio is often used to interpret the results. Smokers' risk of developing coronary heart disease is 2.4 times that of nonsmokers. High cholesterol is also a risk factor, as is age. That men are slightly more likely to get Coronary Heart Disease than women, and that physical activity sharply reduces the chances of Coronary Heart Disease (negative coefficient).
- 7. Example One Inactive, Smoking, 55-year-old Woman A slightly obese, 55-year-old woman, smoker, with somewhat high total cholesterol and is physically inactive has an 18% chance of contracting Coronary Heart Disease within the next ten years.
- 8. Example Two Health-Conscience 65-Year-Old Man Using the logistic output, the chances of a non-smoking, physically active 65-year-old man with a good cholesterol level has practically no chance of contracting Coronary Heart Disease in the next ten years.
Notes de l'éditeur
- 1 National Jewish Outreach Hebrew Reading Crash Course 1
- Logistic Regression Analysis was introduced by the US physicist, Joseph Berkson
- This second example is quite different in nature. And the difference lies in the characteristics of the variables we’re using. We want to explain the outcome, i.e.: passing or failing the exam with our explanatory variable: the number of hours of study. In this case, the dependent variable can take just two possible values: 1 if the student passes, 0 if the student fails. We will call this type of variable ‘dichotomic’ or ‘binary’ variable.
- Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.
- Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.
- Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.
- Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.
- Probability models have become very popular in applied statistics. They are used to model the probability of a given event occurring. In our example, we are interested in modelling the probability of the student passing the exam. Other examples could be to model the probability of an individual participating in a general election, or the probability of investing in R&D. The use of probability models raises interesting issues. Some of them will be seen here, and some of them are going to be overlooked.