Logistic Regression in R and Excel Год выпуска: 2017 Производитель: Lynda Сайт производителя: lynda.com/Office-365-Mac-tutorials/Learning-Logistic-Regression-R-Excel/540348-2.html Автор: Conrad Carlberg Продолжительность: 1:37 Тип раздаваемого материала: Видеоурок Язык: Английский Описание: Business decisions are often binary: take on this project or put it off for a year; extend credit to this customer or insist on cash; open a new retail outlet in a particular location or find another spot. When an outcome is a continuous variable such as revenue, ordinary regression is often a good technique, but when there are only two outcomes, logistic regression usually offers better tools. Learn how to use R and Excel to analyze data in this course with Conrad Carlberg. He takes you through advanced logistic regression, starting with odds and logarithms and then moving on into binomial distribution and converting predicted odds back to probabilities. After this foundation is established, he shifts the focus to inferential statistics, likelihood ratios, and multinomial regression. Conrad's comprehensive coverage of how to perform logistic regression includes tackling common problems, explaining relationships, reviewing outcomes, and interpreting results. Узнайте, как использовать R и Excel для анализа данных в этом курсе с Конрадом Карлбергом. Он проводит вас через продвинутую логистическую регрессию, начиная с коэффициентов и логарифмов, а затем переходя к биномиальному распределению и превращая предсказанные шансы обратно в вероятности. Конрад всесторонне освещает, как выполнять логистическую регрессию, включая решение общих проблем, объяснение отношений, обзор результатов и интерпретацию результатов.
Содержание
00 - Introduction Welcome What you should know Exercise files 01 - Ordinary Regression and Nominal Outcome Variables The normality assumption Recognize abnormal distribution Forecast: Too high or too low Manage different slopes 02 - Solutions to Problems with Ordinary Regression Use of odds instead of probabilities Use of odds to limit the probabilities on the upside Logs: exponents, bases, sum of logs, and the log of products Use of log odds to limit the probabilities on the downside Predict the log of the odds, the logit 03 - Running a Logistic Regression in Excel Set up the worksheet: Original data and logistic regression coefficients Set up the logit column, the antilog column, and the probability column Establish the log likelihood and run Solver Interpret -2LL or deviance 04 - Running a Binomial Logistic Regression in R Install the mlogit package Establish the data frame with XLGetRange The mlogit function syntax Use of glm instead of mlogit 05 - Running a Multinomial Logistic Regressions in R Deal with problems introduced by three or more possible outcomes Identify long versus wide data frames The special mlogit syntax 06 - Conclusion Next steps Exercise Files
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