2261 search results for "regression"

Multiple Regression (Part 2) – Diagnostics

January 26, 2017
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Multiple Regression (Part 2) – Diagnostics

Multiple Regression is one of the most widely used methods in statistical modelling. However, despite its many benefits, it is oftentimes used without checking the underlying assumptions. This can lead to results which can be misleading or even completely wrong. Therefore, applying diagnostics to detect any strong violations of the assumptions is important. In the

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Regression model with auto correlated errors – Part 3, some astrology

January 17, 2017
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Regression model with auto correlated errors – Part 3, some astrology

The results of the study are interesting from an astrological point of view. Astrological signs are divided into groups by order. The first grouping is by alternating order, with the first sign (Aries) positive and the next sign negative, the third positive, and so on through the circle. The second grouping is in groups of Related Post

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Multiple Regression (Part 1)

January 15, 2017
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Multiple Regression (Part 1)

In the exercises below we cover some material on multiple regression in R. Answers to the exercises are available here. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page. We will be using the dataset state.x77, which

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Regression model with auto correlated errors – Part 2, the models

January 14, 2017
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Regression model with auto correlated errors – Part 2, the models

In the first part I created the data for testing the Astronomical/Astrological Hypotheses. In this part, I started by fitting a simple linear regression model. mod.lm = lm(div.a.ts~vulcan.ts) summary(mod.lm) Call: lm(formula = div.a.ts ~ vulcan.ts) Residuals: Min 1Q Median 3Q Max -159.30 -53.88 -10.37 53.48 194.05 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 621.23955 Related Post

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Regression model with auto correlated errors – Part 1, the data

January 12, 2017
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Regression model with auto correlated errors – Part 1, the data

The assumptions of simple linear regression include the assumption that the errors are independent with constant variance. Fitting a simple regression when the errors are auto-correlated requires techniques from the field of time series. If you are interested in fitting a model to an evenly spaced series where the terms are auto-correlated, I have given Related Post

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Linear Regression from Scratch in R

January 5, 2017
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Linear Regression from Scratch in R

One of the very first learning algorithms that you’ll encounter when studying data science and machine learning is least squares linear regression. Linear regression is one of the easiest learning algorithms to understand; it’s suitable for a wide array of problems, and is already implemented in many programming languages. Most users are familiar with the Related Post

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More on Orthogonal Regression

December 27, 2016
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Some time ago I wrote a post about orthogonal regression. This is where we fit a regression line so that we minimize the sum of the squares of the orthogonal (rather than vertical) distances from the data points to the regression line.Subsequently, I received the following email comment:"Thanks for this blog post. I enjoyed reading it. I'm wondering...

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Recursive Partitioning and Regression Trees Exercises

December 13, 2016
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Recursive Partitioning and Regression Trees Exercises

Answers to the exercises are available here. Exercise 1 Consider the Kyphosis data frame(type help(‘kyphosis’) for more details), that contains: -Kyphosis:a factor with levels absent present indicating if a kyphosis (a

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Estimate Regression with (Type-I) Pareto Response

December 11, 2016
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Estimate Regression with (Type-I) Pareto Response

The Type-I Pareto distribution has a probability function shown as below f(y; a, k) = k * (a ^ k) / (y ^ (k + 1)) In the formulation, the scale parameter 0 < a < y and the shape parameter k > 1 . The positive lower bound of Type-I Pareto distribution is particularly

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Forecast double seasonal time series with multiple linear regression in R

December 2, 2016
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Forecast double seasonal time series with multiple linear regression in R

I will continue in describing forecast methods, which are suitable to seasonal (or multi-seasonal) time series. In the previous post smart meter data of electricity consumption were introduced and a forecast method using similar day approach was proposed. ARIMA and exponential smoothing (common methods of time series analysis) were used as forecast methods. The biggest disadvantage of this...

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