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**STATISTICS Linear Statistical Models**

Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University

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**The Method of Least Squares**

Consider the data shown in the following table and figure. We are interested in fitting a straight line to the points in order to obtain a simple mathematical relationship for runoff and rainfall. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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Intuitively, we want that, for each observed value of rainfall, the corresponding value of runoff will be as close as possible to the observed value. It is equivalent to say that we want the vertical deviations to be as small as possible. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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One method of constructing such a straight line to fit the observed data is called the method of least squares. It requires the sum of the squares of the vertical deviations of all the points from the fitted line to be a minimum. Let the rainfall and runoff data in the above figure be respectively represented by x and y. The fitted line is expressed by 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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Remarks 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Given a value of x, what dose the predicted value of y really represent?**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Given a value of x, what dose the predicted value of y really represent?**

It is unlikely that the predicted value will be the same as the observed value at all times. It may even be possible that the predicted value is the same as the observed value only in very few cases. In some cases, the predicted values are far different from observed values. We are sure that the linear model may overpredict or underpredict the observed values. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Linear statistical model**

We are not able to predict y without errors due to existence of the random component. If a phenomenon is stochastic in nature, it cannot be predicted without errors. Random component 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Coefficient of determination**

How well does the least squares line explain the variation in the data? The coefficient of determination represents the proportion of data variation that can be explained by the linear regression model. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Estimating the variance of Y|x**

RSS (Residual sum of squares) = SSE (sum of squared errors) Note: The variance of Y|x is NOT the same as the variance of Y. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Unbiasedness of the least squares estimators**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Confidence intervals of the regression coefficients**

Pivotal quantities 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Hypothesis tests for regression coefficients**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Simple linear regression using R**

Useful material Chapter 11 of Introduction to Probability and Statistics Using R (G. J. Kerns) is highly recommended. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Defining linear regression models**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Conducting regression**

lm(y~model) 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Other useful commands 2017/3/27**

Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**For prediction (x values not observed)**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Graphing the Confidence and Prediction Bands**

You may want to change it. For example, data.frame(x=seq(20,30,by=0.5)) 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Confidence and prediction intervals**

Line of prediction. It represents the estimated conditional expectation of y given x. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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Multiple regression The following slides are provided for your reference only. Due to the time constraint, they will not be covered in this class. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Now let’s consider fitting a linear function of several variables**

Now let’s consider fitting a linear function of several variables. Suppose that we have the following data set: 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**The Linear Regression Model**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Covariance and Correlation Coefficient**

Suppose we have observed the following data. We wish to measure both the direction and the strength of the relationship between Y and X. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**The Analysis of Variance (ANOVA)**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Given X, Y’s are independent normal random variables, i.e.,**

The residual sum of squares (or sum of squared errors, SSE) is expressed by 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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The total sum of squares corrected for the mean is referred to as the total variation. This total variation is split up in two parts: the regression part (SSRm) “explained by the model”, and the residual part (SSE). 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**The ratio is known as the coefficient of determination. **

If the coefficient of determination is large then the model provides a good fit to the data. It also represents the part of the total variation which is explained by the model. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Properties of the Estimators**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**Confidence Intervals 2017/3/27**

Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**The 100(1 – )% confidence interval of 2 is**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**(n–p) degree of freedom.**

However, the true value of is unknown, the above equation can not be used to establish the confidence interval of . We then use s to substitute and it is known that has a t-distribution with (n–p) degree of freedom. 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**The 100(1 – )% confidence interval of is**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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Example 1 A scientist carries out an experiment on the relationship between the yield Y of a crop and the amount of irrigation water X. It is believed that the relationship between expected yield and amount of irrigation water (ignore the units) can be described adequately as 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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**The data shown in the following table were collected in the field.**

2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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Example 2 Data in the following table are rainfall (x) and runoff (y) measured during the rainy season in a study area. A regression model is postulated for the above data 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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Test of Hypotheses 2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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2017/3/27 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Engineering, NTU

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