Questions on (linear or nonlinear) regression, the fitting of functions that best approximate empirical data.

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2
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1answer
177 views

Effects of feature scaling on weight vectors for linear regression

Given that linear regression or polynomial regression can be represented as: $\textbf{w} = (X^{T}X)^{-1}X^{T}Y$ It is standard practice in machine learning to scale each column in their training ...
2
votes
1answer
128 views

Machine Learning: Linear Regression models

I'm currently in a course learning about neural networks and machine learning, and I came across these two formulas in this textbook page on linear regression: 1) $y(x) = a + bx$ and 2) $y(x) = ...
2
votes
1answer
51 views

Predicting the increase/decrease of number

I have these entries in my database that looks like this: ...
2
votes
1answer
30 views

Multiple regression model

I have a multiple regression equation which as four quarters (maybe called them as parameters) ...
2
votes
0answers
70 views

Predicting profit with price variation

I am currently working on a high school project that aims to predict profit from X amount of items to Y amount of profit based off a deviated sale price. For instance: I sale 10 cookies for 10 ...
2
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0answers
102 views

Uni-variate Moving Average Theta coefficients

Consider the Uni-variate Moving Average Models (MA models) MA(1) $$x_t = \mu + w_t +\theta_1w_{t-1}$$ or the second order moving average MA(2) $$x_t = \mu + w_t +\theta_1w_{t-1}+\theta_2w_{t-2}$$ ...
2
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0answers
277 views

Normal equations for minimization of Frobenius norm least squares error

I'm having a hard time understanding the most efficient sequence of steps for deriving the normal equations for Frobenius norm least squares minimization. Here I want to minimize the norm of a matrix ...
2
votes
0answers
36 views

Best line fit for correlated points

Given in $\mathbb{R}^3$ are $n$ points $\mathbf{y}_i\sim N(\mathbf{y}_i-\mathbf{\hat{y}}_i, \mathbf{C}_i)$, which are normally distributed. I want to determine a best fit line $\mathbf{u}(\lambda) = ...
2
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0answers
45 views

OLS standard error that corrects for autocorrelation but not heteroskedasticity

Question: By mapping the OLS regression into the GMM framework, write the formula for the standard error of the OLS regression coefficients that corrects for autocorrelation but not ...
2
votes
1answer
55 views

Is there a site that will allow me to calculate a best fit for a set of data?

I have a bunch of x's and their corresponding y values, but do not have a Wolfram Pro account. Is there another site where I can input my dataset and have it spit out a best-fit regression (be it ...
2
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0answers
34 views

Showing Hat matrix equal specific values

Consider a one way layout model $y_{ij}$ = $\mu_i + e_{ij}$ (1 $\leq$ i $\leq$ a, 1 $\leq$ j $\leq$ $n_i$) where a = 3 and $n_1$ = 2, $n_2$ = 3, $n_3$ = 4. Show that the hat matrix for this design ...
2
votes
1answer
62 views

Trigonometric regression

What methods are performed for regression with trigonometric functions? E.g. : Sequence: $$-1, 0, 1, -1, 0, 1, \text{.....}$$ Regression: ...
2
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0answers
106 views

Is it compulsory to make transformation to the econometric model in order to have only diagonal elements on variance-covariance matrix of errors?

I need some sharped and advanced advices for the following issue ... Model and its assumptions I'm working on the methodology of a two-way error component model. Here is the model: $y_{jis} = ...
2
votes
0answers
128 views

What is ${\rm cov}(e_i, \hat y_i)$ in simple linear regression?

The model is $y_i = \beta_0 + \beta_1x_i + \epsilon_i$ What is ${\rm cov}(e_i, \hat y_i)$? What is ${\rm cov}(\epsilon_i, \hat \beta_1)$? What is ${\rm cov}(e_i, \epsilon_i)$? For 1, I am writing ...
2
votes
0answers
36 views

Smallest set of Liner equations, which exactly fit a set of points

I have a set of 2-d points,(it can be of any arbitrary dimension n). I want to find the minimum set of straight lines(linear equations) which exactly passes through the given 2-d points (unlike ...
2
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0answers
217 views

A* vs D* vs Dijkstra [closed]

I understand the basis of A* as being a derivative of Dijkstra, however, I recently found out about D*. From wikipedia, I can understand the algorithm. What I do not understand is why I would use D* ...
2
votes
1answer
110 views

How to calculate $\sum(X_i-\bar{X})^2$ in R

I'm trying to figure out how to calculate $\sum(X_i-\bar{X})^2$ in R, specifically identifying it in either the aov function or $\operatorname{lm}(y\sim x)$ function. I am trying to use it to ...
2
votes
1answer
31 views

Aproximate data with this equation (or linearize the equation)

I have found an equation that describes the behaviour of a phisical system: $$ y=a_1e^{-a_2x} + a_3 + a_4x + a_5e^{{-a_6} / {(1-x)}}$$ Now I have data of that physical system and I want to ...
2
votes
1answer
108 views

Best fit line using geometric distance (not vertical distance)

There must be a theory of finding the best fit line to a bunch of points in the plane, where "best fit" is defined by the geometric distance, not vertical distance. In other words, we are trying to ...
2
votes
0answers
40 views

Sampling data prior to nonlinear regression

As my question shows it, I am not a statistician. My problem is that I have too many data points to be used in a nonlinear fit (I have millions of them, automatically acquired). Is there a methodology ...
2
votes
1answer
60 views

Basic Multilinear regression question for finding examples or counterexamples.

Hello Wise mathematicians! I have few quenstions about Multi linear regresstion. I've been asked from my friend, but I have very weak knowledge background from that field. It seems my friend is in ...
2
votes
1answer
126 views

Why does the regression line of $x$ on $y$ and $y$ on $x$ meet at $\bar{x}$ and $\bar{y}$?

Why does the least squares regression line of $x$ on $y$ and $y$ on $x$ intersect at $\bar{x}$ and $\bar{y}$? Also, why are the form of regression lines as they are? For the general form ...
2
votes
1answer
67 views

Probit model question (regression)

I'm reading a thesis and I need your help to understand the equation below. $$\Pr(\text{failure}=1 \mid X_1,X_3,X_3,X_4)=\int_{-\infty}^z \varphi(k) \, dk\tag{1}$$ $\varphi(k)$ is the standard ...
2
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0answers
1k views

Least Squares “analytic expression” for fitting a 2D quadratic function to measurements

I have n scattered elevation measurements: $ \{x_i,y_i,z_i\}_{i=1..n} $ that I want to fit a quadratic function to: $ z = ax^2 + by^2 + cxy + dx + ey + f$. The problem can be written as a vector ...
2
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0answers
58 views

Computing evidence for least-squares fit

I'm at a loss trying to implement Bayesian model selection for standard least-squares polynomials fits. I have three polynomials of order $1$, $2$, and $3$, and a sequence of $(x,y)$ data points. ...
2
votes
2answers
32 views

Linear regression. Lowering response maintaining equal independent variable.

I have put some data together and modelled the behaviour of the response ($y$) as function of three independent variables $x_1$, $x_2$ and $x_3$. A simple multi-linear regression. The model looks ...
2
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0answers
47 views

Coefficient of determination

$$ \displaystyle \sum^n_{i = 1} (y_i - \bar{y})^2 = ( \displaystyle \sum^n_{i = 1} (y_i - \bar{y})^2 - \displaystyle \sum^n_{i = 1} (y_i - \hat{y}_i)^2 ) + \displaystyle \sum^n_{i = 1} (y_i - ...
2
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0answers
170 views

Orthonormal Matrix weighted regression

$Q$ is a rectangular matrix with orthonormal columns. A linear system composed of $$Qx= b$$ is really easy to solve as: $$Q'Q=I$$ hence: $$x=Q'b$$ Given that $Q$ is orthonormal can this be used to ...
2
votes
0answers
91 views

Regressing $Y$ back on the residuals

Suppose I have the linear regression model $ \hat{y_i} = a + b x_i $ for $a,b$ obtained via OLS. How does one regress $y$ back on the residuals $\hat{e}_i = y_i - \hat{y}_i$? If we write $ ...
2
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0answers
198 views

Effective model for calculating momentum or growth rate for a time series

I have a series of numbers tracking the performance of an entity on any given day. It's nothing but a simple integer for each date. For example, here's a series for Entity "X" ...
2
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0answers
466 views

Bare minimum of points in multiple polynomial regression

I have a question on multiple polynomial regression and the absolute minimum amount of points in the different terms. The minimum amount of points required for a second order polynomial would (in one ...
2
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0answers
99 views

Find $w$ as the minimizer of regularized logistic regression

Could someone point me to a reference on how to find $w$ as the minimizer of: $$ \frac{1}{2}\sum_{i=1}^{d}q_i(w_i-m_i)^2+\sum_{j=1}^{n}log(1+\exp(-y_jw^Tx_j)) $$ where $q_i$ (initialized with ...
2
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0answers
512 views

Logistic regression algorithm in Casio and Texas Instruments calculators

When using logistic regression on a Casio or Texas Instruments calculator, the output is of the form $$f(x) = \frac{c}{1+ae^{-bx}} $$ The problem I have (when teaching in a class where both types of ...
2
votes
2answers
1k views

Least squares estimator of mu

The question is: Assuming that $y_i = \mu + \epsilon_i $,$i = 1,\ldots,n$ with independent and identically distributed errors $\epsilon_i$ such that $E[\epsilon_i] = 0$ and $Var[\epsilon_i] = ...
2
votes
0answers
359 views

Finding a model for multiple non-linear regression

I want to implement a regression analysis, but I have problems with finding a model for the given data. There are $149$ $(x,y,z)$-values. $y$ values are all positive, $x$ is between $[-10, 10]$ and ...
2
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0answers
3k views

Derivation of standard error of beta in simple linear regression

Countless web pages show the equation for the standard error of the slope in a simple linear regression. For example: ...
2
votes
1answer
322 views

Multiple linear regression with interaction

I'm doing a multiple linear regression with interacting variables. I'll give you an example: $y$=value, $x_1$=material, $x_2$=weight, $x_3$=color $x_1$ and $x_2$ are interacting variables but $x_3$ ...
2
votes
0answers
87 views

Polynomial and exponential regression [duplicate]

Possible Duplicate: Determining computational complexity of stochastic processes I have some points $(x_i,y_i)$ generated by a program. These values are not exact, but are random ...
2
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0answers
1k views

Correlation and Regression Question

Two separate tests are designed to measure a students ability to solve problems. Several students are randomly selected to take both tests and the results are: $$ \begin{matrix} \text{Test A}(x) ...
2
votes
0answers
3k views

Help with problem: Estimated Standard Deviation of Regression Equation (Simple Linear)

This is a practice problem. I've solved part (a). I have provided verified answers (from the published key) to all parts (a), (b) and & (c). I need help solving (b) and (c). Consider a simple ...
2
votes
0answers
309 views

Surface Function Fitting to Spherical Data

I have a set of geographic (longitude,latitude,value) data to which I would like to fit surface functions, specifically, the set of quadratic surfaces: $f(x,y)=Ax^2+Bx^2+Cxy+Dx+Ey+F$ At the moment, ...
2
votes
0answers
68 views

Accurate computation for Linear Regression case

I am writing a program that inputs a sequence of points $(x_i,y_i)$ based on the user clicking on certain pixels in an image shown. The program should then find the "best -fitting" line in the least ...
1
vote
5answers
699 views

Find square root approximation function (tool)

first I have to apologize for any uncorrect naming or categorisation of my question, as I am an electrical engineer rather than a mathematican. I try to find a simple solution for my problem: I have ...
1
vote
1answer
55 views

When is $\mathbf{X}^{T}\mathbf{X}+\lambda\mathbf{I}$ invertible?

The question is quite simple: for a $N \times p$ matrix $\mathbf{X}$ with real entries, when is $\mathbf{X}^{T}\mathbf{X}+\lambda\mathbf{I}$ invertible (where $\mathbf{I}$ is the $p \times p$ identity ...
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vote
4answers
389 views

Parabola from 4 approximate points

I have calculated four approximate points from a sensors to get information. I would like to deduce the closest parabola to my points. The problem is that I can't solve it to get an appropriate ...
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vote
3answers
696 views

Simple Least-Squares Regression Question

Given a set of 5 points (i.e. (1, 3), (2, 8) etc...), how can I get just the slope of the best fit line? I've been looking up least squares regression, but I'm rather statistics ignorant and don't ...
1
vote
3answers
54 views

Transpose of $(X'X)^{-1}$

I am taking a Phd class in econometrics, and the following is used constantly, for $X$ a $n\times k$ matrix, $n \neq k$: $$(X'X)^{-1} = ((X'X)^{-1})'$$ with "$'$" standing for transpose. Having a ...
1
vote
3answers
46 views

Question regarding Sum Notation in the least squares formula [closed]

I'm attempting to figure out the difference between Σx^2 and (Σx)^2 in this least squares regression formula http://i.imgur.com/HwxnM28.jpg. Any ideas? I figure there must be a difference.
1
vote
2answers
124 views

Fit exponential with constant

I have data whic would fit to an exponential function with a constant. So y=aexp(bt) + c Now I can solve an exponential without a constant using least square by taking log of y and making the ...
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vote
2answers
203 views

Why divide by $2m$

I'm taking a machine learning course. The professor has a model for linear regression. Where $h_\theta$ is the hypothesis (proposed model. linear regression, in this case), $J(\theta_1)$ is the cost ...