Questions tagged [sums-of-squares]
sum of squares plays an important role in statistical models based on the normal distribution, like ANOVA.
173
questions
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How are terms in SS (corrected sum of squares) independent?
What does the highlighted test mean? How are the terms not independent? Can someone elaborate in detail?
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5 views
Do LS-means correspond only to type 3 (marginal) ANOVA or do they match the type 2 too?
I understand, that sequential type 1 ANOVA corresponds to unadjusted, raw (data-based) arithmetic means, and marginal type 3 ANOVA corresponds to LS-means (model-based predictions). In case of no ...
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0answers
9 views
ANCOVA $\eta^2$ definition
I am struggling to find references to understand what is the exact definition of $\eta^{2}$ is two way anova.
Let's say I am estimating the following ANCOVA model:
$$
y_{ijk} = \mu + \alpha_i + \...
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1answer
27 views
Source for BIC with residual sum of squares?
Wikipedia states that under certain circumstances, $\mathrm{BIC} $ can be calculated as:
$$\mathrm{BIC} = n \ln(\mathrm{RSS}/n) + k \ln(n),$$
where $\mathrm{RSS}$ is the residual sum of squares. ...
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1answer
60 views
Kruskal–Wallis one-way analysis of variance is related to what kind of regression?
One-way anova is similar to regular linear regression because both use the F-test which involves sums of squares among other reasons.
Is Kruskal–Wallis one-way analysis of variance similar to some ...
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3answers
465 views
Residual sum of squares in a regression
I understand that in a linear regression model, the residual sum of squares will either remain same or fall with the addition of a new variable.
What if the two models were
$$
I \colon y_i=\...
2
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2answers
66 views
Simplifying the Matrix Form of the Solution to Ridge Regression
I'm trying to understand how to obtain the solution to an objective function by solving for the parameter vector $\theta$ in ridge regression. I found an example here from Naomi which takes an example ...
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0answers
222 views
What is the correct implementation of BIC with residual sum of squares?
BIC is most often calculated by maximizing the log likelihood function. However, it is also possible to calculate BIC with residual sums of squares. This is pretty easy to find online and not an issue ...
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0answers
92 views
Formula for type III sum of squares of the intercept term in linear multiple regression
assume we have the regression model:
$$Y = b_0 + b_1 x_1 + \dots + b_k x_k + \varepsilon $$
I know the formulas for all type III sum of squares for the regression terms except the formula for SS of ...
4
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2answers
108 views
Coefficient of determination relationship?
$R^2 = \frac{SSREG}{SSTOT}$ or $R^2 = 1-\frac{SSRES}{SSTOT}$
If $X$ is the predictor random variable for science SAT
and $Y$ is the predictor random variable science GPA
given by equation $$\hat Y =...
2
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1answer
291 views
Sum of Squared Error Chi-Square distribution degree of freedom in Multilinear Regression
In this link it says that $Y$ variables has zero covariance (because covariance matrix has only diagonal terms) which implies they are independent.
Actually in linear regression $Y$ takes its ...
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1answer
329 views
What is the difference between the residual, lack of fit and pure error In F test for Regression Analysis?
What is the difference between the residual, lack of fit and pure error and how to calculate each of them in ANOVA F test for nonlinear regression?
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1answer
58 views
Two way ANOVA, no difference between tests based on type I vs type II sums of squares
I'm learning about two way ANOVA possibly with interaction.
I'm following this tutorial http://www.sthda.com/english/wiki/two-way-anova-test-in-r
This is their code.
...
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1answer
10k views
Calculating SSE in R
I'm following the "Intro to Statistics" course in Data Camp and I'm having some trouble as it seems that the course is contradicting itself: https://s3.amazonaws.com/assets.datacamp.com/production/...
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46 views
Why is regression line represented as $y = b0 + b1 * x$?
I am new to Data Science and ran into Regression Line formula which is
$ y = b0 + b1 *x $ (where x is dependent variable, y is predicted variable)
I understood the meaning of this formula as a ...
0
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1answer
236 views
Better to Minimize Absolute Error or Sum of Squared Error?
I have an Excel model which predicts the number of customers for a given month. The prediction depends on a churn rate. I have the absolute error (actual vs predicted), along with squared error and ...
2
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1answer
985 views
Why error sum of squares has n-2 df (possibly not duplicate, please read on)? (Regression Question Series - Part 4)
In simple linear regression, the error sum of squares is given by
$$
\text{SSE} = \sum_{i=1}^n(y_i - \hat{y_i})^2 \\
\hat{\sigma}^2 = s^2 = \dfrac{\text{SSE}}{n-2}
$$
where $n-2$ is the degrees of ...
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1answer
77 views
Minimize Logged Sum of Squares?
When numerically maximizing the likelihood function it is standard practice to do this indirectly by minimizing the negative log-likelihood. When numerically minimizing the residual sum of squares (...
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0answers
83 views
R: anova(lm): What is the Sum Sq when we have two inputs
What is the formula to calculate the Sum sq column for the inputs?
Answers to some other questions state, that it should be
$RSS = \sum (\hat Y_i -\bar Y)^2$,
yet this is false, it only gives the ...
2
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2answers
399 views
Showing that $\sum_{i=1}^n (y_i-\hat{y_i})(\hat{y_i} - \bar{y}) = 0$ for the generalized linear model [closed]
Exercise :
Prove that for the generalized linear model, it is :
$$\sum_{i=1}^n (y_i-\hat{y_i})(\hat{y_i} - \bar{y}) = 0$$
Question : How would one proceed with proving that for the generalized ...
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1answer
50 views
Squaring floats between -1 and 1 reduces sum of squares, so why do it? [duplicate]
I have been learning basic statistical testing as it relates to agriculture and have become familiar with the common practice of summing squared raw deviation values, whether in something simple like ...
2
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2answers
137 views
Calculating F-statistic, why is SS used instead of just $r^2$?
The calculations and question are for a simple regression (one independent and one dependent variable).
SSres = SSy * (1 - $r^2$)
SSreg = SSy * $r^2$
F = (SSreg / DFreg)/(SSres / DFres)
So, I've ...
1
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1answer
203 views
Understanding ANOVA as regression / type III SS in R
I'm confused about how type III SS are calculated for a "main effect". According to what I have read, Type III SS is calculated by evaluating the change in the SSE by removing only the variable in ...
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1answer
797 views
2-way ANOVA in Python statsmodels yields different Sum of Squares than SPSS
I am learning to use Python for my statistical analyses, and while figuring out how to perform a 2-way ANOVA with statsmodels I found that my Python code yielded slightly aberrant values. Comparing ...
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0answers
1k views
What's the difference between the SS in the variance and the TSS?
I'm trying to understand how these two statistics differ.
My understanding is the variance is the sum of squares of the predictor divided by the degrees of freedom.
On the other hand, the sum of ...
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0answers
84 views
In regression, when partitioning SS among predictors, what determines which predictors get the SS that can be attributed to more than one predictor?
In regression analysis, predictors sometimes correlate (and in my field, psychology, they always do; often because they are simply measurements of the same aspects of human psychology).
If predictors ...
4
votes
2answers
793 views
How should Type II SS be calculated in a mixed model?
I have a data set (and corresponding mixed model) which gets very different p-values for one of the two-way interactions when tested using Type I (sequential, taking care that it's last), and Type II (...
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0answers
30 views
Deriving the F-test from ${{SSE_R-SSE_F}\over{(n-q)-(n-p)}}/{{SSE_F}\over{n-p}}$
Given a Full and Reduced model, the F-test to see if the reduced model is significant is given by
$$
{{SSE_R-SSE_F}\over{(n-q)-(n-p)}}/{{SSE_F}\over{n-p}}
$$
I'm trying to understand how this is ...
2
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0answers
89 views
Find the Sum of Squares and using them complete an F-test for goodness of fit
I have this question ( would be grateful if someone could put it on for me)
https://gyazo.com/5afe5d7c9d12acdff8f9c55db74f97c9
And I am concerned with part b).
I know what $RSS = SSE$ is due to the ...
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1answer
1k views
Which one to choose Type-I, Type-II, or Type-III ANOVA? [duplicate]
I don't understand what the difference is between TypeI and TypeIII?
Since my background is not very mathematics, it's very difficult for me to understand this mathematical notation;
Type I SS: SS(A) ...
0
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1answer
167 views
Does forward model selection by $R^2$ or SSR differ?
I'm trying to implement forward selection and need to add a feature only if it will make the sum of squared residuals (SSR) lower.
I am using Python's statsmodels ...
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0answers
53 views
The Sums of squares in 3 way Anova in R output has changed as the order of the variables in the modes changes [duplicate]
I am trying to fit a 3 way ANOVA using R. Then i realized the sums of squares of the output has changed when the order of the variables in the model are changed.Can anyone figure out the reason for ...
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2answers
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Mixed-effect model single term deletion — should I change my random effects?
In short
I recently had a little conversation on the lme4 project's GitHub on how to properly test the significance of effects in a mixed-effect model, which made ...
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0answers
184 views
Test significance using Type II ANOVA following Type III ANOVA?
I hope you can help me with a theoretical question about how to proceed in my analysis. I have come across many posts discussing differences between Type II and III SS Anovas, however, because I don't ...
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0answers
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Trying to understand hierarchical testing of nested regression models
I've learned multiple regression, but never did I learn hierarchical regression (i.e., the hierarchical testing of nested regression models) before.
Based on the image attached, I assume because the ...
3
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1answer
680 views
In regression analysis, How can R^2 represent the total explained variance, if it can be computed from an equation with only unique contributions?
Background
In regression analysis, $R^2$, the squared multiple correlation, represents the proportion of explained variance by the regression model. Most software's default setting uses Type-III sums ...
5
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1answer
2k views
Is Square Root of the Variance of a Regression Coefficient the Standard Error?
Quick question, in the textbook "Introductory Econometrics", the variance of a Regression Coefficient is given as:
$var(\hat\beta_j) = \frac{\sigma^2}{SST_j(1-R_j^2)}$
where,
$SST_j$= $\sum_{i=1}^n (...
2
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1answer
97 views
Why do type III sums of squares require orthogonal contrasts?
I have read many times that one has to set orthogonal contrast to get correct type III sums of square. E.g. John Fox says
To compute Type-III tests using incremental F-tests, one needs
contrasts ...
0
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1answer
92 views
Obtaining Information criterion or log likelihood from a model's squared error
I am concerned with the formulas I've seen for the AIC or BIC when using a squared-errors instead of likelihood.
On the AIC wikipedia page, there is the cryptic formula for LL:
$ -{\frac {n}{2}}\ln(...
0
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1answer
417 views
Is equation for total sum of squares always valid?
I wanted to do simple calculations for sum of squares for linear regression using a very simple example.
1) Let's assume that we have 3 obervations [x,y]: [[1,1], [2,2], [3,3]].
2) I created simple ...
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0answers
32 views
How to calculate the mean and standard deviation of weighed Noncentral chi distribution?
I would like to calculate the mean and standard deviation of :
Dist=(X^2+Y^2+Z^2)^0.5
Knowing that X, Y and Z are normal noncentral and nonstandard variables. they have a mean different from 0 and ...
4
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2answers
429 views
Motivation for Ward's definition of error sum of squares (ESS)
Ward (1963) provides a commonly used criterion for hierarchical clustering. It's based on the following definition (p. 237):
Given a set of ratings for 10 individuals, $\{2, 6, 5, 6, 2, 2, 2, 0, 0, ...
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Basics relative to chi square, likelihood, fits,
I'm confused to separate all the different meanings and connections.
The background of my question: On the one hand related to lmer models and on the other hand to the goodness of a fit. And their ...
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1answer
180 views
How is SSerror calculated in a factorial design?
I have found that
SSerror = SStotal - SStreatment
which makes sense to me alongside an example of a one-way ANOVA, however, how does this apply to a factorial ANOVA?
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605 views
Sum of Squares in Randomized Complete Block design
I'm interested in the Randomized Complete Block design (RCB) with
three treatments, which I call $A$, $B$, and $C$,
and four blocks ($b=4$):
\begin{align}
\textrm{block 1: } (y_{A,1}; y_{C,1}; y_{B,...
4
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1answer
125 views
What is the physical intuition behind the equality $\sum_i (x_i - \bar x)^2 = \sum_i (x_i - \bar x) x_i$?
Suppose that $x_1,\ldots,x_n$ are real numbers, and let $\bar x$ denote the average $\frac{\sum_i x_i}n.$ I know how to prove on paper that the equality
$$\sum_i (x_i - \bar x)^2 = \sum_i (x_i - \bar ...
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0answers
117 views
ANOVA Type II vs Type II SSq for multiple factors
I have pondered type II vs type III sums of squares for some time and looked at posted examples of calculations for 2 main factors (e.g. this very nice page). That is, in the case of a model:
X = A + ...
0
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1answer
36 views
What is the total sum of squares for these points? [closed]
y = (-2,2,3,4) and x = (3,5,8,12). Lin. Model is y = -2.5 + 0.6x
Please explain how you got the answer. Thank you!
I am confused on whether I should be calculating based on the x or y values, or ...
3
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0answers
3k views
Generalized eta squared
Partial eta-squared are very often used in psychological litterature. As underlined by some authors (e.g., Baguley, 2009; Bakeman, 2005; Olejnik & Algina, 2003), this standardized measure of ...
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387 views
Distribution of $(n-2)MSres/\sigma^2$ in simple linear regression
In simple linear regression, it was proved that $(n-2)MS_{RES}/\sigma^2$ follows a $\chi^2_{n-2}$ distribution.In order to prove this,the fact that $e_i$ or $y_i - \hat{y_i}$ follows a Normal ...
