3

Generally this is accomplished (i.e. simplified) using matrix notation. Let $X$ be a matrix whose rows are your data such that the columns are your dimensions. Further, let $y$ be a vector of your response variable and $\beta$ be a vector of the regression coefficients (i.e. what you are solving for. Append a column of 1's to the left hand side of the ...


3

Terms like "real name" presuppose more about terminology than is justified. No committee or supervising genius lays down the law ineluctably on correct names. But "time line" is a common name. The splendid scholarly survey by Rosenberg and Grafton (see reference here) shows that its pedigree is centuries long (and, for what it's worth, antedates William ...


3

The formula is actually pretty straight forward. Consider the table from your question. The table states that there were a total 90 observations Out of all 90 observations: 76 times, no pizzas or squirrel 9 times, pizzas but no squirrel 4 times, squirrel but no pizzas 1 time, both squirrel and pizza So we want to find out how Pizzas & Squirrels ...


2

Considered transforming your data to fit into the [0,1] interval. For the $\operatorname{sin}(x)$ case, $\frac{ \operatorname{sin}(x) + 1}{ 2 }$ will fit into [0,1] for any $x$.


2

It's not completely clear what the output of your code will be in this case. The Singular Value Decomposition of a real-valued $m$ x $n$ matrix M is a factorisation of the form M = U D V' where U is $m$ x $m$, D is $m$ x $n$ and V is $n$ x $n$. The diagonal entries {$d_{ii}$} of D are the "singular values" of M. If your code provides U,D and V as output, ...


1

$$\mathsf{Chisq}(\text{df} = \nu) \equiv \mathsf{Gamma}(\text{shape}=2\nu,\, \text{rate}=1/2).$$ Thus in R code: # PDF pgamma(3, 5, 1/2); pchisq(3, 10) [1] 0.01857594 [1] 0.01857594 # quantile function (inverse CDF) qchisq(.5, 10); qgamma(.5, 5, 1/2) [1] 9.341818 [1] 9.341818 # random sampling set.seed(1234); x = rchisq(10^6, 10) mean(x); var(x) [1] ...


1

If you didn't find one, it probably doesn't exist. But you can always try translating the Python or Perl one into JavaScript.


1

It sounds like you have a distribution, and want to turn it into a point estimate. The mean is certainly one way to do this, but there are many others. To decide which method is best, you need to specify an error measure on the estimate, otherwise known as a loss function. If the error measure is squared error, then the mean is the optimal point estimate. ...


1

What you are looking for is change point detection. There is an extensive literature on this topic, and there are freely available software packages for performing this type of analysis. For example, see this paper on the R package changepoint which includes a brief survey of the literature, along with examples demonstrating how to perform this kind of ...


1

Draw a graph of differences between observations. If your plot is $x_t$, then it would be $\Delta x_t=x_t-x_{t-1}$. You'll see that it'll have a big spike in the middle. If you calculate the standard deviation before and after the spike, you'd see that it's similar. You'll see that the magnitude of the spike is 2-3 times higher than of these standard ...


Only top voted, non community-wiki answers of a minimum length are eligible