The functional linear model with scalar response is a regression model where the predictor is a functional random variable and the response is a real random variable defined on the same probability space.

Scalars are quantities that are fully described by a magnitude (or numerical value) alone. Vectors are quantities that are fully described by both a magnitude and a direction.

Scalar, a physical quantity that is completely described by its magnitude; examples of scalars are volume, density, speed, energy, mass, and time. Other quantities, such as force and velocity, have both magnitude and direction and are called vectors.

A scalar variable stores a value with no internal components. The value can change. A scalar variable declaration specifies the name and data type of the variable and allocates storage for it.

A scalar signal contains a single element. The signal could be a one-dimensional array with one element, or a matrix of size 1-by-1. A vector signal contains one or more elements, arranged in a series.

They mean if the expectation is to get a single value (in SCALAR context), or if multiple values are expected (LIST context). In LIST context the number of values can be 0, 1, 2, or any other number.

Using Array Variables in Lists As you’ve already seen, lists can contain scalar variables. For example: @list = (1, $scalar, 3); Here, the value of the scalar variable $scalar becomes the second element of the list assigned to @list.

Scalar property refers to the linear relationship between the estimation error and the estimated duration. In most species investigated, including humans, the estimation error increases linearly with the estimated duration across a large range of intervals (see Gibbon et al., 1997).

Work is a scalar quantity because it is the dot product of two vectors (Force and displacement). Dot product of two vectors becomes scalar quantity. So, work done has only magnitude but not direction. Work done may be positive, negative or zero.