A skewed square, often referred to in the context of geometry or data visualization, typically describes a square that has been distorted or transformed such that its angles and side lengths are not uniform. This can create a shape that resembles a square but does not maintain the properties of a true square, such as equal sides and right angles. In data visualization, a skewed square might represent a non-linear relationship or distribution of data points.
They are both quadrilaterals. The both have interior angle of 360 degrees. A rhombus is to a skewed square as a parallelogram is to a skewed rectangle.
as a paralellogram is a skewed square, it is a quadrilateral. it has four sides
The characteristics of the chi-square distribution are: A. The value of chi-square is never negative. B. The chi-square distribution is positively skewed. C. There is a family of chi-square distributions.
A Rhombus is a square tilted to form a diamond shape
The retaining wall is skewed perfectly.
Rhombus
A rhombus is a geometric shape that is a type of skewed square, where all four sides are of equal length but the angles are not 90 degrees.
They are both quadrilaterals. The both have interior angle of 360 degrees. A rhombus is to a skewed square as a parallelogram is to a skewed rectangle.
as a paralellogram is a skewed square, it is a quadrilateral. it has four sides
It fits the description of a rhombus which is a 4 equal sided quadrilateral that is skewed over
The characteristics of the chi-square distribution are: A. The value of chi-square is never negative. B. The chi-square distribution is positively skewed. C. There is a family of chi-square distributions.
A Rhombus is a square tilted to form a diamond shape
i) Since Mean<Median the distribution is negatively skewed ii) Since Mean>Median the distribution is positively skewed iii) Median>Mode the distribution is positively skewed iv) Median<Mode the distribution is negatively skewed
The retaining wall is skewed perfectly.
To determine if the data in a line plot is skewed left, right, or not skewed, you would need to observe the distribution of the data points. If the tail on the left side is longer or fatter, it is left-skewed; if the tail on the right side is longer or fatter, it is right-skewed. If the data points are evenly distributed around a central value, it is not skewed. Without seeing the actual plot, I can't provide a definitive answer.
As the mean is greater than the median it will be positively skewed (skewed to the right), and if the median is larger than the mean it will be negatively skewed (skewed to the left)
Due to systematic error, my results are skewed.