Abstract: The bow-mounted quiver is a removable attachment for a conventional archery bow. The bow-mounted quiver includes a mounting plate adapted for releasable mounting on the archery bow, an arm, and a U-shaped retaining member. The arm has first and second arm portions. A first end of the first arm portion is pivotally attached to the mounting plate, and a second end of the first arm portion is pivotally attached to a first end of the second arm portion. The second arm portion may have an adjustable length. The U-shaped retaining member has a central portion and a pair of free ends. The central portion is pivotally attached to a second end of the second arm portion, and each of the free ends defines a recess for releasably retaining a shaft of an arrow.

Abstract: The bow-mounted quiver is a removable attachment for a conventional archery bow. The bow-mounted quiver includes a mounting plate adapted for releasable mounting on the archery bow, an arm, and a U-shaped retaining member. The arm has first and second arm portions. A first end of the first arm portion is pivotally attached to the mounting plate, and a second end of the first arm portion is pivotally attached to a first end of the second arm portion. The second arm portion may have an adjustable length. The U-shaped retaining member has a central portion and a pair of free ends. The central portion is pivotally attached to a second end of the second arm portion, and each of the free ends defines a recess for releasably retaining a shaft of an arrow.

Abstract: Methods and apparatus are disclosed to predict new product performance metrics. An example disclosed method includes selecting a product hierarchy tree based on a distribution measure for a new product, identifying a segment location of the product hierarchy tree for the new product, retrieving a relationship matrix associated with the segment location of the product hierarchy tree, the relationship matrix comprising available product performance coefficients and attribute information, identifying a spatial location for the new product in the relationship matrix based on the attribute information, calculating, using a programmed processor, an impact parameter based on the spatial location within the relationship matrix, and calculating the performance metric for the new product based on the impact parameter and a channel sales value.

Abstract: Methods and apparatus are disclosed to predict new product performance metrics. An example disclosed method includes selecting a product hierarchy tree based on a distribution measure for a new product, identifying a segment location of the product hierarchy tree for the new product, retrieving a relationship matrix associated with the segment location of the product hierarchy tree, the relationship matrix comprising available product performance coefficients and attribute information, identifying a spatial location for the new product in the relationship matrix based on the attribute information, calculating, using a programmed processor, an impact parameter based on the spatial location within the relationship matrix, and calculating the performance metric for the new product based on the impact parameter and a channel sales value.