Step-by-step explanation:
the side lengths of the base (the ground triangle) are equal. that means every ground side length is 8 in.
and that also makes the 3 triangles of the side areas of the pyramid equal.
so, for the surface area of the pyramid we need to calculate the area of one side area triangle, multiply it by 3 (as we have 3 side areas) and add the area of the ground triangle.
the area of a triangle is
baseline × height / 2
so, for a side area triangle we get
8 × 14 / 2 = 8×7 = 56 in²
all 3 side areas together are then
3×56 = 168 in²
the ground area triangle is then
8 × 6.9 / 2 = 4 × 6.9 = 27.6 in²
so, the total surface area of the pyramid is
168 + 27.6 = 195.6 in²
The adequate intake (AI) for potassium is 4,700 mg in healthy individuals, but unfortunately, most people don’t get enough potassium through their diets. one medium-sized banana typically containing 422 mg. One half of an avocado (100 grams) contains 487 mg of potassium. One cup (156 grams) of frozen spinach contains 540 mg of potassium. One medium-sized sweet potato contains 541 mg of potassium. Just two wedges of watermelon (about 1/8 of a melon or 572 grams) will give you 640 mg of potassium. Three tablespoons of tomato paste or about 50 grams contain 486 mg of potassium. Pomegranates are fantastic source of potassium, as one fruit can bestow 666 mg. One potato can provide 515 mg of potassium.
I hope it helps
Answer:
Discarding the influential outlying cases when detected is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset
Step-by-step explanation:
In a regression class ; If extremely influential outlying cases are detected in a Data set, discarding this influential outlying cases is the right way to go about it
Discarding the influential outlying cases when detected is also known as flagging outliers in a data set, and this is because outliers do not follow the rest of the dataset's pattern. if this outliers are not discarded they would have a negative effect on any model attached to the dataset