Answer:
A
Step-by-step explanation:
X times x is x squared then x times - 2 is - 2x but 4 times x is 4x. So 4x plus - 2x is 2x. Then 4 times - 2 is - 8 put that all together you'll get x^+ 2+2x-8
There is no hard and fast rule to select the class width. It largely depends on our application.However, one thing that should be kept in mind is that the number of classes should neither to be too low nor too high. So keeping this thing in mind, the class width is select.
The range of the data is = Maximum- Minimum = 96 - 11 = 85
10 classes will be most suited for this data.
The class width for each data can be calculated as:
Class Width = Range / Number of Classes = 85/10 = 8.5
Class width is always rounded to nearest next integer. So the class width will be 9 in this case.
So, the best value of class width or interval width for the given data will be 9.
Answer:
The distance between the ice cream shop and Joe's house is the same as the distance between the ice cream shop and the park. So Joe is not closer to the park or his house, he is in the middle.
Step-by-step explanation:
We consider that the ice cream shop is the zero value in the number line. We assume that going north is positive (right side of the number line) and going south is negative (left side of the number line).
The house position is 10 block north of the ice cream shop, so it is represented by the integer 10.
The park is 10 blocks south of the ice cream shop, so it is represented by the integer -10.
The lower absolute value of the integer, the closer position from the ice cream shop.
If we calculate the absollute value of the House position:
|10|=10
Then, we calculate the the absollute value of the Parkposition
|-10|=10
In conclusion, the distance between the ice cream shop and Joe's house is the same as the distance between the ice cream shop and the park. Joe is in the middle.
5(x + y) - 3(x - y)
5x + 5y - 3x + 3y
5x - 3x + 5y + 3y
2x + 8y
5(x + y) - 3(x - y) = 2x + 8y
Answer:
A. linear
Step-by-step explanation:
A linear model is the best-fitting regression model for the data plot because for similar variations in x values, the variation in y values are also similar, that is, if you take points (x1, y1), (x2, y2) and (x3, y3) the y1 - y2 is similar to y2 - y3, when x2 - x1 is also similar to x3 - x2.