Answer:
1. 15%
2. 85%
Step-by-step explanation:
The graph is 100 units,
the shaded part is 15 units,
so it is 15%,
if it is 15% than the unshaded part is 85%.
If you want to solve this problem using formulas, there are two important formulas:
t1 = first term = -5
tn = nth term = last term = -5
n = numbr of terms
Sn = sum of the n terms
tn = t1 + (n - 1)d ---> 65 = -5 + (n - 1)(5)
65 = -5 + 5n - 5
65 = -10 + 5n
75 = 5n
n = 15
Sn = n(t1 + tn)/2 ---> Sn = 15(-5 + 65)/2
Sn = 450
So ur answer rounds up to 450
Letter c
:)
hope i helped
~Luis
Answer:
48.
Step-by-step explanation:
To find the missing number write out the equation :
67 + 65 + ? = 180.
67 + 65 = 132
To find out how much more we need you can minus 132 from 180.
180 - 132 = 48.
Plug it in to see if it works.
67 + 65 + 48 does equal 180.
Hope this helps,
Davinia.
Answer:
1/30
Step-by-step explanation:
6 white marbles
5 red marbles
+ 19 marbles of other colors
-----------------------------------------------
30 marbles in total
First drawing:
p(white) = (number of white marbles)/(total number of marbles)
p(white) = 6/30 = 1/5
Second drawing:
Since the first marble is replaced, there are still 30 marbles in the bag.
p(red) = (number of red marbles)/(total number of marbles)
p(red) = 5/30 = 1/6
The two drawings are independent events, so the overall probability of the two events is the product of the individual probabilities.
p(white then red) = p(white) × p(red)
p(white then red) = 1/5 × 1/6
p(white then red) = 1/30
The most common method for fitting a regression line is the method of least-squares. This method calculates the best-fitting line for the observed data by minimizing the sum of the squares of the vertical deviations from each data point to the line (if a point lies on the fitted line exactly, then its vertical deviation is 0). Because the deviations are first squared, then summed, there are no cancellations between positive and negative values.Example<span>The dataset "Televisions, Physicians, and Life Expectancy" contains, among other variables, the number of people per television set and the number of people per physician for 40 countries. Since both variables probably reflect the level of wealth in each country, it is reasonable to assume that there is some positive association between them. After removing 8 countries with missing values from the dataset, the remaining 32 countries have a correlation coefficient of 0.852 for number of people per television set and number of people per physician. The </span>r²<span> value is 0.726 (the square of the correlation coefficient), indicating that 72.6% of the variation in one variable may be explained by the other. </span><span>(Note: see correlation for more detail.)</span><span> Suppose we choose to consider number of people per television set as the explanatory variable, and number of people per physician as the dependent variable. Using the MINITAB "REGRESS" command gives the following results:</span>
<span>The regression equation is People.Phys. = 1019 + 56.2 People.Tel.</span>