Answer:
31.25 or 31 1/4 cups
Step-by-step explanation:
1 1/4 = 36
x = 900
=> 5/4 = 36
x = 900
Cross multiply
=> 5/4 * 900 = 36 * x
=> 4500/4 = 36x
=> 1125 = 36x
=> 1125 / 36 = 36x / 36
=> 31.25 = x
31.25 = 31 1/4
So, 31.25 or 31 1/4 cups are required for 900 cupcakes.
Answer:
Part 1: There are 4.7*10^21 ways to select 40 volunteers in subgroups of 10
Part 2: The research board can be chosen in 32760 ways
Step-by-step explanation:
Part 1:
The number of ways in which we can organized n elements into k groups with size n1, n2,...nk is calculate as:

So, in this case we can form 4 subgroups with 10 participants each one, replacing the values of:
- n by 40 participants
- k by 4 groups
- n1, n2, n3 and n4 by 10 participants of every subgroups
We get:

Part 2:
The number of ways in which we can choose k element for a group of n elements and the order in which they are chose matters is calculate with permutation as:

So in this case there are 4 offices in the research board, those are director, assistant director, quality control analyst and correspondent. Additionally this 4 offices are going to choose from a group of 5 doctors.
Therefore, replacing values of:
- n by 15 doctors
- k by 4 offices
We get:

Step-by-step explanation:
The probability of success = 8/(8 + 17) = 8/25 = 0.32.
Let X be the random variable denoting the number of successes (number of times the individual won a prize) in four picks.
Hence, X ~ Bin(4, 0.32).
Thus, P(X = 1) = 
Answer:
(0,-7)
Step-by-step explanation:
If nay point is form (x,y)
x is abscissa can be also called x axis coordinate
y is ordinate can be also called y axis coordinate
ordiantes are points lying on y axis.
For any point lying on y axis, its x-axis coordinate will be 0
given that ordinate is -7. it means that value of y coordinate is -7
Thus, coordinates of the point is (0,-7)
Answer:
Solution-
We know that,
Residual value = Given value - Predicted value
The table for residual values is shown below,
Plotting a graph, by taking the residual values on ordinate and values of given x on abscissa, a random pattern is obtained where the points are evenly distributed about x-axis.
We know that,
If the points in a residual plot are randomly dispersed around the horizontal or x-axis, a linear regression model is appropriate for the data. Otherwise, a non-linear model is more appropriate.
As, in this case the points are distributed randomly around x-axis, so the residual plot show that the line of regression is best fit for the data set.
Hope this helps!
Step-by-step explanation: