a store has 30 boxes of melon each box has 4 bags each bag holde 2 melons what is the total number of melon in the store?

Suppose you want to know the exact number of the people who voted for candidate. Would you rather look at the bar graph or at fr

IgorC [24]

I would rather look at a frequency table because the exact numbers would be shown and it could tell you by how much and how frequently they’re increasing . A bar graph could do the same , but sometimes they could be grouped so it wouldn’t tell you the exact number .

4 0

3 years ago

A garment dealer buys for Rs.10,000 and spend Rs.800 on freight. He seeks the entire stock for Rs.12,960. Find his loss/profit p

jeyben [28]

Answer:

Profit percentage $=20\%$ .

Step-by-step explanation:

Given: A garment dealer buys goods for $\text{Rs.}\:10,000$ and spend $\text{Rs.}\:800$ on freight. He seeks the entire stock for $\text{Rs.}\:12,960$ .

To find: His loss/profit percentage.

Solution:

We have,

Cost price of goods $=\text{Rs.}\:10,000$

Spent on freight $=\text{Rs.}\:800$

So, total cost of goods $=\text{Rs.}\:10000+\text{Rs.}\:800=\text{Rs.}\:10,800$

Selling price of entire stock $=\text{Rs.}\: 12,960.$

Now, Profit $=\text{Selling price}-\text{Cost price}$

So, Profit $=\text{Rs.}\:12, 960-\text{Rs.}\:10,800=\text{Rs.}\:2160$

Now,

$\text{Profit\:\%}= \frac{\text{Profit}}{\text{Cost price}} \times 100\%$

$\implies\text{Profit\:\%}= \frac{2160}{\text{10,800}} \times 100\%$

$\implies\text{Profit\:\%}= \frac{2160}{108}\%$

$\implies\text{Profit\:\%}= 20\%$

Hence, profit percentage $=20\%$ .

8 0

3 years ago

What is the equation of a line with a slope of -1/2 and passes through the point (6,-6)

Studentka2010 [4]

Y = mx + b
slope(m) = -1/2
(6,-6)....x = 6 and y = -6
now we sub into the formula and find b, the y int
-6 = -1/2(6) + b
-6 = - 3 + b
-6 + 3 = b
-3 = b

so ur equation is : y = -1/2x - 3 <==

4 0

3 years ago

What's the sum for 329+252

HACTEHA [7]

Answer:

581

Step-by-step explanation:

3 0

3 years ago

An interior automotive supplier places several electrical wires in a harness.Apull test measures the force required to pull spli

oksano4ka [1.4K]

Answer:

a) For this case we can use the following R code to construct the qq plot

> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)

# The above line is in order to store the data in a vector

> qqnorm(data, pch = 1, frame = FALSE)

# The line above is in order to calculate the quantiles from the data assumin Normal distribution

> qqline(data, col = "steelblue", lwd = 2)

# The line above is in order to put a line for the theoretical dsitribution

The result is on the figure attached.

b) For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.

Step-by-step explanation:

For this case we have the following data:

28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4

The quantile-quantile or q-q plot is a graphical procedure in order to check the validity of a distributional assumption for a data set. We just need to calculate "the theoretically expected value for each data point based on the distribution in question".

If the values are asusted to the assumed distribution, we will see that "the points on the q-q plot will fall approximately on a straight line"

For this case our distribution assumed is normal.

Part a

For this case we can use the following R code to construct the qq plot

> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)

# The above line is in order to store the data in a vector

> qqnorm(data, pch = 1, frame = FALSE)

# The line above is in order to calculate the quantiles from the data assuming Normal distribution (0,1)

> qqline(data, col = "steelblue", lwd = 2)

# The line above is in order to put a line for the theoretical distribution

The result is on the figure attached.

Part b

For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.

4 0

3 years ago