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Hatshy [7]
3 years ago
7

What is 20 + 5 + 2 + 3 + 1 minus 1 ​

Mathematics
2 answers:
Serjik [45]3 years ago
8 0
30 :
20+5+2+3+1 = 31
And there’s a minus 1 so it’s 30
Yuri [45]3 years ago
4 0

20 + 5 + 2 + 3 + 1 minus 1 =30

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The equation of a proportional relationship is of the form y=kx, where k is a positive number, and the graph is a line through (
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  see attached

Step-by-step explanation:

The graph of a line with negative slope goes down to the right. The line would still go through the origin. An example is attached.

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3 years ago
Zack watered his garden with 1 3/8
victus00 [196]

Answer:

<u>The correct answer is that Zack used 2 7/8 gallons to water his garden on the fifth week.</u>

Step-by-step explanation:

Let's find out the pattern Zack is using to water his garden, this way:

First week =  1 3/8  gallons of water

Second week = 1 3/4 gallons of water = 1 3/8 + 3/8 = 1 6/8 = 1 3/4

Third week = 2 1/8 gallons of water = 1 3/4 + 3/8 = 1 6/8 + 3/8 = 2 1/8

The constant of adding every week is 3/8 gallons, therefore,

Fourth week = 2 1/8 + 3/8 = 2 4/8 = 2 1/2 gallons of water

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<u>The correct answer is that Zack used 2 7/8 gallons to water his garden on the fifth week.</u>

3 0
3 years ago
A set of bivariate data was used to create a least squares regression line. Which of the following is minimized by the line
77julia77 [94]

Answer:

B) The sum of the squared residuals

Step-by-step explanation:

Least Square Regression Line is drawn through a bivariate data(Data in two variables) plotted on a graph to explain the relation between the explanatory variable(x) and the response variable(y).

Not all the points will lie on the Least Square Regression Line in all cases. Some points will be above line and some points will be below the line. The vertical distance between the points and the line is known as residual. Since, some points are above the line and some are below, the sum of residuals is always zero for a Least Square Regression Line.

Since, we want to minimize the overall error(residual) so that our line is as close to the points as possible, considering the sum of residuals wont be helpful as it will always be zero. So we square the residuals first and them sum them. This always gives a positive value. The Least Square Regression Line minimizes this sum of residuals and the result is a line of Best Fit for the bivariate data.

Therefore, option B gives the correct answer.

5 0
3 years ago
Read 2 more answers
For this question I need to find 80% of 20
Ipatiy [6.2K]

Answer:

16

Step-by-step explanation:

.8 x 20 =16

7 0
3 years ago
3. The Food Marketing Institute shows that 17% of households spend more than $100 per week on groceries. Assume the population p
damaskus [11]

Answer:

A)sample proportion = 0.17,  the sampling distribution of p can be calculated/approximated with normal distribution of sample proportion = 0.17 and standard error/deviation = 0.013281

B) 0.869

C)0.9668

Step-by-step explanation:

A) p ( proportion of population that spends more than $100 per week) = 0.17

sample size (n)= 800

the sample proportion of p = 0.17

standard error of p = \sqrt{\frac{p(1-p)}{n} } = 0.013281

the sampling distribution of p can be calculated/approximated with

normal distribution of sample proportion = 0.17 and standard error/deviation = 0.013281

B) probability that the sample proportion will be +-0.02 of the population proportion

= p (0.17 - 0.02 ≤ P ≤ 0.17 + 0.02 ) = p( 0.15 ≤ P ≤ 0.19)

z value corresponding to P

Z = \frac{P - p}{standard deviation}

at P = 0.15

Z =  (0.15 - 0.17) / 0.013281 = = -1.51

at P = 0.19

z = ( 0.19 - 0.17) / 0.013281 = 1.51

therefore the required probability will be

p( -1.5 ≤ z ≤ 1.5 ) = p(z ≤ 1.51 ) - p(z ≤ -1.51 )

                           = 0.9345 - 0.0655 = 0.869

C) for a sample (n ) = 1600

standard deviation/ error = 0.009391 (applying the equation for calculating standard error as seen in part A above)

therefore the required probability after applying

z = \frac{P-p}{standard deviation} at p = 0.15 and p = 0.19

p ( -2.13 ≤ z ≤ 2.13 ) = p( z ≤ 2.13 ) - p( z ≤ -2.13 )

                               = 0.9834 - 0.0166 = 0.9668

7 0
3 years ago
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