George saves 18% of his total gross weekly earnings from his 2 part-time jobs. He earns $6.25 per hour from one part-time job an
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Answer:
0.42 is closest to the proportion of customer purchase amounts between $14.00 and $16.00
Step-by-step explanation:
Mean =
Standard deviation =
We are supposed to find the proportion of customer purchase amounts between $14.00 and $16.00
P(14<x<16)
Formula :
At x = 14
Refer the z table for p value
P(x<14)=0.1922
At x = 16
Refer the z table for p value
P(x<16)=0.6141
P(14<x<16)=P(x<16)-P(x<14)=0.6141-0.1922=0.42
So, Option C is true
Hence 0.42 is closest to the proportion of customer purchase amounts between $14.00 and $16.00
Answer:
Step-by-step explanation:
We have the following expression:
We can find the GCF with the following steps.
1. Find the GCF for the numerical part 15 and -21
The factors for 15 are : 1,3,5,15
The factors for -21 are -21,-7,-3,-1,1,3,7,21
And the common factors are 1,3
The GCF for the numerical part is 3*1 = 3
2. Find the GCF for the variable
The factors of x^2 are : x*x
The factors of x are: x
The common factors are x for this case, so then the GCF for the variable part is x
3. Multiply the two results from steps 1 and 2
GCf= 3*x = 3x
If we consider the new expression:
We can find the GCF with the following steps.
1. Find the GCF for the numerical part 15 and -21
The factors for 15 are : 1,3,5,15
The factors for -21 are 1,3,7,21
And the common factors are 1,3
The GCF for the numerical part is 3*1 = 3
2. Find the GCF for the variable
The factors of x^2 are : x*x
The factors of x are: x
The common factors are x for this case, so then the GCF for the variable part is x
3. Multiply the two results from steps 1 and 2
GCf= 3*x = 3x
And as we can see both GCF are equal. So then we satisfy the condition required.
Answer:
-95.78
Step-by-step explanation:
As the researcher decided to make the number of parties attended per week the explanatory variable, this would be variable x in the regression line, and of course, the variable y would be the number of text messages sent per day.
After constructing the linear regression equation, the researcher found that an approximate value for the actual value of y could be represented by the line
Since this is an approximate value, it is not expected that it coincides with the actual value of y. We define then the residual for each value of x as the difference between the actual value of y and the approximation for the given x.
For the value x = 2 (the student attended 2 parties that week) the actual value of y is 20 (the student sent 20 text messages per day that week).
The approximate value of y would be according to the regression line
Hence, the residual value for x=2 would be