1. 3(p+q)=p
3p+3q=p; 3p-p=3q; 2p=q so p=q/2
2. 4a=2b-7
2b=4a+7 so b=(4a+7)/2
when a=3 , b=(12+7)/2=19/2=9.5
3. d=rt; r=d/t
4.( 2x15)+(2xwidth)=90
2x width=90-30
width =60/2=30
Answer:
a) The 95% confidence interval for the mean waste recycled per person per day for the population of Maine is between 1.19 and 1.61 pounds.
b) 
Step-by-step explanation:
We have the standard deviation for the sample, which means that the t-distribution is used to solve this question.
The first step to solve this problem is finding how many degrees of freedom, we have. This is the sample size subtracted by 1. So
df = 7 - 1 = 6
95% confidence interval
Now, we have to find a value of T, which is found looking at the t table, with 6 degrees of freedom(y-axis) and a confidence level of
. So we have T = 2.4469, and the answer to question b is 
The margin of error is:
In which s is the standard deviation of the sample and n is the size of the sample.
The lower end of the interval is the sample mean subtracted by M. So it is 1.4 - 0.21 = 1.19 pounds.
The upper end of the interval is the sample mean added to M. So it is 1.4 + 0.21 = 1.61 pounds.
The 95% confidence interval for the mean waste recycled per person per day for the population of Maine is between 1.19 and 1.61 pounds.
Answer:
D. x = -5/2, x = -1/2
Step-by-step explanation:
2x² + 7x + 5 = 0
(2x + 5)(x + 1) = 0
2x + 5 = 0 or x + 1 = 0
2x = -5 or x = -1
x = -5/2 or x = -1/2
<span> In a </span>regular polygon <span>all </span>sides have the same<span> length, therefore:
x</span>² + 8x - 52 = x² - 2x + 8
x² - x² + 8x + 2x = 8 + 52
10x = 60
x = 60/10
x = 6
Answer:
A. Wage=βo +β1, Female + u, where Female (=1 if female) is an indicator variable and u the error term.
Wage = dependent variable
FP = Independent variable.
Step-by-step explanation:
The model selected model shows the relationship between the wages earned by male and female gender based on earnings of the two gender groups.
Wage is the dependent variable while factors upong which their wage depends on will be adopted as the independent variable. Since gender is categorical and cannot be used in that form during regression analysis. The it can be converted into a dummy or encoded variable. Such as treating female as = 1.