Answer:
k = 2.5
Step-by-step explanation:
2.5 (4k+2) = 12k
10k + 5 = 12k
-10k
5 = 2k
5/2
2.5
Answer:

Step-by-step explanation:
There is enough information to make a equation in slope-intercept form.

'm' is the slope. 'b' is the y-intercept.
In the given question, 3 is the slope and 2 is the y-intercept. This means that:
m = 3
b = 2
Plug in the appropriate values:

Hope this helps.
Answer:
Step-by-step explanation:
In this problem, we have the following linear equations:
y=3x+5
y=ax+b
We know that a linear equation is an equation for a line. In a system of linear equations, two or more equations work together.
1. What values for a and b make the system inconsistent?
A system is inconsistent if and only if the lines are parallel in which case the system has no solution. This is illustrated in the first Figure bellow. Two lines are parallel if they share the same slope. So, the system is inconsistent for:
a=3
for any value of b
2. What values for a and b make the system consistent and dependent?
A system is consistent if and only if the lines are the same in which case the system has infinitely many solutions. This is illustrated in the second Figure bellow. So, the system is consistent and dependent for:
a=3 and b=5
To get 1/10 -> 1000/10 =100
and 1% of 100 -> 100/100 = 1 or 1%
100 for question 1
1 for q2
Answer:

And on this case if we see the significance level given
we see that
so we fail to reject the null hypothesis that the observed outcomes agree with the expected frequencies at 10% of significance.
Step-by-step explanation:
A chi-square goodness of fit test determines if a sample data obtained fit to a specified population.
represent the p value for the test
O= obserbed values
E= expected values
The system of hypothesis for this case are:
Null hypothesis: ![O_i = E_i[/tex[Alternative hypothesis: [tex]O_i \neq E_i](https://tex.z-dn.net/?f=O_i%20%3D%20E_i%5B%2Ftex%5B%3C%2Fp%3E%3Cp%3EAlternative%20hypothesis%3A%20%5Btex%5DO_i%20%5Cneq%20E_i%20)
The statistic to check the hypothesis is given by:

On this case after calculate the statistic they got: 
And in order to calculate the p value we need to find first the degrees of freedom given by:
, where k represent the number of levels (on this cas we have 10 categories)
And in order to calculate the p value we need to calculate the following probability:

And on this case if we see the significance level given
we see that
so we fail to reject the null hypothesis that the observed outcomes agree with the expected frequencies at 10% of significance.