Answer:
50
Step-by-step explanation:
Diagonals of a parallelogram bisect each other. So HK = JK and GK = LK.
HK = JK
c − 7 = 3c − 33
26 = 2c
c = 13
GK = LK
c + 12 = LK
13 + 12 = LK
25 = LK
GL is the sum of GK and LK:
GL = GK + LK
GL = 2LK
GL = 50
Answer:
16
Step-by-step explanation:
Given
4c + d and c = 5, d = - 4, then substitute values into the expression
= (4 × 5) + (- 4) = 20 - 4 = 16
Answer:
Step-by-step explanation:
Hello!
Y: sales of a product in a marketing district (million dollars)
X: population in a marketing district (million persons)
The objective is to test if there is a linear association between the sales of a product and the population of a marketing district.
Parameter; Estimated Value; 95 Percent Confidence limits
Intercept 7.43119 -1.18518 16.0476
Slope 0.755048 0.452886 1.05721
a.
To test if there is or not an association between these two variables, you have to do a hypothesis test for the slope. If the slope is equal to zero, there is no linear association between the two variables, if the slope is different from zero, then there is a linear association between the variables.
So the student's hypotheses are:
H₀: β = 0
H₁: β ≠ 0
The data the student used to conclude is the 95%CI for the slope [0.452886;1.05721]
To be able to decide over an hypothesis test using a confidence interval there are several conditions to be met, one of them is that the confidence level and the significance level should be complementary, this means that if the interval was constructed using 1 - α= 0.95, then the hypothesis test should be conducted with a significance level of α= 0.05.
Considering that the value of the slope stated in the null hypothesis is not included in the given interval, i.e. zero is not included in the CI, then the decision is to reject the null hypothesis.
Then it can be concluded that there is a linear association between the sales of a product and the population in a marketing district.
b.
Although in the context of the variables of study it makes no sense that the estimate of the intercept takes negative numbers, keep in mind that mathematically if possible and correct. This means that obtaining a negative estimate of the intercept does not represent a calculation error or problem for the regression model. In general, when this occurs, a footnote is made indicating has no biological meaning or sense in context.
After all, what you have done with the estimate is a mathematical assertion of the social variables.
I hope it helps!
What is the upper quartile, Q3, of the following data set? 54, 53, 46, 60, 62, 70, 43, 67, 48, 65, 55, 38, 52, 56, 41
scZoUnD [109]
The original data set is
{<span>54, 53, 46, 60, 62, 70, 43, 67, 48, 65, 55, 38, 52, 56, 41}
Sort the data values from smallest to largest to get
</span><span>{38, 41, 43, 46, 48, 52, 53, 54, 55, 56, 60, 62, 65, 67, 70}
</span>
Now find the middle most value. This is the value in the 8th slot. The first 7 values are below the median. The 8th value is the median itself. The next 7 values are above the median.
The value in the 8th slot is 54, so this is the median
Divide the sorted data set into two lists. I'll call them L and U
L = {<span>38, 41, 43, 46, 48, 52, 53}
U = {</span><span>55, 56, 60, 62, 65, 67, 70}
they each have 7 items. The list L is the lower half of the sorted data and U is the upper half. The split happens at the original median (54).
Q3 will be equal to the median of the list U
The median of U = </span>{<span>55, 56, 60, 62, 65, 67, 70} is 62 since it's the middle most value.
Therefore, Q3 = 62
Answer: 62</span>
Step-by-step explanation:
208
= 128 + 64 + 16
= 1 * 2⁷ + 1 * 2⁶ + 1 * 2⁴
= 11010000. (base 2)
