Answer:
Lixin obtains 15 marks
Step-by-step explanation:
Lets write two equations based on the question where: <u>D= Devi and L=Lixin</u>
D=L+15 <em>- "devi scores 15 marks more than lixin"</em>
D=2L <em>- "devi obtains twice as many marks as lexin"</em>
Now, substitute 2L as D in the first equation we wrote: (D=L+15)
2L=L+15
Next, subtract L from both sides:
<u>L=15</u>
Answer:
Option c (Upper tailed) is the correct choice.
Step-by-step explanation:
Given that:
The average attendance is:
= 74,900
We will have to test:
⇒ 
or,
Verses,
⇒ 
or,
The other given alternatives aren't connected to the given scenario. So the above is the correct one.
Answer:
z= 0.278
Step-by-step explanation:
Given data
n1= 60 ; n2 = 100
mean 1= x1`= 10.4; mean 2= x2`= 9.7
standard deviation 1= s1= 2.7 pounds ; standard deviation 2= s2 = 1.9 lb
We formulate our null and alternate hypothesis as
H0 = x`1- x`2 = 0 and H1 = x`1- x`2 ≠ 0 ( two sided)
We set level of significance α= 0.05
the test statistic to be used under H0 is
z = x1`- x2`/ √ s₁²/n₁ + s₂²/n₂
the critical region is z > ± 1.96
Computations
z= 10.4- 9.7/ √(2.7)²/60+( 1.9)²/ 100
z= 10.4- 9.7/ √ 7.29/60 + 3.61/100
z= 0.7/√ 0.1215+ 0.0361
z=0.7 /√0.1576
z= 0.7 (0.396988)
z= 0.2778= 0.278
Since the calculated value of z does not fall in the critical region so we accept the null hypothesis H0 = x`1- x`2 = 0 at 5 % significance level. In other words we conclude that the difference between mean scores is insignificant or merely due to chance.
Answer:
The residual value is -1.8 when x = 3
Step-by-step explanation:
We are given the following table
x | y
0 | -3
2 | -1
3 | -1
5 | 5
6 | 6
Residual value:
A residual value basically shows the position of a data point with respect to the line of best fit.
The residual value is calculated as,
Residual value = Observed value - Predicted value
Where observed values are already given in the question and the predicted values are calculated by using the equation of line of best fit.
When we substitute x = 3 in the above equation then we would get the predicted value.
So the predicted value is 0.8
From the given table, the observed value corresponding to x = 3 is -1
So the residual value is,
Residual value = Observed value - Predicted value
Residual value = -1 - 0.8
Residual value = -1.8
Therefore, the residual value is -1.8 when x = 3
Note: A residual value closer to 0 is desired which means that the regression line best fits the data.
A
(-2c-3d) (- 11) (- 2c-3d) (- 11) left parenthesis, minus, 2, c, minus, 3, d, right parenthesis, left parenthesis, minus, 11, right parenthesis
C
(66c + 99d) \ cdot \ dfrac {1} {3} (66c + 99d) ⋅ 3
1 left parenthesis, 66, c, plus, 99, d, right parenthesis, dot, start fraction, 1, divided by, 3, end fraction
<span> E
11\cdot(2c+3d)11⋅(2c+3d)11, dot, left parenthesis, 2, c, plus, 3, d, right parenthesis
</span> answer
(-2c-3d) (- 11) = 22c + 33d
(66c + 99d) * 1/3 = 22c + 33d
11 * ( 2c+3d) = 22c + 33d
