The predicted number of wins for a team that averages 1.5 goals per game will be 17 according to the regression model.
From the table given, the linear regression model for the data can be expressed as :
- y = 14.02x - 3.64 ; where ;
- y = Predicted number of wins ;
- x = average number of goals per match ;
- slope = 14.02 ;
- Intercept = - 3.64
<u>The Number of wins for a team which averages 1.5 goals per match can be calculated thus</u> :
Average goals per match, x = 1.5
Substitute x = 1.5 into the equation :
y = 14.02(1.5) - 3.64
y = 17.39
y = 17 (nearest whole number)
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Answer:
D=32.09
Step-by-step explanation:
160.01-127.92=32.09
Answer:
a)0.6192
b)0.7422
c)0.8904
d)at least 151 sample is needed for 95% probability that sample mean falls within 8$ of the population mean.
Step-by-step explanation:
Let z(p) be the z-statistic of the probability that the mean price for a sample is within the margin of error. Then
z(p)=
where
- Me is the margin of error from the mean
- s is the standard deviation of the population
a.
z(p)=
≈ 0.8764
by looking z-table corresponding p value is 1-0.3808=0.6192
b.
z(p)=
≈ 1.1314
by looking z-table corresponding p value is 1-0.2578=0.7422
c.
z(p)=
≈ 1.6
by looking z-table corresponding p value is 1-0.1096=0.8904
d.
Minimum required sample size for 0.95 probability is
N≥
where
- z is the corresponding z-score in 95% probability (1.96)
- s is the standard deviation (50)
- ME is the margin of error (8)
then N≥
≈150.6
Thus at least 151 sample is needed for 95% probability that sample mean falls within 8$ of the population mean.
I believe the answer is 2(15t).