We're trying to fit a simple linear regression model for the the whole population:Y=Bo + B,X + E. (Assume ɛ are are independent and normally distributed with constant variance). We draw a random sample of n=6 from the population and get a sample correlation r= 0.5. Compute the 4 test statistics for testing the null Họ: slope=0 in the population. (This can be written as Ho: B1=0. It's also the same as testing whether roopulation=0) (Round your final answers to 2 decimal places, but don't round during intermediate steps.) a. First compute: R2 = Submit Answer Tries 0/3 1-R2 = b. Now compute the 4 statistics below:

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ANOVA for Regression
We're trying to fit a simple linear regression model for the the whole population:Y=Bo + B,X + ɛ. (Assume ɛ are are independent and normally distributed with constant variance).
We draw a random sample of n=6 from the population and get a sample correlation r = 0.5.
Compute the 4 test statistics for testing the null Ho: slope=0 in the population. (This can be written as Ho: B1=0. It's also the same as testing whether rpopulation=0)
(Round your final answers to 2 decimal places, but don't round during intermediate steps.)
a. First compute: R2 =
1-R2 =
Submit Answer Tries 0/3
b. Now compute the 4 statistics below:
x2
It
IF
Z =
Compute the values of the 4 test statistics.
x2
F%3D
Submit Answer Tries 0/3
Submit Answer Tries 0/3
Submit Answer Tries 0/3
Submit Answer Tries 0/3
Compute the p-values for each statistic. Assume the alternative for the Z andt test is 1-sided (Enter p-value as a decimal not a percent.): HA: slopepopulation > 0, and assume the alternative for the x2
and F is 2-sided: HA: slopepopulation * 0.
p-value =
How many degrees of freedom?
p-value =
p-value =
p-value =
Compare the p-values for the Z and t. Why are they so different?
O Because n is only =6 and Z and t give quite different results for very small samples.
O Because Z and t always yield very different p-values.
Submit Answer Tries 0/4
Submit Answer
Tries 0/3
Submit Answer
Tries 0/3
Submit Answer Tries 0/3
Transcribed Image Text:ANOVA for Regression We're trying to fit a simple linear regression model for the the whole population:Y=Bo + B,X + ɛ. (Assume ɛ are are independent and normally distributed with constant variance). We draw a random sample of n=6 from the population and get a sample correlation r = 0.5. Compute the 4 test statistics for testing the null Ho: slope=0 in the population. (This can be written as Ho: B1=0. It's also the same as testing whether rpopulation=0) (Round your final answers to 2 decimal places, but don't round during intermediate steps.) a. First compute: R2 = 1-R2 = Submit Answer Tries 0/3 b. Now compute the 4 statistics below: x2 It IF Z = Compute the values of the 4 test statistics. x2 F%3D Submit Answer Tries 0/3 Submit Answer Tries 0/3 Submit Answer Tries 0/3 Submit Answer Tries 0/3 Compute the p-values for each statistic. Assume the alternative for the Z andt test is 1-sided (Enter p-value as a decimal not a percent.): HA: slopepopulation > 0, and assume the alternative for the x2 and F is 2-sided: HA: slopepopulation * 0. p-value = How many degrees of freedom? p-value = p-value = p-value = Compare the p-values for the Z and t. Why are they so different? O Because n is only =6 and Z and t give quite different results for very small samples. O Because Z and t always yield very different p-values. Submit Answer Tries 0/4 Submit Answer Tries 0/3 Submit Answer Tries 0/3 Submit Answer Tries 0/3
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