Answer:
0.0623 ± ( 2.056 )( 0.0224 ) can be used to compute a 95% confidence interval for the slope of the population regression line of y on x
Step-by-step explanation:
Given the data in the question;
sample size n = 28
slope of the least squares regression line of y on x or sample estimate = 0.0623
standard error = 0.0224
95% confidence interval
level of significance ∝ = 1 - 95% = 1 - 0.95 = 0.05
degree of freedom df = n - 2 = 28 - 2 = 26
∴ the equation will be;
⇒ sample estimate ± ( t-test) ( standard error )
⇒ sample estimate ± (
) ( standard error )
⇒ sample estimate ± (
) ( standard error )
⇒ sample estimate ± (
) ( standard error )
{ from t table; (
) = 2.055529 = 2.056
so we substitute
⇒ 0.0623 ± ( 2.056 )( 0.0224 )
Therefore, 0.0623 ± ( 2.056 )( 0.0224 ) can be used to compute a 95% confidence interval for the slope of the population regression line of y on x
110+90+145=345
345 divided by 3=115
so the score of her third game was 145 points.
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Answers:
- a) Mean = 20.4
- b) New mean = 20
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Explanation:
To get the mean, we add up the scores and divide by 10 (because there are 10 scores at first)
28+13+4+12+32+22+13+22+26+32 = 204
204/10 = 20.4
The mean is 20.4
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For part b), we redo those steps shown above, but tack 16 onto the list. So we'll add up all the values (including that 16 at the end) and divide by 11 this time.
28+13+4+12+32+22+13+22+26+32+16 = 220
220/11 = 20
The new mean is 20.
The new mean is slightly smaller than the old mean. Notice how 16 is smaller than 20.4, so this new score pulls down the mean just a little bit.