1 to 1, which is greater than -1.2
Approx 0.995
In a normal distribution, approximately 15.87% of the data falls beyond a z-score of -1.00 in the left tail. This is because a z-score of -1.00 corresponds to the 15.87th percentile of the distribution. Therefore, the proportion of the distribution located in the tail beyond z = -1.00 is about 15.87%.
In a normal distribution, approximately 76.4% of the data falls below a z score of 1.04. Therefore, the proportion of the distribution that corresponds to z scores greater than 1.04 is about 23.6%. This can be found using standard normal distribution tables or calculators.
0.3829 approx.
To find the proportion of a normal distribution corresponding to z-scores greater than +1.04, you can use the standard normal distribution table or a calculator. The area to the left of z = 1.04 is approximately 0.8508. Therefore, the proportion of the distribution that corresponds to z-scores greater than +1.04 is 1 - 0.8508, which is approximately 0.1492, or 14.92%.
It is 0.017864
Pr(Z > 1.16) = 0.123
0% of a normal (of any) distribution falls between z 1.16 and z 1.16. 1.16 - 1.16 = 0.
Approx 0.995
In a normal distribution, approximately 15.87% of the data falls beyond a z-score of -1.00 in the left tail. This is because a z-score of -1.00 corresponds to the 15.87th percentile of the distribution. Therefore, the proportion of the distribution located in the tail beyond z = -1.00 is about 15.87%.
1.16/z = -1.16/z * * * * * No! Pr(Z > 1.16) = 0.123 So Pr(-1.16 < Z < 1.16) = 1 - 2*0.123 = 1 - 0.246 = 0.754
In a normal distribution, approximately 76.4% of the data falls below a z score of 1.04. Therefore, the proportion of the distribution that corresponds to z scores greater than 1.04 is about 23.6%. This can be found using standard normal distribution tables or calculators.
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Prob(-0.5 < z < 0.5) = 0.3830
It is .121
The coefficient of simple determination tells the proportion of variance in one variable that can be accounted for (or explained) by variance in another variable. The coefficient of multiple determination is the Proportion of variance X and Y share with Z; or proportion of variance in Z that can be explained by X & Y.
0.50