Answer:
ya you can take it both like integer and decimal
cause - the negative mark make it negative the . make it decimal
Answer:
1.A
2.B
3.A
4. D
5.C
Step-by-step explanation:
make me brainlest :>>
Step-by-step explanation:
There is no diagram, please update question.
Answer:
A. 0.0625
B. 0.1089
C. 0.81
D. 0.0196
Step-by-step explanation:
The proportion of variance is shared by the two correlated variables is given as the r².
Therefore,
For A. r = 0.25
Proportion of variance is shared by the two correlated variables = 0.25²
= 0.0625
For B. r = 0.33
Proportion of variance is shared by the two correlated variables = 0.33²
= 0.1089
For C. r = 0.90
Proportion of variance is shared by the two correlated variables = 0.90²
= 0.81
For D. r = 0.14
Proportion of variance is shared by the two correlated variables = 0.14²
= 0.0196
Answer:
Confidence Interval for the mean
Step-by-step explanation:
Confidence interval is made using the observations of a <em>sample</em> of data obtained from a population, so it is constructed in such a way, that, with a certain <em>level of confidence </em>(this is the statement mentioned in the question), that is, one could have a percentage of probability that the interval, or range around the value obtained, frequently 95%, contains the true value of a population parameter (in this case, the population mean).
It is one way to extract information from a population using a sample of it. This kind of information is what inference statistic is always looking for.
An <u>approximation</u> about how to construct this interval or range:
- Select a random sample.
- For the specific case of a <em>mean</em>, you need to calculate the mean of the <em>sample </em>(sample mean), and, if standard deviation is unknown or not mentioned, also calculate the sample standard deviation.
- With this information, and acknowledged that these values follows a standard normal distribution (a normal distribution with mean 0 and a standard deviation of 1), represented by random variable Z, one can use all this information to calculate a <em>confidence interval for the mean</em>, with a certain confidence previously choosen (for example, 95%), that the population mean must be in this interval or <em>range around this sample mean.</em>
