Answer:
For this case assuming that the random variable is X
And replacing n = 24 we got:
And we notate the distribution we got: 
Step-by-step explanation:
Previous concepts
The t distribution (Student’s t-distribution) is a "probability distribution that is used to estimate population parameters when the sample size is small (n<30) or when the population variance is unknown".
The shape of the t distribution is determined by its degrees of freedom and when the degrees of freedom increase the t distirbution becomes a normal distribution approximately.
The degrees of freedom represent "the number of independent observations in a set of data. For example if we estimate a mean score from a single sample, the number of independent observations would be equal to the sample size minus one."
Solution to the problem
For this case assuming that the random variable is X
And replacing n = 24 we got:
And we notate the distribution we got:
Answer:
78
Step-by-step explanation:
Smallest square: 1
Small Square: 2, Area of 4
Mid Square: 3, Area of 9
Mid White Square: 5, Area of 25
Big Square: 8, Area of 64
1 + 4 + 9 + 64 = 78
Answer:
Explanation is in a file
Step-by-step explanation:
Answer:
The probability mass of X is 0.03
Step-by-step explanation:
If we set the winning requirement of your heads and my tails then the occurring possibility of both is 1/2 or 0.5.
Hence let us make a graph and use the figures to calculate the all the probabilities of you getting a heads.
Where X represents the number of dollars won during the flip of the coin, probability of heads represent the chances of occurrence of the value and of winning the dollars.
The probability of winning start to drop as the winning amount increases.
X 0 1 2 3 4 5
Probability of Heads 0 0.50 0.25 0.13 0.06 0.03
Answer:
The correlation coeffcient for this case was provided:
r =0.934
And this coefficient is very near to 1 the maximum possible value, so then we can interpret that the relationship between the entrace exam score and the grade point average are strongly linearly correlated .
We can also find the 
who represent the determination coefficient and we got:
And the interpretation for this is that a linear model explains appproximately 87.2% of the variability between the two variables
Step-by-step explanation:
Previous concepts
The correlation coefficient is a "statistical measure that calculates the strength of the relationship between the relative movements of two variables". It's denoted by r and its always between -1 and 1.
And in order to calculate the correlation coefficient we can use this formula:
![r=\frac{n(\sum xy)-(\sum x)(\sum y)}{\sqrt{[n\sum x^2 -(\sum x)^2][n\sum y^2 -(\sum y)^2]}}](https://tex.z-dn.net/?f=r%3D%5Cfrac%7Bn%28%5Csum%20xy%29-%28%5Csum%20x%29%28%5Csum%20y%29%7D%7B%5Csqrt%7B%5Bn%5Csum%20x%5E2%20-%28%5Csum%20x%29%5E2%5D%5Bn%5Csum%20y%5E2%20-%28%5Csum%20y%29%5E2%5D%7D%7D)
The correlation coeffcient for this case was provided:
r =0.934
And this coefficient is very near to 1 the maximum possible value, so then we can interpret that the relationship between the entrace exam score and the grade point average are strongly linearly correlated .
We can also find the who represent the determination coefficient and we got:
And the interpretation for this is that a linear model explains appproximately 87.2% of the variability between the two variables
