We use the chi-square distribution when making inferences about a single population variance.
Short Description of Chi-Square Distribution
The continuous probability distribution known as the chi-square distribution. The number of degrees of freedom (k) a chi-square distribution has determines its shape. This type of sampling distribution has a variance of 2k and a mean equal to its number of degrees of freedom (k). The range is of a chi-square distribution is from 0 to ∞.
Variance plays a key role in the analysis of risk and uncertainty. The sample variance, an unbiased estimator of population variance, is expressed by the following formula of core statistic for a sample size 'n' and Y' as the sample mean:
S² = ∑(Yₓ - Y') / (n-1)
The formula, (n-1)S² / σ² has the central chi-square distribution as χ²ₙ₋₁. Here (n-1) represents the degrees of freedom.
Learn more about chi-square distribution here:
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Answer:
5
Step-by-step explanation:
Factors of 35: 1,5,7,35
Factors of 10: 1,2,5,10
5 is the greatest common factor.
Answer: Should be 150mm
Step-by-step explanation:
Find the surface area of each figure. Round answers to the nearest tenth, if necessary
2 mm-
2/5 mm
10 mm
3 mm
O
75 mm2
150 mm2
92 mm2
86 mm2
Answer:
(1,1) is a solution of the system.
Step-by-step explanation:
Let's solve the system.
y = 2x - 1
5x - 4y = 1
In the first equation, y is already separated as a function of x. So we replace in the second equation;
5x - 4(2x - 1) = 1
5x - 8x + 4 = 1
4 - 1 = 8x - 5x
x = 1
y = 2x - 1 = 2(1) - 1 = 1
(1,1) is a solution of the system.
