Answer:
You can not break that down any further if x does not equal anything and the equation is not equal to anything.
Step-by-step explanation:
Answer:
Answer = d. Chi-Square Goodness of Fit
Step-by-step explanation:
A decision maker may need to understand whether an actual sample distribution matches with a known theoretical probability distribution such as Normal distribution and so on. The Goodness-of-fit Test is a type of Chi-Square test that can be used to determine if a data set follows a Normal distribution and how well it fits the distribution. The Chi-Square test for Goodness-of-fit enables us to determine the extent to which theoretical probability distributions coincide with empirical sample distribution. To apply the test, a particular theoretical distribution is first hypothesized for a given population and then the test is carried out to determine whether or not the sample data could have come from the population of interest with hypothesized theoretical distribution. The observed frequencies or values come from the sample and the expected frequencies or values come from the theoretical hypothesized probability distribution. The Goodness-of-fit now focuses on the differences between the observed values and the expected values. Large differences between the two distributions throw doubt on the assumption that the hypothesized theoretical distribution is correct and small differences between the two distributions may be assumed to be resulting from sampling error.
Answer:
The following tells what variable terms are and a explanation. I hope this is helpful
Step-by-step explanation:
Each expression is made up of terms. A term can be a signed number, a variable, or a constant multiplied by a variable or variables. ... In , the terms are: 5x, 3y, and 8. When a term is made up of a constant multiplied by a variable or variables, that constant is called a coefficient.
Answer:
m∠CBD = m∠CDB ⇒ proved
Step-by-step explanation:
Let us solve the question
∵ AB ⊥ BD ⇒ given
→ That means m∠ABD = 90°
∴ m∠ABD = 90° ⇒ proved
∵ ED ⊥ BD ⇒ given
→ That means m∠EDB = 90°
∴ m∠EDB = 90° ⇒ proved
∵ ∠ABD and ∠EDB have the same measure 90°
∴ m∠ABD = m∠EDB ⇒ proved
∵ m∠ABD = m∠ABC + m∠CBD
∵ m∠EDB = m∠EDC + m∠CDB
→ Equate the two right sides
∴ m∠ABC + m∠CBD = m∠EDC + m∠CDB
∵ m∠ABC = m∠EDC ⇒ given
→ That means 1 angle on the left side = 1 angle on the right side, then
the other two angles must be equal in measures
∴ m∠CBD = m∠CDB ⇒ proved
Answer:
The true true level of significance of this test is more than 0.01.
Step-by-step explanation:
No standard deviation and we are told that the investigator still used z rather than the more appropriate t - distribution.
This method of using the z-distribution when standard deviation is unknown will definitely result in a smaller critical value and this in turn simply means that the p-value will be smaller than what it should really be.
Thus, it means the critical value is getting closer to the mean value than the way it should be.
Therefore, means that for a given significance of 0.01 and using the z-distribution under this no standard deviation situation, the true true level of significance of this test is more than 0.01.
