What is meant by a 95% confidence interval?
4enevi
Answered question
2022-10-14
Answer & Explanation
Jaylin Wheeler
Beginner2022-10-15Added 11 answers
An essential part of statistics is accounting for the variability of the estimate. Statistical analysis can also describe how far from the estimates the actual values are likely to be. To describe the precision of an estimate, statisticians use margins of error and confidence intervals.
The margin of error and the confidence interval apply to the estimated value of the parameter for the entire population, not for the value of the variable for particular individuals.
A confidence interval is an interval that will contain a population parameter a specified proportion of the time. The confidence interval can take any number of probabilities, with the most common being 95% or 99%.
For example:
If repeated samples were taken and the 95% confidence interval computed for each sample, 95% of the intervals would contain the population mean. Naturally, 5% of the intervals would not contain the population mean.
New Questions in Inferential Statistics
Which of the following statements is not correct for the relation R defined by aRb, if and only if b lives within one kilometre from a?
A) R is reflexive
B) R is symmetric
C) R is not anti-symmetric
D) None of the aboveA line segment is a part of a line as well as a ray. True or False
Which characteristic of a data set makes a linear regression model unreasonable?
Find the meaning of 'Sxx' and 'Sxy' in simple linear regression
In the least-squares regression line, the desired sum of the errors (residuals) should be
a) zero
b) positive
c) 1
d) negative
e) maximizedCan the original function be derived from its order Taylor polynomial?
Should the independent (or dependent) variables in a linear regression model be normal or just the residual?
What is the relationship between the correlation of two variables and their covariance?
What kind of technique is to be adopted if I have to find an equation or model for say, depends on , changes for a set of , which changes for different .
Correlation bound
Let x and y be two random variables such that:
Corr(x,y) = b, where Corr(x,y) represents correlation between x and y, b is a scalar number in range of [-1, 1]. Let y' be an estimation of y. An example could be y'=y+(rand(0,1)-0.5)*.1, rand(0,1) gives random number between 0, 1. I am adding some noise to the data.
My questions are:
Is there a way where I can bound the correlation between x, y' i.e. Corr(x,y')?I mentioned y' in light of random perturbation, I would like to know what if I don't have that information, where I only know that y' is a estimation of y. Are there any literature that cover it?What is the benefit of OU vs regression for modeling data, say data in the form of () pairs?
Can you determine the correlation coefficient from the coefficient of determination?
How can one find the root of sesquilinear form with positive definite matrix?
From numerical simulation and regression analysis I discovered that the root-mean-square amplitude of white noise with bandwidth is proportional to . How can this be derived mathematically ?
In a Simple Linear Regression analysis, independent variable is weekly income and dependent variable is weekly consumption expenditure. Here % confidence interval of regression coefficient, is .
