0
You don't. There are times when you use 99% or 99.5%, and others where you will settle for 90%.
The percentage chosen will depend on the implications of making the wrong decision.
Wiki User
∙ 11y ago
Add your answer:
Earn +20 pts
How do I write an interval notion and graph it...it is a set of real numbers that would be less than or equal to -4?
For an interval of numbers, two types of brackets are used, [] and (), the first signifies that interval includes the number before/after it and the latter indicate the interval includes everything upto that value.e.g.[0,2] indicates an interval of all real numbers from 0 to 2 including those numbers(-1,6) indicates an interval of all real numbers between -1 and 6 but not -1 and 6 themselves[5,12) indicates an interval of all real numbers from 5 upto but not including 12and (-9,-2] indicates an interval of all real numbers from -2 down to but not including -9.so, an interval of real numbers less than and equal to -4 would be (-­∞,-4], we use a ( for -∞ as, obviously, infinity can never be reached.To graph line intervals, we use a solid line along the interval and use filled circles, •, to signify that the point it is on is included in the interval, and empty circles, ○, to signify the point it is on is not included in the interval. So an interval of [5,12) would be drawn like this,•--------------------○5 6 7 8 9 10 11 12the drawing for (-­∞,-4] would simply be a straight solid line from the end of the negative side of the number line upto -4 with a • to show that -4 is included.
What is the most reasonable interval for the data 5 10 30 40 20?
A good way to assess what is a reasonable interval when graphing data is to see if there are any common factors in the data set. In this case 5, 10, 30, 40 and 20 are all clearly divisible by 5. Therefore, 5 would be a reasonable interval to use when graphing the data.
How do you use the points in each coordinate grid to determine what scale interval was used on each axis?
I'm not sure I understand your question, but if the point (10,6) is plotted 2 squares to the right of the origin and 3 squares up, then the horizontal scale interval is 5 and the vertical scale interval is 2. Each horizontal space represents 5; each vertical space represents 2.
What function f whose arc length from to x is 2x?
It is mathematically impossible to use arc length and an interval alone to determine a function!
What is 33 percent of 43500?
14355 use a calculator
Why do you use confidence intervals?
Statistical estimates cannot be exact: there is a degree of uncertainty associated with any statistical estimate. A confidence interval is a range such that the estimated value belongs to the confidence interval with the stated probability.
When you use a confidence interval to reach a conclusion about the population mean you are applying a type of reasoning or logic called?
normal distribution
Why can you never really have 100 percent confidence interval?
You can. For example, you can be 100% certain that the value when you roll a die will lie between 1 and 6. Or that the mean of 100 rolls will lie between 1 and 6. It is simply that a 100% CI has little use.
Confidence level and significance level?
I have always been careless about the use of the terms "significance level" and "confidence level", in the sense of whether I say I am using a 5% significance level or a 5% confidence level in a statistical test. I would use either one in conversation to mean that if the test were repeated 100 times, my best estimate would be that the test would wrongly reject the null hypothesis 5 times even if the null hypothesis were true. (On the other hand, a 95% confidence interval would be one which we'd expect to contain the true level with probability .95.) I see, though, that web definitions always would have me say that I reject the null at the 5% significance level or with a 95% confidence level. Dismayed, I tried looking up economics articles to see if my usage was entirely idiosyncratic. I found that I was half wrong. Searching over the American Economic Review for 1980-2003 for "5-percent confidence level" and similar terms, I found: 2 cases of 95-percent significance level 27 cases of 5% significance level 4 cases of 10% confidence level 6 cases of 90% confidence level Thus, the web definition is what economists use about 97% of the time for significance level, and about 60% of the time for confidence level. Moreover, most economists use "significance level" for tests, not "confidence level".
If Web Search Results for A bank wishes to estimate the mean balances owed by their MasterCard customers within 75 The population standard deviation is estimated to be 300 If a 98 percent confidence?
A bank wishing to estimate the mean balances owed by their MasterCard customers within 75 miles with a 98 percent confidence can use the following formula to calculate the required sample size: Sample size = (Z-score)2 * population standard deviation / (margin of error)2 Where Z-score = 2.326 for 98 percent confidence Population standard deviation = 300 Margin of error = desired confidence intervalSubstituting the values into the formula the required sample size is: 2.3262 * 300 / (Confidence Interval)2 = 553.7Therefore the bank would need to have a sample size of 554 to estimate the mean balances owed by their MasterCard customers within 75 miles with a 98 percent confidence.
Will The finite population correction factor lead to a wider confidence interval?
No since it is used to reduce the variance of an estimate in the case that the population is finite and we use a simple random sample.
What is the relationship between confidence interval and standard deviation?
Short answer, complex. I presume you're in a basic stats class so your dealing with something like a normal distribution however (or something else very standard). You can think of it this way... A confidence interval re-scales margin of likely error into a range. This allows you to say something along the lines, "I can say with 95% confidence that the mean/variance/whatever lies within whatever and whatever" because you're taking into account the likely error in your prediction (as long as the distribution is what you think it is and all stats are what you think they are). This is because, if you know all of the things I listed with absolute certainty, you are able to accurately predict how erroneous your prediction will be. It's because central limit theory allow you to assume statistically relevance of the sample, even given an infinite population of data. The main idea of a confidence interval is to create and interval which is likely to include a population parameter within that interval. Sample data is the source of the confidence interval. You will use your best point estimate which may be the sample mean or the sample proportion, depending on what the problems asks for. Then, you add or subtract the margin of error to get the actual interval. To compute the margin of error, you will always use or calculate a standard deviation. An example is the confidence interval for the mean. The best point estimate for the population mean is the sample mean according to the central limit theorem. So you add and subtract the margin of error from that. Now the margin of error in the case of confidence intervals for the mean is za/2 x Sigma/ Square root of n where a is 1- confidence level. For example, confidence level is 95%, a=1-.95=.05 and a/2 is .025. So we use the z score the corresponds to .025 in each tail of the standard normal distribution. This will be. z=1.96. So if Sigma is the population standard deviation, than Sigma/square root of n is called the standard error of the mean. It is the standard deviation of the sampling distribution of all the means for every possible sample of size n take from your population ( Central limit theorem again). So our confidence interval is the sample mean + or - 1.96 ( Population Standard deviation/ square root of sample size. If we don't know the population standard deviation, we use the sample one but then we must use a t distribution instead of a z one. So we replace the z score with an appropriate t score. In the case of confidence interval for a proportion, we compute and use the standard deviation of the distribution of all the proportions. Once again, the central limit theorem tells us to do this. I will post a link for that theorem. It is the key to really understanding what is going on here!
When should you use confidence interval and when should you use prediction interval?
Specifically, which to use in this scenario?Recommend a wireless phone plan from the alternatives listed above. The company is going to commit to using this phone plan for a long time. [Hint: use the relevant Margin of Error here.]It's from a test question. We're a given a regression analysis on phone minutes usage for a sample of company but the company stated in this question was not included in the sample (Therefore, prediction interval should be used?)
When finding a confidence interval for true mean spent of all citizens should you us a z-score or t-score?
If the sample size is less then 30 use the T table, if greater then 30 use the Z table.
How do you use class interval in a sentence?
The term class interval is used in statistics.
What number is always used when you use a proportion to solve a percent problem?
100
How do you use confidence into a sentence?
Confidence is needed to do many things. Self confidence can be supported by the people around you. Confidence! Rallied the girl.
