1.4 Interval Estimation – Case 2: Unknown variance σ2
... 1. Suppose that you wish to construct a confidence interval for a population mean µ. A sample of size n = 50 is drawn and the sample mean x̄ = 15 computed. Given the following statements (a)-(e), decide which relate to case 1 (σ 2 is known) and which relate to case 2 (σ 2 is unknown). (a) s2 = 91.39 ...
... 1. Suppose that you wish to construct a confidence interval for a population mean µ. A sample of size n = 50 is drawn and the sample mean x̄ = 15 computed. Given the following statements (a)-(e), decide which relate to case 1 (σ 2 is known) and which relate to case 2 (σ 2 is unknown). (a) s2 = 91.39 ...
course 602-651_2010
... patient is 60 days with a standard deviation of 15. If it is reasonable to assume an approximately normal distribution of lengths of stay, find the probability that a randomly selected patient from this group will have a length of stay: (a) Greater than 50 days (b) Less than 30 days (c) Between 30 a ...
... patient is 60 days with a standard deviation of 15. If it is reasonable to assume an approximately normal distribution of lengths of stay, find the probability that a randomly selected patient from this group will have a length of stay: (a) Greater than 50 days (b) Less than 30 days (c) Between 30 a ...
Confidence intervals using the t distribution
... CI: X-bar – tCRIT* sX-bar < mu< X-bar+ tCRIT* sX-bar If we use the critical values of t at .95 we will have an interval that includes mu if our sample is one of the 95% that falls within a 95% CI around mu. If we use the critical values of t at .99 we will have an interval that includes mu if our sa ...
... CI: X-bar – tCRIT* sX-bar < mu< X-bar+ tCRIT* sX-bar If we use the critical values of t at .95 we will have an interval that includes mu if our sample is one of the 95% that falls within a 95% CI around mu. If we use the critical values of t at .99 we will have an interval that includes mu if our sa ...
Sampling Distribution
... Note: this approach is valid for normally distributed populations even though they have different known variances and/or different sample sizes Note: For non-normally distributed populations, we need to draw large sample and thus to apply CLT to assume population normality and finally to use this ap ...
... Note: this approach is valid for normally distributed populations even though they have different known variances and/or different sample sizes Note: For non-normally distributed populations, we need to draw large sample and thus to apply CLT to assume population normality and finally to use this ap ...
Unit 26: Small Sample Inference for One Mean
... Inference for One Mean Summary of Video The z-procedures for computing confidence intervals or hypothesis testing work in cases where we know the population’s standard deviation. But that’s hardly ever the case in real life. For times when we don’t know the population standard deviation but still wa ...
... Inference for One Mean Summary of Video The z-procedures for computing confidence intervals or hypothesis testing work in cases where we know the population’s standard deviation. But that’s hardly ever the case in real life. For times when we don’t know the population standard deviation but still wa ...
Question 1
... How many prawns should be sampled from a catch if we want to be 95% confident, that the sample mean is no more than 1.4 grams from the actual mean weight of the catch? Assume that the range of weights in the catch is ...
... How many prawns should be sampled from a catch if we want to be 95% confident, that the sample mean is no more than 1.4 grams from the actual mean weight of the catch? Assume that the range of weights in the catch is ...
