Bayesian Analysis - NYU Stern
... ( step) Compute b*(r) = (X’X)-1X’y*(r). Draw the observation on (r) from the normal population with mean b*(r) and variance (X’X)-1. Cycle between the two steps 50,000 times. Discard the first 10,000 and retain every 10th observation from the retained 40,000. ...
... ( step) Compute b*(r) = (X’X)-1X’y*(r). Draw the observation on (r) from the normal population with mean b*(r) and variance (X’X)-1. Cycle between the two steps 50,000 times. Discard the first 10,000 and retain every 10th observation from the retained 40,000. ...
Simple Random Sampling
... There are two things to notice. First, the mean of all possible samples selected with replacement (i.e., 10.1) is equal to the mean of all samples selected without replacement, and both sample means are equal to the population mean. Thus, the sample mean on average remains an unbiased estimator of ...
... There are two things to notice. First, the mean of all possible samples selected with replacement (i.e., 10.1) is equal to the mean of all samples selected without replacement, and both sample means are equal to the population mean. Thus, the sample mean on average remains an unbiased estimator of ...
Homework
... exceeds 80 degrees. If 20 days of at least 80 degree weather reveals an average of 13.9 pounds with a standard deviation of 2.3 pounds, who is correct at the alpha level of .05 using the p-value. Interpret the p-value and comment on its meaning. 3. Use Excel to get P-values using the data file MOIST ...
... exceeds 80 degrees. If 20 days of at least 80 degree weather reveals an average of 13.9 pounds with a standard deviation of 2.3 pounds, who is correct at the alpha level of .05 using the p-value. Interpret the p-value and comment on its meaning. 3. Use Excel to get P-values using the data file MOIST ...
Tests for Serial Correlation in Mean and Variance of Time Series
... vertical line indicates the closing value of the last day for each month, the black dotted line the mean value of each month, and the blue dashed line the maximum and minimum values of each month. It is evident from the plots that using different statistics results in a different relationship betwee ...
... vertical line indicates the closing value of the last day for each month, the black dotted line the mean value of each month, and the blue dashed line the maximum and minimum values of each month. It is evident from the plots that using different statistics results in a different relationship betwee ...
Sample Exam Questions
... 21) An educator wanted to look at the study habits of university students. As part of the research, data was collected for three variables - the amount of time (in hours per week) spent studying, the amount of time (in hours per week) spent playing video games and the GPA - for a sample of 20 male u ...
... 21) An educator wanted to look at the study habits of university students. As part of the research, data was collected for three variables - the amount of time (in hours per week) spent studying, the amount of time (in hours per week) spent playing video games and the GPA - for a sample of 20 male u ...
ast3e_chapter08
... Confidence Interval for a Mean Summary: 95% Confidence Interval for a Population Mean When the standard deviation of the population is unknown, a 95% confidence interval for the population mean is: ...
... Confidence Interval for a Mean Summary: 95% Confidence Interval for a Population Mean When the standard deviation of the population is unknown, a 95% confidence interval for the population mean is: ...
12.1 Inference for Linear Regression
... this population of data has been added to the graph. We call this the population regression line (or true regression line) because it uses all the observations that month. ...
... this population of data has been added to the graph. We call this the population regression line (or true regression line) because it uses all the observations that month. ...
German tank problem
In the statistical theory of estimation, the problem of estimating the maximum of a discrete uniform distribution from sampling without replacement is known in English as the German tank problem, due to its application in World War II to the estimation of the number of German tanks.The analyses illustrate the difference between frequentist inference and Bayesian inference.Estimating the population maximum based on a single sample yields divergent results, while the estimation based on multiple samples is an instructive practical estimation question whose answer is simple but not obvious.