Sample Problems Qualifying Exam for B01.1305 Statistics and Data Analysis
... You have just calculated the average and standard deviation of a list of 101 numbers, finding an average of 240.0 and a standard deviation of 25.88. Unfortunately, a check of the list of numbers uncovers two errors. A number originally listed as 230 should be 200. A number originally listed as 250 s ...
... You have just calculated the average and standard deviation of a list of 101 numbers, finding an average of 240.0 and a standard deviation of 25.88. Unfortunately, a check of the list of numbers uncovers two errors. A number originally listed as 230 should be 200. A number originally listed as 250 s ...
Logistic regression
... – Assumes that the log odds of y=1 is a linear function of x (i.e., w∙x) – Learning goal is to learn a weight vector w such that examples with y=1 are predicted to have high P(y=1|x) and vice versa • Maximum likelihood estimation is a approach that achieves this • Iterative algorithm to learn w us ...
... – Assumes that the log odds of y=1 is a linear function of x (i.e., w∙x) – Learning goal is to learn a weight vector w such that examples with y=1 are predicted to have high P(y=1|x) and vice versa • Maximum likelihood estimation is a approach that achieves this • Iterative algorithm to learn w us ...
3. Confidence Interval (INTR)
... The following shows the meaning of each item in the case of list data specification. Data ............................ data type C-Level ........................ confidence level (0 < C-Level < 1) List(1) .......................... list whose contents you want to use as data of sample 1 List(2) .... ...
... The following shows the meaning of each item in the case of list data specification. Data ............................ data type C-Level ........................ confidence level (0 < C-Level < 1) List(1) .......................... list whose contents you want to use as data of sample 1 List(2) .... ...
Class5
... Indicates approximately how far the observed value of the statistic is from its mean • Literally: it indicated the standard deviation you would find if you took a very large number of samples, found the sample average for each one, and worked with these sample averages as a data set ...
... Indicates approximately how far the observed value of the statistic is from its mean • Literally: it indicated the standard deviation you would find if you took a very large number of samples, found the sample average for each one, and worked with these sample averages as a data set ...
lecture 16 estimating parameters
... numbers to the question, “We are 99% confident that the true mean monthly sales for the owner’s tour company are between $9,9,74.25 and $10,025.75. Again, the whole point of doing two confidence intervals in this example was to show that when you increase the confidence level, the z-score gets large ...
... numbers to the question, “We are 99% confident that the true mean monthly sales for the owner’s tour company are between $9,9,74.25 and $10,025.75. Again, the whole point of doing two confidence intervals in this example was to show that when you increase the confidence level, the z-score gets large ...
Algebra II Module 4, Topic C, Lesson 21: Teacher
... This applet may require an updated version of an operating system to work correctly. If the applet does not work for all students due to a computer’s operating system or network settings, attempt to demonstrate it for the whole class, as it is an effective way to complement how students obtained the ...
... This applet may require an updated version of an operating system to work correctly. If the applet does not work for all students due to a computer’s operating system or network settings, attempt to demonstrate it for the whole class, as it is an effective way to complement how students obtained the ...
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.