Statistical Description of Data
... The Kolmogorov-Smirnov (or K–S) test is applicable to unbinned distributions that are functions of a single independent variable, that is, to data sets where each data point can be associated with a single number (lifetime of each light bulb when it burns out, or declination of each star). In such c ...
... The Kolmogorov-Smirnov (or K–S) test is applicable to unbinned distributions that are functions of a single independent variable, that is, to data sets where each data point can be associated with a single number (lifetime of each light bulb when it burns out, or declination of each star). In such c ...
Estimating Means and Proportions
... same as the mean of the population from which the sample is drawn. • Variance: The variance of this distribution is equal to the population variance divided by the sample size (n). • Justification: Central Limit Theorem. • Implications: If we draw a random sample of sufficient size we can estimate t ...
... same as the mean of the population from which the sample is drawn. • Variance: The variance of this distribution is equal to the population variance divided by the sample size (n). • Justification: Central Limit Theorem. • Implications: If we draw a random sample of sufficient size we can estimate t ...
Statistics Notation Review - Department of Agricultural Economics
... Measures of dispersion of interest are the range, maximum, minimum, variance, standard deviation, and coefficient of variation. The range is the difference between the largest value in the sample (the maximum) and the smallest value (the minimum), that is R x max x min , where R denotes the rang ...
... Measures of dispersion of interest are the range, maximum, minimum, variance, standard deviation, and coefficient of variation. The range is the difference between the largest value in the sample (the maximum) and the smallest value (the minimum), that is R x max x min , where R denotes the rang ...
Sampling and estimation 2
... • There is an asymmetry between H0 and H1: In fact, if the data is inconclusive, we end up not rejecting H0. • If H0 is true the probability to reject H0 is (say) 5%. That DOES NOT MEAN we are 95% certain that H0 is true! • How much evidence we have for choosing H1 over H0 depends entirely on how mu ...
... • There is an asymmetry between H0 and H1: In fact, if the data is inconclusive, we end up not rejecting H0. • If H0 is true the probability to reject H0 is (say) 5%. That DOES NOT MEAN we are 95% certain that H0 is true! • How much evidence we have for choosing H1 over H0 depends entirely on how mu ...
Inferential Statistics Probability From Samples to Populations
... • A type I error is made when a researcher rejects the null hypothesis when it is true • The probability of making this type of error is equal to the level of significance • A type II error is made when a researcher accepts the null hypothesis when it is false • As the level of significance incr ...
... • A type I error is made when a researcher rejects the null hypothesis when it is true • The probability of making this type of error is equal to the level of significance • A type II error is made when a researcher accepts the null hypothesis when it is false • As the level of significance incr ...
partial solutions to test two sample questions.
... 7. The weights of 39 boxes of cereal are measured. The sample standard deviation is 0.37 ounces. The sample mean is 15.9 ounces. We are concerned that the mean weight of all boxes may be under 16 ounces. (a) State the null and alternative hypotheses. (b) Test the null hypothesis at the 5% level, usi ...
... 7. The weights of 39 boxes of cereal are measured. The sample standard deviation is 0.37 ounces. The sample mean is 15.9 ounces. We are concerned that the mean weight of all boxes may be under 16 ounces. (a) State the null and alternative hypotheses. (b) Test the null hypothesis at the 5% level, usi ...
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... We can use iNZight to check how well the Bootstrap method works, by repeating the process many times taking different random samples from a known population. ...
... We can use iNZight to check how well the Bootstrap method works, by repeating the process many times taking different random samples from a known population. ...
