Powerpoint (Measures of Central Tendency
... median is the middle value when the data is ordered from least to greatest. ...
... median is the middle value when the data is ordered from least to greatest. ...
AP Statistics - Fall Final Exam
... He has contracted with 60 families, each with two children, who have agreed to test the product. He randomly assigns 30 families to the group that will use the new formula and 30 to the group that will use the company’s current detergent formula. The most important reason for this random assignment ...
... He has contracted with 60 families, each with two children, who have agreed to test the product. He randomly assigns 30 families to the group that will use the new formula and 30 to the group that will use the company’s current detergent formula. The most important reason for this random assignment ...
Non-Parametric Statisitics William Simpson 25th April 2014
... involving and inferences about sums of data •Central limit theorem says that the distribution of a sum approaches the normal as sample size increases ...
... involving and inferences about sums of data •Central limit theorem says that the distribution of a sum approaches the normal as sample size increases ...
8.5 to 8.6
... Use the sample data with a 0.05 significance level to test the claim of a production manager that the M&Ms have a mean that is actually greater than 0.8535 g. Use the traditional method. ...
... Use the sample data with a 0.05 significance level to test the claim of a production manager that the M&Ms have a mean that is actually greater than 0.8535 g. Use the traditional method. ...
Problem set for PA551
... 27. The average number of traffic accidents per 10 miles of state highway had been 42. New data this year from a sample of 144 stretches of roadway shows a mean of 38 accidents per 10 miles with a standard deviation of 24. Is this a statistically significant decrease (.05 level) in the accident rate ...
... 27. The average number of traffic accidents per 10 miles of state highway had been 42. New data this year from a sample of 144 stretches of roadway shows a mean of 38 accidents per 10 miles with a standard deviation of 24. Is this a statistically significant decrease (.05 level) in the accident rate ...
z-scores: Location of Scores and Standardized Distributions
... • Last week Andi had exams in Chemistry and in Spanish. On the chemistry exam, the mean was µ = 30 with σ = 5, and Andi had a score of X = 45. On the Spanish exam, the mean was µ = 60 with σ = 6 and Andi had a score of X = 65. For which class should Andi expect the better grade? ...
... • Last week Andi had exams in Chemistry and in Spanish. On the chemistry exam, the mean was µ = 30 with σ = 5, and Andi had a score of X = 45. On the Spanish exam, the mean was µ = 60 with σ = 6 and Andi had a score of X = 65. For which class should Andi expect the better grade? ...
Everything is not normal,Some comparisons are not odious,The big
... remember that the nonparametric equivalent of the Pearson’s correlation coefficient is the Spearman’s correlation coefficient. The problem is that nonparametric tests are more demanding than their parametric equivalent to obtain statistical significance, but they must be used as soon as there’s any ...
... remember that the nonparametric equivalent of the Pearson’s correlation coefficient is the Spearman’s correlation coefficient. The problem is that nonparametric tests are more demanding than their parametric equivalent to obtain statistical significance, but they must be used as soon as there’s any ...
Testing the Differences between Means
... see how likely the sample results are, given our hypothesis about the population. If the sample results are plausible under the hypothesis about the population, we retain the hypothesis and attribute any departure form our expected results to pure chance based on sampling error. If the sample result ...
... see how likely the sample results are, given our hypothesis about the population. If the sample results are plausible under the hypothesis about the population, we retain the hypothesis and attribute any departure form our expected results to pure chance based on sampling error. If the sample result ...
statistics and its role in psychological research
... Of importance is the fact that the mean is used as the reference point for transforming the raw scores into their respective deviation scores. The deviation score of X (x) shows how far, as well as in what direction, it is away from X (2 units above 14 in the case when X = 16). This foreshadows the ...
... Of importance is the fact that the mean is used as the reference point for transforming the raw scores into their respective deviation scores. The deviation score of X (x) shows how far, as well as in what direction, it is away from X (2 units above 14 in the case when X = 16). This foreshadows the ...
Lesson Plan - Howard County Public School System
... sum of the distances from the mean = MAD total # of data 11. Emphasize the following: The mean absolute deviation of a set of data is the average distance between each data value and the mean. The MAD is a statistic (because it is describing a sample) and is a useful measure of the amount of variabi ...
... sum of the distances from the mean = MAD total # of data 11. Emphasize the following: The mean absolute deviation of a set of data is the average distance between each data value and the mean. The MAD is a statistic (because it is describing a sample) and is a useful measure of the amount of variabi ...
Bootstrapping (statistics)
In statistics, bootstrapping can refer to any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in the broader class of resampling methods.Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed dataset (and of equal size to the observed dataset).It may also be used for constructing hypothesis tests. It is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors.