Chapter Solutions
... b. The mode is the value that occurs most often. For data grouped into a frequency distribution, the mode is the midpoint of the class containing the most observations. There are more observations (12) in the $12 up to $14 class than in any other class. The midpoint of the class is $13, which is the ...
... b. The mode is the value that occurs most often. For data grouped into a frequency distribution, the mode is the midpoint of the class containing the most observations. There are more observations (12) in the $12 up to $14 class than in any other class. The midpoint of the class is $13, which is the ...
Introduction to Estimation
... Suppose we are given two unbiased estimators for a parameter. Then, we say that the estimator with a smaller variance is more efficient. Example 1: For a normally distributed population, it can be shown that the sample median is an unbiased estimator for µ. It can also be shown, however, that the sa ...
... Suppose we are given two unbiased estimators for a parameter. Then, we say that the estimator with a smaller variance is more efficient. Example 1: For a normally distributed population, it can be shown that the sample median is an unbiased estimator for µ. It can also be shown, however, that the sa ...
Math 116 - Final Exam - Spring 2007
... Your task is to write a paragraph or two which answers the following question and provides an explanation for your answer: Do the results above provide statistical evidence that the normal body temperature is less than 98.6. Justify your answer. (Note: If the evidence suggests a normal body temperat ...
... Your task is to write a paragraph or two which answers the following question and provides an explanation for your answer: Do the results above provide statistical evidence that the normal body temperature is less than 98.6. Justify your answer. (Note: If the evidence suggests a normal body temperat ...
The t Test for Two Independent Samples
... • The observations within each sample must be independent • The two populations from which the samples are selected must be normal • The two populations from which the samples are selected must have equal variances – Homogeneity of variance ...
... • The observations within each sample must be independent • The two populations from which the samples are selected must be normal • The two populations from which the samples are selected must have equal variances – Homogeneity of variance ...
Statistical Significance Testing Brian K. Miller, Ph.D. Key Terms
... v In other words, effect observed in sample data is not due to random _____________or chance v To determine statistical significance: v We must compare size of effect to our measure of ...
... v In other words, effect observed in sample data is not due to random _____________or chance v To determine statistical significance: v We must compare size of effect to our measure of ...
Paired Samples versus Independent Samples
... responses of one treatment group as a whole to the responses of the other treatment group as a whole. We will calculate summary measures for the observations from one treatment group and compare them to similar summary measures calculated from the observations from the other treatment group. ...
... responses of one treatment group as a whole to the responses of the other treatment group as a whole. We will calculate summary measures for the observations from one treatment group and compare them to similar summary measures calculated from the observations from the other treatment group. ...
Power 10
... • Develop a model that has a theoretical basis. • Gather data for the two variables in the model. • Draw the scatter diagram to determine whether a linear model appears to be appropriate. • Determine the regression equation. • Check the required conditions for the errors. • Check the existence of ou ...
... • Develop a model that has a theoretical basis. • Gather data for the two variables in the model. • Draw the scatter diagram to determine whether a linear model appears to be appropriate. • Determine the regression equation. • Check the required conditions for the errors. • Check the existence of ou ...
http://www.ruf.rice.edu/~lane/stat_sim/sampling_dist/index.html
... Group Activity • Why isn’t the average score the same as the population score? • When you use a sample there is always some degree of uncertainty! • We can measure this uncertainty with a sampling distribution of the mean ...
... Group Activity • Why isn’t the average score the same as the population score? • When you use a sample there is always some degree of uncertainty! • We can measure this uncertainty with a sampling distribution of the mean ...
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.