... 10,000 samples. This is very unlikely, so we will __________ the ______ hypothesis in favor of the __________ hypothesis and conclude that the cola actually did lose sweetness. If the P-value is small we say that our result is ____________________. The smaller the Pvalue, the stronger the evidence p ...
Descriptive Measures
... The Mode (Mo) of a data set is the value that occurs more than once and the most often. Mode is not always a measure of central tendency; this value need not occur in the center of the data. There may be more than one mode if several numbers occur the same (and the largest) number of times. ...
... The Mode (Mo) of a data set is the value that occurs more than once and the most often. Mode is not always a measure of central tendency; this value need not occur in the center of the data. There may be more than one mode if several numbers occur the same (and the largest) number of times. ...
Hypothesis Testing - University of Strathclyde
... • if the data are not normally distributed either look for a transformation which does normalise the distributions (e.g. log, square root) or use a Mann-Whitney test (the non-parametric equivalent to the t-test) • using a transformation is more sensitive but might lead to results and particularly co ...
... • if the data are not normally distributed either look for a transformation which does normalise the distributions (e.g. log, square root) or use a Mann-Whitney test (the non-parametric equivalent to the t-test) • using a transformation is more sensitive but might lead to results and particularly co ...
Descriptive statistics 2012_13
... tails - thus we use a two tailed test (with .025 in each tail). If we test a directional hypothesis, the level of significance applies to one tail only. ...
... tails - thus we use a two tailed test (with .025 in each tail). If we test a directional hypothesis, the level of significance applies to one tail only. ...
Descriptive Measures
... they are of little value in practical prediction situations. With correlation around 0.50, crude group prediction may be achieved. In describing the relationship between two variables, correlation coefficients that are this low do not suggest a good relationship. Correlation coefficients ranging ...
... they are of little value in practical prediction situations. With correlation around 0.50, crude group prediction may be achieved. In describing the relationship between two variables, correlation coefficients that are this low do not suggest a good relationship. Correlation coefficients ranging ...
day2-E2005
... Statistical inference: The use of a statistical analysis of data to draw conclusions from observations subject to random variation. Data are considered as a sample from a population (real or hypothetical) The purpose of the statistical analysis is to make statements about certain aspects of this pop ...
... Statistical inference: The use of a statistical analysis of data to draw conclusions from observations subject to random variation. Data are considered as a sample from a population (real or hypothetical) The purpose of the statistical analysis is to make statements about certain aspects of this pop ...
Descriptive Statistics (60 points)
... The ticket price of each team is, in average, $27.04 away from the mean of the sample. (d) Compute the standardized values (z -scores) for the listed ticket prices in the table above. Should the Dallas Cowboys’ ticket price be considered an outlier? Explain. The z-score for the Cowboys’ ticket price ...
... The ticket price of each team is, in average, $27.04 away from the mean of the sample. (d) Compute the standardized values (z -scores) for the listed ticket prices in the table above. Should the Dallas Cowboys’ ticket price be considered an outlier? Explain. The z-score for the Cowboys’ ticket price ...
Inference about a Mean / y y y n = + + L
... Note: The procedure illustrated is for an upper one-sided test. It can be adapted for a lower one-sided or a two-sided test. ...
... Note: The procedure illustrated is for an upper one-sided test. It can be adapted for a lower one-sided or a two-sided test. ...
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.