SOC 8311 Basic Social Statistics
... For most continuous variables, a positively skewed distribution typically has a mean much larger than its median. A negatively skewed distribution typically has a mean smaller than its median. U.S. household income is positively skewed: in 2006 the median was $48,201 but the mean was $66,570. What p ...
... For most continuous variables, a positively skewed distribution typically has a mean much larger than its median. A negatively skewed distribution typically has a mean smaller than its median. U.S. household income is positively skewed: in 2006 the median was $48,201 but the mean was $66,570. What p ...
Chapter Review
... 1. A professor records the values of several variables for each student in her class. These include the variables listed below. Which of these variables are categorical? (a) Score on the final exam (out of 100 points) (b) Final grade for the course (A, B, C, D, or F) (c) The total number of points e ...
... 1. A professor records the values of several variables for each student in her class. These include the variables listed below. Which of these variables are categorical? (a) Score on the final exam (out of 100 points) (b) Final grade for the course (A, B, C, D, or F) (c) The total number of points e ...
Lecture Notes for Week 13
... If M M SE is large, this would indicate a relatively large amount of variation among groups, and we have reason to doubt that group means are all equal. In other words, if the F statistic is much larger than 1, we reject H0 . This would be the case in the second configuration. How large is “large” d ...
... If M M SE is large, this would indicate a relatively large amount of variation among groups, and we have reason to doubt that group means are all equal. In other words, if the F statistic is much larger than 1, we reject H0 . This would be the case in the second configuration. How large is “large” d ...
Variability
... So, an s of 1.76 tells us that each score differs from the mean by an average of 1.76 points. *Why n-1? N represents the true population and n-1 represents the sample. Since we are projecting onto the sample, it is better to overestimate the variability (be conservative). The larger the sample size, ...
... So, an s of 1.76 tells us that each score differs from the mean by an average of 1.76 points. *Why n-1? N represents the true population and n-1 represents the sample. Since we are projecting onto the sample, it is better to overestimate the variability (be conservative). The larger the sample size, ...
Good Luck! - TAMU Stat
... read off the data as 0.1, 0.3, 0.5, 0.5, 0.6, 0.9, 0.9 1.2 1.8, 6.2 7. For the above simulated data, compute the 10% trimmed mean. A. 1.65 B. 0.75 C. 1.30 D. 0.84 E. 0.77 Answer: D. 10% trimmed mean is the average of what is left over after removing 10% of the observations from either end of the ord ...
... read off the data as 0.1, 0.3, 0.5, 0.5, 0.6, 0.9, 0.9 1.2 1.8, 6.2 7. For the above simulated data, compute the 10% trimmed mean. A. 1.65 B. 0.75 C. 1.30 D. 0.84 E. 0.77 Answer: D. 10% trimmed mean is the average of what is left over after removing 10% of the observations from either end of the ord ...
8.2 Estimating Population Means
... (i.e. take a new sample, compute the CI, do again, etc.), then on average, 95 of those confidence intervals I created will contain μ. ...
... (i.e. take a new sample, compute the CI, do again, etc.), then on average, 95 of those confidence intervals I created will contain μ. ...
Parametric Statistics
... experimenter controls directly and to which he randomly assigns participants – Allows for cause and effect interpretations ...
... experimenter controls directly and to which he randomly assigns participants – Allows for cause and effect interpretations ...
Notes 2 - Wharton Statistics
... a survey on a different subject each day and come up with a valid 95% confidence interval for the population mean, then over our lifetime, about 95% of the intervals will contain the true population means. In introductory statistics, for situations in which the population is infinite, a central limi ...
... a survey on a different subject each day and come up with a valid 95% confidence interval for the population mean, then over our lifetime, about 95% of the intervals will contain the true population means. In introductory statistics, for situations in which the population is infinite, a central limi ...
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.