§9.3--Hypothesis Tests for One Population Mean When is Known
... If z falls into the non-rejection region, then we assume that there is not enough evidence against the null hypothesis to reject it. If z falls into the rejection region, then we are declaring that it is better to reject H0 than to accept that something as improbable as z has just occurred by random ...
... If z falls into the non-rejection region, then we assume that there is not enough evidence against the null hypothesis to reject it. If z falls into the rejection region, then we are declaring that it is better to reject H0 than to accept that something as improbable as z has just occurred by random ...
Standard Deviation
... When computing the sample variance, we use the sample mean to compute the deviations. For the population variance we use the population mean for the deviations. It turns out that the deviations using the sample mean tend to be a bit smaller than the deviations using the population mean. If we were t ...
... When computing the sample variance, we use the sample mean to compute the deviations. For the population variance we use the population mean for the deviations. It turns out that the deviations using the sample mean tend to be a bit smaller than the deviations using the population mean. If we were t ...
Confidence Intervals
... is exactly correct when the population distribution is exactly Normal. No population of real data is exactly Normal. • An inference procedure is called robust if the probability calculations involved in the procedure remain fairly accurate when a condition for using the procedures is violated. • For ...
... is exactly correct when the population distribution is exactly Normal. No population of real data is exactly Normal. • An inference procedure is called robust if the probability calculations involved in the procedure remain fairly accurate when a condition for using the procedures is violated. • For ...
View/Open
... One of the most important concepts in statistics is the Central Limit Theorem. Informally, this theorem states that if we know the value of the variance S 2 of a random variable and take a sample of n independent measurements (y1 , y2 , . . . , yn ) on that variable, the distribution of the sample m ...
... One of the most important concepts in statistics is the Central Limit Theorem. Informally, this theorem states that if we know the value of the variance S 2 of a random variable and take a sample of n independent measurements (y1 , y2 , . . . , yn ) on that variable, the distribution of the sample m ...
Measures of Variability
... can see, 14 is an outlier and skews this distribution because if it were not in the distribution, the range would be (81 – 40) = 41. When is the range useful? The range gives you a rough guide to the variability in a data set, as it tells you how a particular score compares to the highest and lowest ...
... can see, 14 is an outlier and skews this distribution because if it were not in the distribution, the range would be (81 – 40) = 41. When is the range useful? The range gives you a rough guide to the variability in a data set, as it tells you how a particular score compares to the highest and lowest ...
Types of Variables - Center for Astrostatistics
... values of the variable and the proportion of times (or probability) it occurs (which may be expressible in functional form). The first two RV’s above are ‘continuous’. b. Probability distribution of a continuous random variable: idealized curve (perhaps from a histogram) which represents probability ...
... values of the variable and the proportion of times (or probability) it occurs (which may be expressible in functional form). The first two RV’s above are ‘continuous’. b. Probability distribution of a continuous random variable: idealized curve (perhaps from a histogram) which represents probability ...
Exam 1
... From the confidence interval results we know that with 95% assurance the slope is in the range [-0.243,-0.029], in particular at least with 95% assurance the slope is negative. Comment: Even though the R-square is relatively small, this only casts doubt on the predicted values of slope and intercept ...
... From the confidence interval results we know that with 95% assurance the slope is in the range [-0.243,-0.029], in particular at least with 95% assurance the slope is negative. Comment: Even though the R-square is relatively small, this only casts doubt on the predicted values of slope and intercept ...
... drawn. If we were to compute the standard deviation of the sampling distribution, we would compute the SS and divide by the number of means (16), and not 15. Can you articulate why that would be the case? The standard deviation of this sampling distribution of the mean would be 1.58, which is less t ...
