By CK-12 Foundation
CK-12’s complicated likelihood and Statistics-Second version is a transparent presentation of the elemental subject matters in statistics and likelihood, yet finishes with the rigorous themes a sophisticated placement path calls for. quantity 2 contains the final 7 chapters and covers the next issues: Sampling Distributions and Estimations, speculation checking out, Regression and Correlation, Chi-Square, research of Variance and F-Distribution, and Non-Parametric records. it is also a set of assets within the ultimate bankruptcy.
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Additional resources for CK-12 Advanced Probability and Statistics Second Edition Volume 2
The numerical measure that we use to determine the strength of the sample evidence we are willing to consider strong enough to reject is called the level of significance, and it is denoted by . If we choose, for example, , we are saying that the data we have collected would happen no more than 1% of the time when is true. 01. If our data results in a statistic that falls within the region determined by the level of significance, then we reject . Therefore, the region is called the critical region.
Understand the relationship between the Central Limit Theorem and the normal approximation of a sampling distribution. Introduction In the previous lesson, you learned that sampling is an important tool for determining the characteristics of a population. ) were unknown, random sampling was used to yield reliable estimates of these values. The estimates were plotted on graphs to provide a visual representation of the distribution of the sample means for various sample sizes. It is now time to define some properties of a sampling distribution of sample means and to examine what we can conclude about the entire population based on these properties.
65. The size of the standard error is a function of the population parameter. By looking at the formula , it is obvious that the standard error will increase as the quantity increases. Referring back to our example, the maximum for this product occurred when there was an even split in the population. When . If , then . Likewise, if , then . If were either 0 or 1 (none or all of the student body approves of the dress code), then the standard error would be 0. This means that there would be no variation, and every sample would give the same estimate.