Review Material for Quiz 1 - 3 (one-sample tests)

# Review Material for Quiz 1 - 3 (one-sample tests) - 1...

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Statistics 101A Professor Esfandiari Week 1 Review Material The objective of this lecture is to: Review of concepts the are prerequisites to this lecture Introduce you to sampling distribution o How it is constructed o How it is different from the sample distribution o How it is different from the population distribution o The sampling distribution of the mean, o The sampling distribution of the percentage o The importance of sampling distribution in statistical inference Central Limit Theorem (CLT) o The importance of CLT in statistical inference o The relationship between the normal model and CLT The concept of standard error o The standard error of the sampling distribution of the sample mean o The standard error of the sampling distribution of the percentage The relationship between standard error and sample size The differences and similarities between standard deviation and standard error Show you how to make inference about the population mean and proportion via the confidence interval I Review of concepts the are prerequisites to this lecture Population : Population includes all the potential elements that could be part of a study. Population could be finite; i.e. we know how many elements are included in the population. Population could be infinite ; we do not know how many elements are included in the population. Examples of finite populations : All the high school students in the United States. All of the students who major in political science in state universities in the United States. Examples of infinite populations: The number of pebbles in a certain beach. The number of trees in a certain jungle, etc. Samples are a fraction of the population and we already discussed methods of choosing unbiased and representative samples. Parameters are used to describe a population. Statistics are used to describe a sample. 1

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relates to describing the sample . So, calculating the mean, median, mode, variance, standard deviation, and correlation for a sample of size n are examples of descriptive statistics. Inferential statistics is about generalizing the results from the sample to the population. For example, when we make a statement such as; "We are 95% confident that this drug lowers blood pressure between 10 to 20 points", we are not making this statement about a sample of size n. We are implying that these results are true for the population of individuals who have high blood pressure. Thus, we are generalizing the findings from the sample of size n to the population. II Introduction to the concept of sampling distribution Three distributions to keep in mind A Distribution of a quantitative variable in the sample – histogram of data resulting from a single sample In order to create this distribution, you need the X scores for a sample (assuming it is random) of n objects/individuals from the population of interest. Mean =
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Review Material for Quiz 1 - 3 (one-sample tests) - 1...

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