Statistical Methods- Lecture 5-6-7 Flashcards

1
Q

What is a contingency table?

A

A table that summarizes data for two categorical variables is called a contingency table.

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2
Q

What are two differences between bar plots and histograms?

A

1) Bar plots can be used for displaying distributions of categorical variables, while histograms are used for numerical variables
2) The x-axis in a histogram is a number line, hence the order of the bars cannot be changed, while in a bar plot the categories can be listed in any order

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3
Q

What is the null and alternative hypothesis?

A

Null hypothesis means that there is no difference

Alternative hypothesis is when there is a difference

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4
Q

What do we use for the unknown population parameters of interest?

A

We use sample statistics as point estimates for the unknown population parameters of interest.

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5
Q

What are sample proportions?

A

Sample proportions will be nearly normally distributed with mean equal to the population proportion, p, and s.d. is Equal to (p(1-p)/n)^1/2

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6
Q

What is the standard deviation of the sampling distribution called?

A

The standard deviation of the sampling distribution of a point estimate is called the standard error of the point estimate.

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7
Q

What conditions need to be met for Central Limit Theorem to apply?

A

Sampled observations must be independent, which they are more likely be if random sampling is used and if sampling without replacement, n<10% of the population

There should be at least 10 expected “successes” and 10 expected “failures” in the observed sample.

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8
Q

What does the CLT state?

A

The CLT states SE=(p(1-p)/n)^1/2

With the condition that np and n(1-p) are at least 10

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9
Q

What happens when the conditions are not met?

A
  • if either np or n(1-p) is small, the distribution is more discrete
  • when np or n(1-p)<10, the distribution is more skewed
  • the larger both np and n(1-p), the more normal the distribution
  • When np and n(1-p) are both very large, the discreteness of the distribution is hardly evident, and the distribution looks more like a normal distribution
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