Exam 2-3 Flashcards

0
Q

Sample

A

A subset of individuals in the population; the group about which we actually collect information.

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1
Q

Central Limit Theorem

A

When sampling from a non-Normal population, the sampling distribution of x bar is approximately Normal whenever the sample is large and random

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2
Q

Theoretical sampling distribution of x bar

A

The distribution of all possible samples of the same size from the same population

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3
Q

Approximate sampling distribution of x bar

A

The distribution of x bar values obtained from repeatedly taking SRS’s of the same size from the same population

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4
Q

Sampling distribution of x bar

  1. Center
  2. Spread
  3. Shape
A
  1. mean of x bar = population mean valid for all sample sizes and populations of all shapes
  2. Stand.deviation of x bar= stand dev of population decided by the square root of n
  3. Normal -shape of x bar distribution is exactly normal for any n; Non-Normal - shape of sampling distribution of x bar is approximately normal when n (sample size) is large
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5
Q

Facts about Sampling Distribution of x bar

A
  • Mean= mu regardless of population shape or sample size
  • standard dev of x bar is always less than the standard deviation of the population for samples of any size where n>1
  • standard dev of x bar gets smaller as n increases at rate square root of n. To cut stand dev in half, quadruple sample size
  • Shape is normal if population is normal for any sample size
  • shape is approximately Normal if we take a large random sample from a non-normal population
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6
Q

Standard deviation if x bar ( standard deviation of the sampling distribution of x bar)

A

A measure of variability of the values of the statistic x bar about mu ; a measure of the variability of the sampling distribution of x bar; in other words the average amount that statistic (x bar) deviates from it’s mean. Computed as sigma over square root of n

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7
Q

Predicting sampling distribution of x bar

A

Take only one sample of size n
Use results to make inference about the population
Because mean =mu and standard deviation of x bar= sigma over square root of n; and the shape is approx Normal if sample is random and large according to CLT

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8
Q

R-sq is a fraction of

A

Variation in the values of y that is explained by the least squares regression of y on x

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9
Q

Outlier in y direction of a Scatterplot have …… Residuals but other outliers need not to have large residuals

A

Large

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10
Q

Influential observations in x direction of Scatterplot are often ….. For the least-squares regression line

A

Influential

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11
Q

To add categorical variable to Scatterplot

A

Add different color or symbol for each category

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