Ch 1-5
Basic Probability
Experimental
Design
Sampling Distribution
Parameter Estimation
100

What are the objectives of Statistical Analysis? BONUS- What is the biggest challenge?

-The objective is to collect, organize and interpret data according to well defined procedures -The biggest challenge is to recognize the types of analysis appropriate for a given situation and how to interpret and apply results

100
What is the difference between theoretical and empirical probability? 

Theoretical probability is the determined with a good degree of confidence and is based on prior experience, such as coins prob of being heads is 1/2, while empirical probability is estimating the relative frequency of occurrence of an event rests on our past observations and are overall subjective. p 75,76

100

What is an experimental studies?

An experimental study there is a variable that is manipulated in a controlled way or with defined procedures

100

What would be the binomial expression for (p+q)4

1p^4+ 4(p^3q)+ 6(p^2q^2)+  4(pq^3)+ 1(q^4) p 122
100

Which estimation is associated with the long run average or expected value of the estimate will equal the population parameter 

Point Estimation p 134

200

What is the difference between relative frequency and cumulative frequency 

relative frequency is the percentage of an object while the cumulative frequency is the running total of objects being reviewed p.31

200

What is the importance of random sampling and describe the steps of a stratified random sampling? 

To reduce bias in an experiment. p 77  A stratified random sampling is similar to distinct groups called stratas then randomly taking a random sample from each strata. The point of stratified sampling is to proportionally represent a diverse sample in the study

200

Define the response, explanatory, and extraneous variables? 

Response variables are the variables whose changes we are recording, the explanatory variable is the variable causing the changes and the extraneous variable are the "extra" variables

200

What is the Normal Distribution Sampling Theorem?

If a variable x is normally distributed with a mean mu and a standard deviation sigma, then the sample distribution of the mean x bar, based on random samples of size n, will also be normally distributed and have a mean mu and a standard deviation for the sample mean. p 108

200

Which estimation would be use if we would like to know how close the true parameter may be to the true parameter? And when would we use the other one? 

Interval estimation. and when we need the single best guess for a numerical value p 135

300

If the mean is greater than the median and mode is less than the median what can you infer about the distribution?

The distribution is skewed right p. 48

300
Describe what a sample space would look like for a mutually exclusive outcome, a mutually exhaustive outcome. And describe the ways that outcome can be changed?

All of the instances are in their own circles and none are outside of the circles. The outcome could be mutually exclusive and not exhaustive or vice a versa  or neither. p78

300

What is the difference between spurious and confounding variables?

The spurious correlation is a misleading association where x directly causes y while the cofounding is multiple variables one of which influences both the the explanatory and responding variables. Lecture

300

What is the Central Limit Theorem?

If a variable, x, has a distribution with a mean mu and a standard deviation sigma then the sample distribution of the mean x bar, based on random samples of a size n, will have a mean equal to mu and a standard deviation and will tend to be normal in form as the sample size becomes large p 111

300

Fill in the blank: the variation of the unknown estimation errors is exactly the same as the _____________, the standard error of the means is also the standard deviation of the distribution of the __________. 

Variation of the sample means, sampling errors

400

What is the difference between variation and standard deviation? Bonus- what is the difference between symbol for sample and population variance and standard deviation?

Variation is the extent to which observations differ among themselves in value p 54 and standard deviation is the square root of the variance p 59 sigma and s

400

What is an example of a permutation and what is an example of a combination probability problem?


A combination problem does not consider order while a permutation problem does. So a permutation problem value of n decreases after each iteration, ex winning a race, while combination is the overall possibilities and has the same number of chances of being selected for instance picking a name out of a hat when the selected names are put back p 462 & 466

400

What is an observational study?

A study that uses the observations to collect data, there is no changes imposed by the scientists.

400

Define what is the theorem for sampling two independent populations?

If two independent varaibles x1 and x2 are noramly distributed with respective mean u1 and u2 and standard deviations sigma 1 and sigma 2, then the sampling distributions of bar x1 - bar x2 will also be normally distributed with a mean u1- u2, and a standard error where n1 and n2 are the respective random sample size p114

400

What are the percentage for the standard deviation values and percentages? What is the terms for these values? 

Values:1.645, 1.96, 2.58, The percentages are: 90%, 95%, 99% Confidence intervals, confidence limits  p 139

500

Define Covariation?

When a object is measured on two or more variables we want to determine its association or define the relationship the object has with the two variables this can be associated related, correlated, dependent, interdependent, non-orthogonal, a function of one another, or that they covary p 67

500

What formula is this P(A| B) = P(A and B) \P(B)

Baye's Formula 

500

In the 12 days of Christmas, how many geese are laying?

6

500

How does size impact the sampling distribution? Which theorem tells us this?

The larger a a sample size the closer the distribution is to normal and the central limit theorem tells us about this phenomena p 133

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