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devry math 221 all weeks discussions

Question # 00006677
Subject: Mathematics
Due on: 01/31/2014
Posted On: 01/16/2014 10:36 AM

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Week 1 discussion

Descriptive Statistics (graded)

If you were given a large data set such as the sales over the last year of our top 1,000 customers, what might you be able to do with this data? What might be the benefits of describing the data?

Week 2 discussion

Regression (graded)

Suppose you are given data from a survey showing the IQ of each person interviewed and the IQ of his or her mother. That is all the information that you have. Your boss has asked you to put together a report showing the relationship between these two variables. What could you present and why?

Week 3 discussion

Statistics in the News (graded)

Keep your eyes and ears open as you read or listen to the news this week. Find/discover an example of statistics in the news to discuss the following statement that represents one of the objectives of statistics analysis: “Statistics helps us make decisions based on data analysis.” Briefly discuss how the news item or article meets this objective. Cite your references.

Week 4 discussions

Discrete Probability Variables (graded)

What are examples of variables that follow a binomial probability distribution? What are examples of variables that follow a Poisson distribution? When might you use a geometric probability?

Week 5 discussion

Interpreting Normal Distributions (graded)

Assume that a population is normally distributed with a mean of 100 and a standard deviation of 15. Would it be unusual for the mean of a sample of 3 to be 115 or more? Why or why not?

Week 6 discussion

Confidence Interval Concepts (graded)

Consider the formula used for any confidence interval and the elements included in that formula. What happens to the confidence interval if you (a) increase the confidence level, (b) increase the sample size, or (c) increase the margin of error? Only consider one of these changes at a time. Explain your answer with words and by referencing the formula.

Week 7 discussion

Rejection Region (graded)

How is the rejection region defined and how is that related to the z-score and the p value? When do you reject or fail to reject the null hypothesis? Why do you think statisticians are asked to complete hypothesis testing? Can you think of examples in courts, in medicine, or in your area?

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devry math 212 all weeks discussions

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Posted On: 01/16/2014 10:37 AM
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Tutorial Preview …statistics xx the xxxx to discuss xxx following statement xxxx represents xxx xx the xxxxxxxxxx of statistics xxxxxxxxx “Statistics helps xx make xxxxxxxxx xxxxx on xxxx analysis ” xxxxxxx discuss how xxx news xxxx xx article xxxxx this objective xxxx your references x probability xxxxxxxx xxxxx be xxxxx and Books x The Balls x 15 xxxxx xxx 5 xxxxx 10 Yellow xxx Books = xx There xxx xx Blue xxx 15 Yellow xxx probability of xxxxxxxxx a xxxx xxxxx be xxxxxx the total x of balls xxx of xxxxx xx 40 xxxxxx If we xxxx what is xxx probability xx xxxxxxxxx blue: xxxx would be xxxxx because there xxx 15 xxx xx 40 xxx things Now xxxxx the conditional xxxxxxxxxxxx If xx xxxx with xxxxx then we xxxx work with xxxx This xxxxx xx wrote: xxxxxx | blue) x 5/15 P(Book x Yellow) x xxxxx This xxxxx be an xxxxxxx of conditional xxxxxxxxxxx Week x xxxxxxxxxxx Discrete xxxxxxxxxxx Variables (graded) xxxx are examples xx variables xxxx xxxxxx a xxxxxxxx probability distribution? xxxx are examples xx variables xxxx xxxxxx a xxxxxxx distribution? When xxxxx you use x geometric xxxxxxxxxxxx xxx Binomial xxxxxxxxxxxx is one xx the discrete xxxxxxxxxxx distribution xx xx used xxxx there are xxxxxxx two mutually xxxxxxxxx outcomes xx x trial xxxxx outcomes are xxxxxxxxxxxxx labeled Success xxx Failure xxx xxxxxxxx Distribution xx used to xxxxxx the probability xx observing x xxxxxxxxx in x trials, with xxx probability of xxxxxxx on x xxxxxx trial xxxxxxx by p xxxxxxxx P(X = xx = xxx x r(1-p)n-r…
Week_1_discussion_all.docx (21.31 KB)
Preview: What xx the xxxxxxxxxxx of getting xxxxxxx 7 headsPoisson xxxxxxxxxxxxxxxxxxx distribution xx xxx of xxx discrete probability xxxxxxxxxxxx This distribution xx used xxx xxxxxxxxxxx the xxxxxxxxxxxxx for an xxxxx with the xxxxx average xxxx xx value(?) x poisson random xxxxxxxxxxx refers to xxx number xx xxxxxxx in x poisson experiment xxxxxxxxxxxxxx = e-??x / xxxxxxxxxxxxx is xx xxxxxxx rate xx value x xx a poisson xxxxxx variable x xx the xxxx of logarithm(e=2 xxxx Example:Consider, in xx office x xxxxxxxxx arrived xxxxx Calculate the xxxxxxxxxxxxx for exactly x customers xx xx arrived xx tomorrow  Geometric xxxxxxxxxxxxxxx where you xxx hypothesis xx xxxxxxxxx how xxxxx an outcome xxxx occur when xxx know xxx xxxxxxxxxx of xxx geometric shape x an example xxxxx be xxxxxxxx xxxxx where xxx know the xxxxxxxxx shape you xxxx to xxx xxx have xx determine the xxxx of the xxxxx it xxxx xx easiest xx target Week x discussionInterpreting Normal xxxxxxxxxxxxx (graded)Assume xxxx x population xx normally distributed xxxx a mean xx 100 xxx x standard xxxxxxxxx of 15 xxxxx it be xxxxxxx for xxx xxxx of x sample of x to be xxx or xxxxx xxx or xxx not? When data xx skewed to xxx right xxx xxxx will xx greater than xxx median There xx a xxxxxx xxx of xxxxxxx this: Always: mode xxxx meanAlways: mode xxxx medianMost xx xxx time: xxxx < median xxxx meanThe reason xxxx happens xx xxxxxxx the xxxxx median, and xxxx are all xxxxxxxx of xxx xxxxxx of.....
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