Question;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?;Post-Class Topic: Social Media's Use of Data (graded);This century is already being;characterized as the era of ?big data.? You are probably active or at;least knowledgeable about the proliferation of various social media;outlets, like Facebook, Twitter, LinkedIn and flickr. Do you feel like;too much personal data is retained forever? Do you have any concerns;about how your personal data is used? Or, are you satisfied that most;studies based on personal data collected by large companies maintain;sufficient controls and respect an individual?s privacy by only;publishing aggregate figures (or ?statistics?) which summarize trends?;(There is no correct answer, just informed opinions.);Week 2Regression (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?Post-Class Topic: Correlation and Causation (graded);If two variables are strongly correlated, does it necessarily;always follow that there is a direct cause-and-effect relationship;between the two variables? Can you think of two variables which are;often associated with each other and are highly correlated, but there is;no direct;cause-and-effect relationship between them? For example, do you think it;is a correct conclusion that watching soap operas gives girls eating;disorders like anorexia if a study showed that ?girls who watch soap;operas are more likely to have eating disorders.?;Week 3Statistics 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;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?Post-Class Topic: Interpreting the ?Most Likely? outcome of a Binomial (graded);Do;you think that the ?most likely? outcome in a binomial distribution is;the outcome that will occur most of the time?? For example, what is the;?most likely? composition of a four-member committee chosen randomly;from a large population that is 50% women and 50% men? What is the;probability of the committee composed by two mean and two women? What is;the probability of the committee containing one man and three women?;What is the probability of the committee containing three men and one;woman?;Week 5Interpreting 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?Post-Class Topic: Central Limit Theorem (graded);Explain what property associated;with the Central Limit Theorem you consider the most important;contribution, enabling the use of the normal distribution for sample;means with large sample size.Week 6;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.;Post-Class Topic: Confidence Intervals and Hypothesis Testing (graded);The EPA will grant a tax credit if the city-highway mileage estimate is at least 31 mpg.Construct;the 95% and 99% confidence intervals for the mean mpg [miles per;gallon] if we have a data sample with 49 observations of mileage of a;new car model, with x-bar = 31.5531 mpg and known std. dev. sigma = 0.8;mpg. Which CI is wider and why? Would the EPA grant a tax credit if the;99% CI is (31.26, 31.85) and why? This is an example of hypothesis;testing using CIs. If the EPA minimum qualifying mileage were 33 mpg;instead of 31 mpg, would the EPA grant a tax credit with the same 99%;CI?Week 7Rejection 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?
Paper#60535 | Written in 18-Jul-2015Price : $42