CH. 3 part 2
Understand how to identify and interpret distribution shape according to the notes (symmetry, skewness).
Q 3.1: If my data (histogram) has a skewness of 1.4, is it
a) highly left skewed
b) moderately left skewed
c) symmetric
d) moderately right skewed
e) highly right skewed
Q 3.2: If my data (histogram) has a skewness of -0.7, is it
a) highly left skewed
b) moderately left skewed
c) symmetric
d) moderately right skewed
e) highly right skewed
What is the z-score? What does it tell us? How do you calculate it?
Q 3.3 I have a sample of data with mean of 7, standard deviation of 2. The ninth value recorded is equal to 6.5. What is ?
What is Chebyshev’s Theorem? What does it say about values within 2, 3, and 4 standard deviations of the mean? For other distances from the mean?
Q 3.4 According to Chebyshev’s Theorem, at least what proportion or percentage of values is within 2.5 standard deviations of the mean for any distribution?
What is the empirical rule? For what distribution types does it apply?
What is the definition of an outlier? How do you detect an outlier?
Q 3.5 I have a sample of data with mean 10, standard deviation 2, and the largest value in the data set is 16.1. Is this value considered to be an outlier?
What is covariance? What is correlation? Calculate these.
Q 3.6: What is covariance and correlation for the following two samples of data?
Pepsi sales: 20, 21, 22, 23, 24
Pepsi price: .8, .7, .6, .5, .4
Chapter 4
What is an experiment? A sample space?
How do you count the number of experimental outcomes?
combinations (see HW for practice)
permutations (see HW)
Assigning probabilities: classical method, relative frequency method
complement of an event, union of two events, intersection of two events, mutually exclusive events
conditional probability ; multiplication law
independent events