Introduction to CI (estimation)
Point Estimate: Using one # to rely on as our estimate for a population parameter.
In our case, the point estimate is _X.
The population parameter is M.
What determines how wide the interval around _X should be?
1. Find the requested value.
A researcher for a car insurance company wishes to estimate the mean annual premium
that men aged
for their car insurance. A random sample of 16 men aged between 20
and 24 yields the following annual premiums, in do
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Is Wal-mart Good for America?
Answer the following questions as you view the documentary.
1. What did you notice at the Wal-Mart shareholders meeting? Do you think this is
different from other shareholders' meetings? If yes, in what ways?
MAT215 - Professor Mankowski
Hypothesis Testing Writing h0 and h1
Instructions: For each situation below, write out both the null and alternative hypothesis
An Atlantic City casino manager finds a way to rearrange the floor in such a way as to
Take-Home Computational Quiz
a. The student makes more than 15,000.
M = 12,837 SD = 1,500
15,000 12,837 = 2,163 / 1,500 = 1.44
P(z > 1.44) = 0.9251
1 0.9251 = 0.0749 = 7.49%
b. The student makes between 13,000 an
Central Limit Theorem
If we take an indefinite amount of samples of size n from a population where n >= 30.
1. Distribution of all sample averages is normal.
2. Average of all sample averages = M
3. Standard Deviation = SD / sqrtN.
How do we know its time
Process that leads to a well-defined results called outcomes.
Set of all possible outcomes.
o Flipping 3 coins.
o Rolling a die for a number divisible by 3.
o Guessing a true/false question.
An event is
9. H0: M = 26,035
H1: M = 27,690
CR = .05
Assuming H0 is really true, the distribution of _Xs curve (median = 26,025)
Critical Region Tells when to accept/reject H0. CR comes from Level of Significance
To find out
Addition Rule Used when we see or.
Are we mutually exclusive?
If we are not mutually exclusive: P(A or B) = P(A)+P(B)-P(AnB)
Discrete Random Variables Random variable whose values can be listed.
Example: X = # of As at end of term.
X = letter in alphabet
g. Identify and interpret the (rough) shape of your histograms in part (f) in terms of
modality, symmetry, and skewness.
h. This week, we learned about how we organize descriptive data. Are there any
applications from this weeks content to your major, you