OIS 2340 MW 2:00 – 3:20
OIS 2340 Midterm 2 Study Guide
Chapter 6 Continuous Probability Distribution
1. Discrete probability distribution – the probability will be an exact number. Classic die example.
2. Continuous probability distribution – the probability will fall between an interval. There is no such thing as an exact number.
3. What you need to calculate Z-scores (both population and sample): a. x = data value Don't forget about the age old question of What is the state of the trait that corresponds to the highest fitness tends to be concentrated around the mean?
b. µ = population mean
c. σ = population standard deviation
d. x = data value ( for sample z score) If you want to learn more check out What are the medium veins?
e. x(bar) = sample mean
f. s = standard deviation We also discuss several other topics like Explain why innovation matters?
g. Population z score: σ− μ
x xbar Z−
h. Sample z score: s
4. Normal Distribution If you want to learn more check out Does law of karma work?
a. Bell shaped; unimodal
b. 50 – 50 on each side
c. Mean, median, and mode are equal
5. Variations in normal distribution
a. Change in mean shifts distribution left or right
b. Changing standard deviation increases/decreases the spread of the curve 6. Standard Normal Distribution
a. Normal distribution; mean = 0; standard deviation = 1
b. Values above mean have positive z values
c. Values below mean have negative z values
OIS 2340 MW 2:00 – 3:20 We also discuss several other topics like What is oxygen binding capacity?
d. For the example, look at slide 13 in Ch6 PPT
7. Normal Probabilities
a. P(x) is equal to 0, when x is a continuous variable
b. Probability for a range of values, lets say x1 and x2, is the area under the curve between x1 and x2
c. Standard Normal Distribution Table – provides probabilities associated with z values 8. To have an easier time with calculations
a. Know the z-score formulas
b. Have the table ready!
c. Have the intuition; draw the bell curve
d. Review the ppt slides that go over the examples – not too hard!
Chapter 7 Intro to Sampling Distributions
1. Differentiate between Pop. Mean and Sample Mean
a. Population Mean: µ = (∑ x)/N
• x = values in population
• N = population size
b. Sample Mean: xbar = (∑ x)/n
• Xbar = sample mean
• x = Selected sample values
• n = sample size
2. Sample Error – difference between a statistic and a parameter We also discuss several other topics like What is metaphysical theory?
3. Average Value of Sample Means – average value of sample means will equal the population mean
4. Sampling Distribution – distribution of possible values will be randomly selected from population.
5. Standard Deviation of Sample Means – also called standard error
a. Know the formula
6. I don’t think most of this was covered in class. Go to the review session for thorough preparation for the midterm; Go to Class on Monday