# IMC Exam 2 1-4

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 Author: eriklnelson ID: 140717 Filename: IMC Exam 2 1-4 Updated: 2012-03-09 16:49:12 Tags: Formulas Folders: Description: Formulas Show Answers:

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1. examples of non-random sampling
• quota sampling
• panelling
• postal and telephone surveys
2. continuous data
data that can take any value whatsoever
3. discrete data
data that can only take on specific values, such as money, which changes value in whole units
4. categorical data
data classified into a number of distinct categories
5. ordinal data
data that has been classified into a number of distinct ranked categories
6. frequency distribution & relative f d
• group the data into bands of specific values and display the frequency of occurrence of each band
• -relative show proportions or percentages rather than frequencies
7. cumulative f d & relative cum f d
• can be used to show the number of items with a value less than or equal to a given figure.
• relative show the proportion or % with a value less than or equal to a given figure
8. lorenz curve
visual comparison of the 2 cumulative frequency distributions
9. histogram
displays the number or % of items falling within a given band through the AREA of a bar
• arithmetic mean
• -not necessarily an observed value
• -greatly affected by extremes
• Population
• -for raw data fi = 1 for each item
• -for grouped data, use the midpoints
• -for sample standard deviation, devide by (n-1) rather than n
• Geometric mean
• -useful for COMPOUNDING relationships
• -UNDERSTATES the mean compared to the arithmetic mean
10. value =
value =
value =
formulas for median, range and intertquartile range
11. = median

-UNAFFECTED by extremes
12. range =
highest - lowest

-considers ONLY EXTREMES
13. interquartile range =

-UNAFFECTED by extremes
14. mode
• most frequently occurring item
• -must be observed value
• -UNAFFECTED by extremes
15. perfect symmetrical population
mean = median = mode
16. positively skewed distribution
• mode<median<mean
17. negatively skewed distribution
• mean<median<mode
18. regression is used to
establish if a relationship exists between two factors, a regression calculation provides a l ine of best fit
19. y = a + bx
• linear regression
• a = the intercept i.e. the height at which the line cuts the y-axis
• b = slope
20. interpolation
where we use the regression line to estimate future results from data within an already observed range
21. extrapolation
where we try to estimate results from beyond our existing range of experiences and data
22. APR
annual percentage rate
23. (1+APR) = (1 + r/n)^n
• interest rate
• r = annual flat rate
• n = number of compounding periods pa
24. R = e^rt -1
continously compounded interest rate
25. (1+r)^n
basic discounted cash flow compound factor with n years to run
26. Dn = Do (1 + r)^n
depreciation where r is negative
27. 1/(1+r)^n
basic discount factor
28. 1/r (1-1/(1+r)^n)
discount annuity
29. 1/r
discount in perpetuity
30. 1/e^rt
continous compounded basic discount factor