Maths

Question 1

Describe 4 types of data

Answer 1

1. Ratio - data is interval data with a natural zero point. For example, time is ratio since 0 time is meaningful. Degrees Kelvin has a 0 point (absolute 0) and the steps in both these scales have the same degree of magnitude.
2. Interval - like ordinal except we can say the intervals between each value are equally split (distance is meaningful). The most common example is temperature in degrees Fahrenheit. The difference between 29 and 30 degrees is the same magnitude as the difference between 78 and 79 (although I know I prefer the latter). With attitudinal scales and the Likert questions you usually see on a survey, these are rarely interval, although many points on the scale likely are of equal intervals.
3. Ordinal - efers to quantities that have a natural ordering. The ranking of favorite sports, the order of people’s place in a line, the order of runners finishing a race or more often the choice on a rating scale from 1 to 5. With ordinal data you cannot state with certainty whether the intervals between each value are equal. For example, we often using rating scales (Likert questions). On a 10 point scale, the difference between a 9 and a 10 is not necessarily the same difference as the difference between a 6 and a 7. This is also an easy one to remember, ordinal sounds like order.
4. Nominal - basically refers to categorically discrete data such as name of your school, type of car you drive or name of a book. This one is easy to remember because nominal sounds like name (they have the same Latin root).

Question 2

Enumeration or census v. sample

Answer 2

Census refers to the quantitative research method, in which all the members of the population are enumerated. On the other hand, the sampling is the widely used method, in statistical testing, wherein a data set is selected from the large population, which represents the entire group.

Question 3

Primary Data vs. Secondary Data

Answer 3

Primary data refers to the first hand data gathered by the researcher themself. Surveys, observations, experiments, questionnaire, personal interview, etc.

Secondary data means data collected by someone else earlier. Government publications, websites, books, journal articles, internal records etc.

Question 4

Mean

Answer 4

Measure of central tendency. = Sum of items / Count of items

Question 5

Median

Answer 5

Measure of central tendency. Sort items high to low and select middle item.

Question 6

Mode

Answer 6

Which value occurs most often.

Question 7

Bimodal distribution

Answer 7

When two clearly separate groups are visible in a histogram, you have a bimodal distribution. Literally, a bimodal distribution has two modes, or two distinct clusters of data.

Question 8

Range

Answer 8

= High value minus low value

Question 9

Variance

Answer 9

Subtract the mean from each value. Square the difference. Sum the squares of the differences and divide by the number of cases.

Question 10

Standard deviation

Answer 10

Square root of the variance.
Measure of the amount of variation of a random variable expected about its mean. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range.

Question 11

Normal distribution

Answer 11

Bell-shaped curve, where each band has a width of 1 standard deviation

Question 12

Dependent variable (y variable)

Answer 12

Variable being predicted or explained

Question 13

Independent variable (x variable)

Answer 13

Variable used to predict or explain.

Question 14

Difference between bivariate regression and multiple regression

Answer 14

How many variables used to predict. Bivariate = One X variable, while multiple = two or more X variables.
Bivariate would be used to predict number of automobiles per household.
Multiple regression would be used to predict house sale price, based on a number of factors including bedroom and bathroom count, accessibility to employment, etc.

Question 15

Regression

Answer 15

Assumes a straight line can be used
to describe the relationship between
the independent (x) variable and the
dependent (y) variable.
▪ y = a + bx (or y = mx + b)
▪ a is the line’s y intercept
▪ b is the line’s slope
▪ R2 measures how well the line fits the
data and ranges from 0.0 to 1.0

Question 16

Cohort component model

Answer 16

We divide the population into cohorts by age (often
five years), sex, and race/ethnicity.
▪ Population change is subdivided into three
components: births, deaths, migrants
▪ Calculate birth rates, survival rates, and migration
rates for a recent period
▪ Extend those rates into the future, possibly adjusting
them upward or downward
▪ Birth and death data is readily available; migration
data is difficult, primary source is American
Community Survey

Question 17

Economic base
theory

Answer 17

Assumes two kinds of industry
 Basic or export: sells to customers outside
the area of analysis
 Service or non-basic: sells to customers
within the area

Question 18

Economic base multiplier

Answer 18

Total employment / basic employment
 A multiplier of 4.0 says that 4 total jobs are
created for every additional basic job

Question 19

Location quotients

Answer 19

Compare the local
concentration of
employment in an industry to the
national employment in that industry
▪ LQi =
Local percent of employment in industry i
National percent of employment in industry i

If LQi is greater than 1.0 we can assume an
export or basic industry
 If LQi is less than 1.0 we can assume we import some goods or services
 If LQi = 1.0, the region produces just enough to serve the region, and no more

Question 20

Shift share analysis

Answer 20

interprets changes in an
industry’s local employment (over a period of x
years) in terms of three components:
 National share: how much would local industry
employment have changed if it mirrored the
change in total national employment
 Industry mix: how much additional would it have
changed if it mirrored the difference between
national industry employment and national total
employment
 Local shift: how many additional jobs did the local
industry gain or lose, presumably due to local
competitive advantage or disadvantage.