PLAN MAKING & IMPLIMENTATION

Question 1

Population concepts

Answer 1

Migration - People moving and out of a community
Natural Increase - More births than deaths
Step Down Method - Use projections from larger entity
Cohort Survival Method - Multiple factors (cohorts)

Question 2

Projecting populations

Answer 2

Electric meters / Utilities

Question 3

Mathematical / Graphic techniques

Answer 3

Linear -
Exponential - Percentage
Gompertz Curve - Period of rapid growth then leveling

Question 4

Birth Rate

Answer 4

2.1 birth rate necessary to maintain population size

Europe under 2, the US at 2.

Question 5

Data Sources

Answer 5

US Bureau of census, Fedstats, National center for health statistics, state level dept of health

Question 6

Employment Data

Answer 6

US Bureau of census, counter business patterns, census transport planning package

Question 7

Economic Base Model - Multiplier Effect

Answer 7

How many additional jobs will be created by a new business? Is GM creating the new jobs? Not entirely, GM will cause other companies to create jobs.

Total Employment / Basic Employment
So every basic employee generates xxx employees. That person PLUS xxx employees.

Question 8

Economic Base Model - Economic Base Analysis

Answer 8

Tries to determine the multiplier effect using location Quotient.

(Regional Employment in Industry / Total Regional Employment) VERSUS (National Employment in Industry / Total National Employment)

If location quotient > 1 then exporting jobs. If location quotient < 1 then importing jobs. Only basic activities can be imported (manufacturing). Non basic can’t be imported (hairdresser).

Question 9

Shift-Share Analysis

Answer 9

Analyzes change in employment in given area / industry. Looks at two periods (ie between 1990 and 2000 a specific industry grew xxx percent while nationally xxx percent)

Question 10

Input-Output Analysis

Answer 10

US bureau of economic analysis creates multipliers for different sectors. Certain jobs will have certain impacts.

Regional input-output modeling systems (RIMS). Provides multipliers by region. (ie casino employment in Las Vegas, how much additional jobs, population, etc.)

Question 11

Statistics

Answer 11

Descriptive - No tests, just raw data.

Inferential - Use of sample information to tell us what is happening in the greater population and to measure the degree of uncertainty involved.

A sample statistic (english letters) to estimate or make an inference of a greater population parameter (greek letters).

Question 12

Statistics - Speed of vehicles example

Answer 12

Descriptive - Speed of each vehicle collected

Inferential - When broken down into speed classes, we can see how many cars were travelling between xxx and xxx mph (frequency), we can see what portion or percent of total cars were travelling under xxx mph (cumulative frequency), we can see what portion of the traffic was travelling at xxx mph (relative frequency), we can see what total portion of traffic was travelling under xxx mph (cumulative relative frequency).

Question 13

Categorized Data

Answer 13

Frequency - count of occurrences (cases) in each category

Cumulative Frequency - count of cases up to and including this category

Relative Frequency - each category’s proportion of total Sums to 1.0

Cumulative Relative Frequency - proportion of total

Question 14

Histogram \ Bar Chart

Answer 14

Graphing of frequencies

Vertical Axis is frequency, relative frequency or cumulative frequency

Horizontal Axis is catogories

Bar chart of relative frequency, sum = 1.0

Question 15

Statistical Vocabulary

Answer 15

Cases, entities, observations = what you are studying (ie cars, drivers)

Variables = Characteristics of the cases that are different for different cases (they “vary” ie type of car, speed, volume)

Discrete Variable = Take only some values in range (ie number of children in household or number of fire stations)

Continuous Variable = Variable that can take any value within the range (ie 5 miles or 5.342343 miles)

Question 16

Measurement levels of Variables

Answer 16

Nominal - names categories, no value or order (ie race or marital status or car color)

Ordinal - ordered but don’t know size of gap between values (ie strongly agree to strongly disagree, 1st 2nd 3rd)

Interval - know order and size of gap (ie temperature)

Ratio - also has a true zero (ie income)

Question 17

Measurement levels and Analysis

Answer 17

Levels matter because each level has a specific kind of analysis that make sens for it. (ie race is nominal variable African American =1, White =2, Asian = 3, etc)

Question 18

Measures of Central Tendency

Answer 18

Central Tendency measures the center
Mean = The average
Median = The middle number of ordered list (best for skewed data ie income or speed where an outlier is throwing off the mean)
Mode = The most common (best for nominal or categorical data, okay for ordinal data, bad for continuous data)

Question 19

Measures of Dispersion

Answer 19

Dispersion measures how spread out things are around that center

Range = maximum value - minimum value

Question 20

Probability Concepts

Answer 20

Statistical Inference is based on being able to calculate probability of making an error

Probability of any event = proportion of that event out of all possibilities (ie relative frequency)

Relative Frequency (histogram) of a variable = probability distribution

Confidence Interval = 95% confident that the true mean of the population of car speeds fall between xxx and xxx mph. Can work backward to estimate needed sample size to achieve level of confidence required. Z table will tell you what sample size necessary.

Question 21

Inference - Hypothesis Testing (7 steps)

Answer 21

1. Try to reject the null - can never prove something true, but can prove something false.
2. If you reject the null, then alternative must be true.
3. If you cannot reject the null, then all you can say is that the null may be true.

Question 22

Regression Analysis

Answer 22

Ordinary Least Squares Regression (OLS) - Linear Relationship.

OLS Example…
(dependent = speed; independent = # of cars)
Expectation: more cars means lower speed. Regression equation found: speed = 68 mph - 1.02 ( # of cars). 0 cars, speed = 68 mph. Lose 1.02 mph with every additional car. 67 cars would reduce speed to 0.
Speed as a function of the number of cars.

Used to show relationship between Dependent Variable and Independent Variable

Does not prove causality, only statistical relationship.

Question 23

Sample Questions

Answer 23

OLS Example…
Is the housing stock substandard and to the degree it is substandard. The standard regression finds you have R squared of 0.43. What does this mean? 43% of the variation in the housing conditions is explained by the variables in analysis. NOT that 57% or 43% of housing stock is substandard.

Survey of household Example…
A survey of households to determine their attitudes about development had poor results. An accusation is made about inaccurate samples which don’t represent the city as a whole. What method would not improve the probability that a sample is accurate? Decrease sample size. By replicating the study, improving sampling techniques, using a less diverse population, and using multiple sample techniques would improve accuracy.

You hypothesize that introducing traffic calming measures will reduce average speed. You test your hypothesis, based on results what can you conclude regarding the null?