307 exam 1

Question 1

descriptive analytics

Answer 1

what has happened?

ex: mean, max, min

Question 2

predictive analytics

Answer 2

what could happen?

ex: regression, time series analysis

Question 3

prescriptive analytics

Answer 3

what should we do?

ex: optimization, stimulation

Question 4

scatter plot

Answer 4

can be used to show the relationship between two variables

Question 5

correlation

Answer 5

used to measure the strength of the association of two variables

• correlation is only concerned with strength of the relationship
• no casual effect is implied with correlation

Question 6

regression analysis

Answer 6

used to:
- predict the value of a dependent variable based on the value of at least on independent variable
- explain the impact of changes in an independent on the dependent variable

Question 7

X and Y

Answer 7

X = ind. variable
Y = dependent variable

relationship between X and Y id described by a linear function

Question 8

Linear vs. Curvilinear realtionships

Answer 8

linear = straight
curvilinear = curved

Question 9

multiple regression

Answer 9

explores the relationship between two or more independent X variables and the dependent Y variable

Question 10

positive curvilinear relationship

Answer 10

rises rapidly, but then levels off as time goes on

ex: age and maintenance of a vehicle

Question 11

U shaped curvilinear relationship

Answer 11

as X increases. Y declines at first, but then begins to increase

ex: entrepreneurial activity and GDP per capita

Question 12

exponential relationship

Answer 12

Y decreases very rapidly at first, but then slows down

ex: value of an automobile and its age

Question 13

least squares method

Answer 13

determines the value of b0 and b1 that minimize the sum of the squared differences around the prediction line

Question 14

regression analysis: relevant range

Answer 14

represents all values from the smallest to the largest X used in developing the regression model. When predicting Y for a given value of X, you cannot extrapolate beyond this range of X values

Question 15

Measures of variation: SST

Answer 15

total sum of squares:

measure of variation of y values around their mean, predicted Y

Question 16

total variation: SST

Answer 16

a quantity that measures the total amount of variation exhibited by the observed values of the dependent variable Y

SST = SSP +SSE

Question 17

explained variation: SSR (regression sum of squares)

Answer 17

represents variation that is explained by the relationship X an Y

Question 18

unexplained variation: SSR (error of sum of squares)

Answer 18

represents variation due to factors other than the relationship between X an Y