MGSC 291 EXAM 2 QUESTIONS & ANSWERS
For the Bike Share csv:
How many days of data do we have? - Answers -731
For the Bike Share csv:
Why is a linear regression model appropriate for this assignment?
a. Because the predictor variables are categorical
b. Because the predictor variables are continuous
c. Because the response variable is continuous
d. Because the response variable is binary - Answers -c. Because the response
variable is continuous
For the Bike Share csv:
What does the R-squared value of simplefit model tell you?
a. Approximately 9.92% of the variability in ride count is explained by the simplefit
model.
b. Approximately 8.02% of the variability in ride count is explained by the simplefit
model.
c. Approximately 9.92% of the variability in weathersit is explained by the simplefit
model.
d. Approximately 8.02% of the variability in weathersit is explained by the simple fit
model. - Answers -a. Approximately 9.92% of the variability in ride count is explained
by the simplefit model.
For the Bike Share csv:
How do we interpret the coefficient of weathersitwet in the simplefit model?
a. On wet days, the expected ride count increases by approximately 3,073.5 compared
to clear days.
b. On wet days, the expected ride count decreases by approximately 3,073.5 compared
to clear days.
c. On wet days, the expected ride count increases by approximately 3,073.5 compared
to cloudy days.
d. On wet days, the expected ride count decreases by approximately 3,073.5 compared
to cloudy days. - Answers -b. On wet days, the expected ride count decreases by
approximately 3,073.5 compared to clear days.
For the Bike Share csv:
Using the ridefit model, what is the expected impact of temperature on the ride count?
, a. For every 1 unit increase in the ride count, we expect the temperature to increase by
approximately 155.98 degrees.
b. For every 1 degree increase in temperature, we expect the ride count to decrease by
approximately 155.98 rides.
c. For every 1 degree increase in temperature, we expect the ride count to increase by
155.98 rides.
d. For every 1 unit increase in the ride count, we expect the temperature to decrease by
approximately 155.98 degrees. - Answers -c. For every 1 degree increase in
temperature, we expect the ride count to increase by 155.98 rides.
For the Bike Share csv:
logridefit is known as a _____________ model.
a. log-log
b. log-linear
c. logistic - Answers -b. log-linear
For the Bike Share csv:
Using the logridefit model, which of the following is interpreted as the impact of
temperature on expected ride count?
a. 0.045
b. 1.045 - Answers -b. 1.045
For the Bike Share csv:
Using the logridefit model, what is the expected impact of temperature on ride count?
a. For each 1 degree increase in temperature, we expect ride count to increase by
0.045.
b. For each 1 degree increase in temperature, we expect ride count to increase by
4.5%.
c. For each 1 degree increase in temperature, we expect ride count to decrease by
0.955.
d. For each 1 degree increase in temperature, we expect ride count to decrease by
95.5%. - Answers -b. For each 1 degree increase in temperature, we expect ride count
to increase by 4.5%.
For the Bike Share csv:
The loglogfit model is known as a ____________ model.
a. log-log
b. log-linear
c. logistic - Answers -a. log-log
For the Bike Share csv:
Using the loglogfit model, what is the expected impact of temperature on the ride count?
For the Bike Share csv:
How many days of data do we have? - Answers -731
For the Bike Share csv:
Why is a linear regression model appropriate for this assignment?
a. Because the predictor variables are categorical
b. Because the predictor variables are continuous
c. Because the response variable is continuous
d. Because the response variable is binary - Answers -c. Because the response
variable is continuous
For the Bike Share csv:
What does the R-squared value of simplefit model tell you?
a. Approximately 9.92% of the variability in ride count is explained by the simplefit
model.
b. Approximately 8.02% of the variability in ride count is explained by the simplefit
model.
c. Approximately 9.92% of the variability in weathersit is explained by the simplefit
model.
d. Approximately 8.02% of the variability in weathersit is explained by the simple fit
model. - Answers -a. Approximately 9.92% of the variability in ride count is explained
by the simplefit model.
For the Bike Share csv:
How do we interpret the coefficient of weathersitwet in the simplefit model?
a. On wet days, the expected ride count increases by approximately 3,073.5 compared
to clear days.
b. On wet days, the expected ride count decreases by approximately 3,073.5 compared
to clear days.
c. On wet days, the expected ride count increases by approximately 3,073.5 compared
to cloudy days.
d. On wet days, the expected ride count decreases by approximately 3,073.5 compared
to cloudy days. - Answers -b. On wet days, the expected ride count decreases by
approximately 3,073.5 compared to clear days.
For the Bike Share csv:
Using the ridefit model, what is the expected impact of temperature on the ride count?
, a. For every 1 unit increase in the ride count, we expect the temperature to increase by
approximately 155.98 degrees.
b. For every 1 degree increase in temperature, we expect the ride count to decrease by
approximately 155.98 rides.
c. For every 1 degree increase in temperature, we expect the ride count to increase by
155.98 rides.
d. For every 1 unit increase in the ride count, we expect the temperature to decrease by
approximately 155.98 degrees. - Answers -c. For every 1 degree increase in
temperature, we expect the ride count to increase by 155.98 rides.
For the Bike Share csv:
logridefit is known as a _____________ model.
a. log-log
b. log-linear
c. logistic - Answers -b. log-linear
For the Bike Share csv:
Using the logridefit model, which of the following is interpreted as the impact of
temperature on expected ride count?
a. 0.045
b. 1.045 - Answers -b. 1.045
For the Bike Share csv:
Using the logridefit model, what is the expected impact of temperature on ride count?
a. For each 1 degree increase in temperature, we expect ride count to increase by
0.045.
b. For each 1 degree increase in temperature, we expect ride count to increase by
4.5%.
c. For each 1 degree increase in temperature, we expect ride count to decrease by
0.955.
d. For each 1 degree increase in temperature, we expect ride count to decrease by
95.5%. - Answers -b. For each 1 degree increase in temperature, we expect ride count
to increase by 4.5%.
For the Bike Share csv:
The loglogfit model is known as a ____________ model.
a. log-log
b. log-linear
c. logistic - Answers -a. log-log
For the Bike Share csv:
Using the loglogfit model, what is the expected impact of temperature on the ride count?
