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Solutions Manual for Business Analytics 4th Edition By Jeffrey Cam1
Solutions Manual for Business Analytics 4th Edition By Jeffrey Cam1
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Solutions Manual for Business Analytics 4th Edition ByJeffrey Camm, James Cochran, Michael Fry, Jeffrey
Ohlmann (All Chapters, 100% Original Verified, A+
Grade)
What is the difference between analyzing residual plots for single variable regression
models and analyzing residual plots for multiple regression models - ANSWER
Single variable regression plots give insight into the gross relationship between the
independent and dependent variable, whereas multiple regression plots give insight
into the net relationship, controlling for the other independent variables included in
the regression model.
If an independent variable has a p-value of 0.07, which of the following could
represent the Lower 95% and the Upper 95% for that variable? - ANSWER Should
cross 0
The p-value, 0.07, is greater than 0.05 so the independent variable is not significant
at the 5% significance level. Therefore, the 95% confidence interval for the
coefficient of the independent variable must include zero. The interval between -
14.52 and 3.25 contains zero.
What does R-square indicate? - ANSWER R-square indicates what percentage of
the variability in the dependent variable is explained by the regression line
multicollinearity - ANSWER Multicollinearity occurs when two or more independent
variables are highly correlated
Multicollinearity is usually not an issue when the regression model is only being used
for forecasting
If the street fair organizer wanted to compare the explanatory power of the original
model and the following new regression model, which value should he consult for the
new model? - ANSWER It is important to use the Adjusted R2 to compare two
regression models that have a different number of independent variables.
Previous Question Question 3 of 20 Next Question
An airport shuttle company forecasts the number of hours its drivers will work based
on the distance to be driven (in miles) and the number of jobs (each job requires the
pickup and drop-off of one set of passengers) using the following regression
equation:
Travel time=-0.60+0.05(distance)+0.75(number of jobs)
On a given day, Victor and Sofia drive approximately the same distance but Sofia
has two more jobs than Victor. If Victor worked for 4 hours, for how long can the
company expect Sofia to work? - ANSWER 5.5
, The only difference between the workloads of the two drivers is the number of jobs
each has; Sofia has two additional jobs. Therefore the company can expect Sofia to
work the four hours Victor worked, plus an additional 0.75 hours for each of the two
additional jobs, that is, 4+0.75(2)=5.5 hours.
A sporting goods store manager wants to forecast annual sneaker revenues based
on the type of sport (running, tennis, or walking), color (red, blue, white, black, or
violet) and its target audience (men or women). How many independent variables
should the manager include in her multiple regression analysis? - ANSWER Sales
revenue is the dependent variable. Type of sport, color, and target audience are
categorical variables which must be represented using dummy variables. Recall that
it is necessary to use one fewer dummy variables than the number of options in a
category. Thus, type of sport should be represented by 3-1=2 dummy variables,
color should be represented by 5-1=4 dummy variables, and target audience should
be represented by 2-1=1 dummy variables, for a total of 2+4+1=7 independent
variables.
single variable linear regression - ANSWER to investigate the relationship between
a dependent variable and one independent variable.
A coefficient in a single variable linear regression characterizes the gross
relationship between the independent variable and the dependent variable.
multiple regression - ANSWER investigate the relationship between a dependent
variable and multiple independent variables.
ŷ =a+b1x1+b2x2+...+bkxk
Coefficients in multiple regression characterize relationships that are net with respect
to the independent variables included in the model but gross with respect to all
omitted independent variables.
R-squared vs adjusted R-squared - ANSWER Because R2 never decreases when
independent variables are added to a regression, it is important to multiply it by an
adjustment factor when assessing and comparing the fit of a multiple regression
model. This adjustment factor compensates for the increase in R2 that results solely
from increasing the number of independent variables.
Adjusted R2 is provided in the regression output.
It is particularly important to look at Adjusted R2, rather than R2, when comparing
regression models with different numbers of independent variables.
multicollinearity - ANSWER Multicollinearity occurs when there is a strong linear
relationship among two or more of the independent variables.
Indications of multicollinearity include seeing an independent variable's p-value
increase when one or more other independent variables are added to a regression
model.
We may be able to reduce multicollinearity b
dummy value - ANSWER Multiple regression models allow us to include multiple
dummy variables for categorical data—day of week, for example.
A dummy variable is equal to 1 when the variable of interest fits a certain criterion.
For example, a dummy variable for "Saturday" would equal 1 for observations
relating to Saturdays and 0 for observations related to all other days.
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