1
Business Analytics Module 5 Questions
and Verified Answers Updated 2025.
Graded A
=IF(B2="Monday",1,0) EXAMPLE, B column contains the weekdays names
- ANS DUMMY FORMULA IF
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? - ANS 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.
Adding a lagged variable is costly in two ways: - ANSEach lagged variable
creates an incomplete line of data. If we have a single lagged variable, our
first observation will be incomplete. If we have two lagged variables, our
first two observations will be incomplete, and so on. The loss of each data
point decreases our sample size by one, which reduces the precision of our
estimates of the regression coefficients.
1
, 2
In addition, if the lagged variable, or variables, do not increase the model's
explanatory power, the addition of the variable decreases Adjusted R2, just
as the addition of any variable to a regression model can.
Adjusted R square - ANSis another measure of explanatory power.
Adjusted R2 equals R2 multiplied by an adjustment factor. It is used when
comparing the explanatory power of regression models that have different
numbers of independent variables.
Although the linear relationship between distance and price is significant,
the relationship is fairly week - ANS15% is low compared to selling price on
house size, we obtained an R squared of 74%.
As with single variable linear regression, it is important to evaluate several
metrics to determine whether a multiple variable linear regression model is
a good fit for our data. - ANSAdjusted 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.
As with single variable regression models, if the underlying multiple
relationship is linear, the residuals follow a normal distribution with a mean
of zero and fixed variance. - ANSWe should also analyze the p-values of
the independent variables to determine whether there is a significant
relationship between the variables in the model. If the p-value of each of
the independent variables is less than 0.05, we conclude that there is
sufficient evidence to say that we are 95% confident that there is a
2
Business Analytics Module 5 Questions
and Verified Answers Updated 2025.
Graded A
=IF(B2="Monday",1,0) EXAMPLE, B column contains the weekdays names
- ANS DUMMY FORMULA IF
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? - ANS 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.
Adding a lagged variable is costly in two ways: - ANSEach lagged variable
creates an incomplete line of data. If we have a single lagged variable, our
first observation will be incomplete. If we have two lagged variables, our
first two observations will be incomplete, and so on. The loss of each data
point decreases our sample size by one, which reduces the precision of our
estimates of the regression coefficients.
1
, 2
In addition, if the lagged variable, or variables, do not increase the model's
explanatory power, the addition of the variable decreases Adjusted R2, just
as the addition of any variable to a regression model can.
Adjusted R square - ANSis another measure of explanatory power.
Adjusted R2 equals R2 multiplied by an adjustment factor. It is used when
comparing the explanatory power of regression models that have different
numbers of independent variables.
Although the linear relationship between distance and price is significant,
the relationship is fairly week - ANS15% is low compared to selling price on
house size, we obtained an R squared of 74%.
As with single variable linear regression, it is important to evaluate several
metrics to determine whether a multiple variable linear regression model is
a good fit for our data. - ANSAdjusted 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.
As with single variable regression models, if the underlying multiple
relationship is linear, the residuals follow a normal distribution with a mean
of zero and fixed variance. - ANSWe should also analyze the p-values of
the independent variables to determine whether there is a significant
relationship between the variables in the model. If the p-value of each of
the independent variables is less than 0.05, we conclude that there is
sufficient evidence to say that we are 95% confident that there is a
2
