BADM 211 EXAMS SET 2026 PRACTICE
QUESTIONS AND ANSWERS FULL
SOLUTION VIEW AHEAD
●● MLR:
Answer: -Linear functional form
-Flexible; can be applied for predictive and explanatory modeling
-Caution: we need to avoid collinearity
●● if the predictor p_i, i <.05: would it be considered significantly
associated with the outcome variable?
Answer: Yes
●● if beta_i > 0 then what kind of association is there between the
predictor variable and the outcome variable?
Answer: postive
●● Assumptions of MLR
Answer: - that there is a linear relationship between the predictors and
the outcome variable
- the cases are independent of each other
- the noise follows a normal distribution
,-the variance does not depend on the values of the predictors
●● Overfitting
Answer: -the aim of partitioning data is to avoid this
-occurs when we overestimate the model performance based on the data
to the training set
-partition the data into training and validation sets
●● Two ways of normalizing the data are
Answer: 1- subtracting the mean and dividing by the standard deviation
2- rescale the variation to uniform range to 0to1
●● In a dataset, rows and columns correspond to ________ and
_________ respectively.
Answer: rows = y axis of data points/observations
columns = x axis of variables/catrgories
●● A numerical variable can be defined as ___________.
Answer: where the measurement or number has a numerical meaning
●● We need to dummy code
Answer: For nominal data! Set list of variable options where the final
option would be implied
, EX: gender
●● Two types of categorical variables are ____________ and
Answer: nominal - not ordered
ordinal - ordered and ranking
●● Scatter plots represent
Answer: how much one variable is affected by another. The relationship
between two variables is called their correlation
●● We use _________ to visualize the entire distribution of a variable
Answer: boxplots and histograms
●● What is linear regression?
Answer: Linear regression is a statistical technique where the score of a
variable Y is predicted from the score of a second variable X. X is
referred to as the predictor variable and Y as the criterion variable.
●● What are the differences between predictive modeling and
explanatory modeling?
Answer: explanatory is evaluating how well the predictive model
explains an outcome
QUESTIONS AND ANSWERS FULL
SOLUTION VIEW AHEAD
●● MLR:
Answer: -Linear functional form
-Flexible; can be applied for predictive and explanatory modeling
-Caution: we need to avoid collinearity
●● if the predictor p_i, i <.05: would it be considered significantly
associated with the outcome variable?
Answer: Yes
●● if beta_i > 0 then what kind of association is there between the
predictor variable and the outcome variable?
Answer: postive
●● Assumptions of MLR
Answer: - that there is a linear relationship between the predictors and
the outcome variable
- the cases are independent of each other
- the noise follows a normal distribution
,-the variance does not depend on the values of the predictors
●● Overfitting
Answer: -the aim of partitioning data is to avoid this
-occurs when we overestimate the model performance based on the data
to the training set
-partition the data into training and validation sets
●● Two ways of normalizing the data are
Answer: 1- subtracting the mean and dividing by the standard deviation
2- rescale the variation to uniform range to 0to1
●● In a dataset, rows and columns correspond to ________ and
_________ respectively.
Answer: rows = y axis of data points/observations
columns = x axis of variables/catrgories
●● A numerical variable can be defined as ___________.
Answer: where the measurement or number has a numerical meaning
●● We need to dummy code
Answer: For nominal data! Set list of variable options where the final
option would be implied
, EX: gender
●● Two types of categorical variables are ____________ and
Answer: nominal - not ordered
ordinal - ordered and ranking
●● Scatter plots represent
Answer: how much one variable is affected by another. The relationship
between two variables is called their correlation
●● We use _________ to visualize the entire distribution of a variable
Answer: boxplots and histograms
●● What is linear regression?
Answer: Linear regression is a statistical technique where the score of a
variable Y is predicted from the score of a second variable X. X is
referred to as the predictor variable and Y as the criterion variable.
●● What are the differences between predictive modeling and
explanatory modeling?
Answer: explanatory is evaluating how well the predictive model
explains an outcome
