Business Analysis

Business Analysis Statistics Test #2

A firm has eight regional branch offices. Customers within each region are coded using the variable name VALUE and are categorized as either "High Value" or "Medium Value." The response variable used in the past year is the total number of purchases made by each client. Assume REGION and VALUE have a substantial interaction. What do you draw from this?

There are more high value clients in regions with higher average buy levels.

The disparity between medium and high value clients' average purchases varies by area.

There are more medium value clients in areas with higher average buy levels.

Some regions have more high value consumers than others.

Correct! Wrong!

The correct answer:
The disparity between medium and high value clients' average purchases varies by area.

An existing multiple linear regression model is modified by removing a non-contributing predictor variable (Pr > |t| =0.658). What will happen as a result?

No change in R-Square

A decrease in Mean Square Error

A decrease in R-Square

An increase in R-Square

Correct! Wrong!

The correct answer:
A decrease in R-Square

The link between a binary response and an ordinal predictor variable, wrist size, is examined in the LOGISTIC method output that follows. The analyst decides on Large (L) as the reference level using reference cell coding. What is a person with a large wrist size's estimated logit? If one is required, simply click the calculator icon to bring one up.

0.5663

0.0819

-3.7727

-1.0415

Correct! Wrong!

The correct answer:
-1.0415

In the LOGISTIC process, which of the following best defines a concordant pair of observations?

In comparison to an observation without the event, an observation with the event has a lower anticipated probability.

There is an equal chance that one observation will be associated with the occurrence as another.

The anticipated probability for an observation with and without the occurrence is the same.

A prediction for an observation that includes the event is higher than for an observation that does not.

Correct! Wrong!

The correct answer:
A prediction for an observation that includes the event is higher than for an observation that does not.

You may check for non-linearity in binary logistic regression by visualizing:

Odds ratio values plotted against a predictor variable in a box.

An empirical logit trend plot against a predictor variable

A plot of projected probability values against a predictor variable using logistic regression.

Binary response scatterplot vs predictor variable

Correct! Wrong!

The correct answer:
An empirical logit trend plot against a predictor variable

What is the LOGISTIC procedure's default approach to handling observations with incomplete data?

Only instances with completely populated variables are employed.

In order to accommodate for the missing values, parameters are estimated.

All the data is used to estimate the parameters.

Missing values are imputed.

Correct! Wrong!

The correct answer:
Only instances with completely populated variables are employed.

By examining the correlation between which function of the input variables, Spearman statistics in the CORR technique are helpful for identifying unnecessary variables.

Rank-ordered values of the variables

Concordant and discordant pairs of ranked observations

Weighted sum of chi-square statistics for 2x2 tables

Logit link (log(p/1-p))

Correct! Wrong!

Explanation:
A ranked variable is an ordinal variable, meaning it may be used to rank all of the data points (1st, 2nd, 3rd, etc.). Although you may not be able to calculate the exact worth of each of your points, you do know which comes first.

A logistic regression model's input variable, Region (A, B, or C), is investigated by an analyst. The analyst finds that when Region = A, the likelihood of purchasing a specific item is 1. What issue does this highlight?

Influential observations

Collinearity

Problems that arise due to missing values

Quasi-complete separation

Correct! Wrong!

Explanation:
When the dependent variable partially or partially completely separates an independent variable or a mixture of numerous independent variables, this is known as quasi-complete separation. In a discrete outcome variable, levels in a category variable or values in a numeric variable are separated by groups.

What disadvantage exists in cleaning up the data (imputation, transformations, etc.) on raw data before partitioning it for accurate assessment as opposed to cleaning up the data after partitioning it?

The advantages of the purification techniques are not applied to the training (and test) data sets.

It violates assumptions of the model.

It requires extra computational effort and time.

It is impossible to compare the efficacy of various washing techniques.

Correct! Wrong!

The correct answer:
It is impossible to compare the efficacy of various washing techniques.

To execute an unbiased evaluation during the model building process, an analyst has access to a large enough volume of data to do a 3-way partitioning of the data into training, validation, and test sets. What goal does the test data set serve?

To offer a fair assessment metric for the finished model.

To reduce total sample size to make computations more efficient.

To build the predictive models.

To compare models and select and fine-tune the final model.

Correct! Wrong!

The correct answer:
to offer a fair assessment metric for the finished model.

Compared to the SCORE statement in the LOGISTIC procedure, think about scoring fresh observations in the SCORE procedure. Which claim is accurate?

In contrast to the SCORE procedure, the LOGISTIC procedure's SCORE statement generates both anticipated probabilities and predicted logits.

Only anticipated logits are returned by the SCORE statement in the LOGISTIC process, while only predicted probabilities are returned by the SCORE procedure.

The output is the same for both the SCORE process and the SCORE statement in the LOGISTIC procedure.

In contrast to the SCORE method, the SCORE statement in the LOGISTIC procedure simply gives anticipated probabilities.

Correct! Wrong!

The correct answer:
In contrast to the SCORE method, the SCORE statement in the LOGISTIC procedure simply gives anticipated probabilities.

Business Analysis Statistics Test #2

An existing multiple linear regression model is modified by removing a non-contributing predictor variable (Pr > |t| =0.658). What will happen as a result?

In the LOGISTIC process, which of the following best defines a concordant pair of observations?

You may check for non-linearity in binary logistic regression by visualizing:

What is the LOGISTIC procedure's default approach to handling observations with incomplete data?

By examining the correlation between which function of the input variables, Spearman statistics in the CORR technique are helpful for identifying unnecessary variables.

A logistic regression model's input variable, Region (A, B, or C), is investigated by an analyst. The analyst finds that when Region = A, the likelihood of purchasing a specific item is 1. What issue does this highlight?

What disadvantage exists in cleaning up the data (imputation, transformations, etc.) on raw data before partitioning it for accurate assessment as opposed to cleaning up the data after partitioning it?

To execute an unbiased evaluation during the model building process, an analyst has access to a large enough volume of data to do a 3-way partitioning of the data into training, validation, and test sets. What goal does the test data set serve?

Compared to the SCORE statement in the LOGISTIC procedure, think about scoring fresh observations in the SCORE procedure. Which claim is accurate?

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