# Business Analysis Statistics Test #2

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#### 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?

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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?

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A decrease in R-Square

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-1.0415

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

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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:

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An empirical logit trend plot against a predictor variable

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

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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.

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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?

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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?

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

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