Advanced Regression Assessment Test

2.

Can the skewness of variables be calculated just using the mean and median?

Yes
No
Depends
None

Correct Answer

A. No

Explanation

Skewness is a measure of the asymmetry of a probability distribution. It provides information about the shape of the distribution. While the mean and median can provide some indication of the central tendency of the data, they do not capture the full picture of the distribution's shape. Skewness takes into account the tails of the distribution and the relative frequencies of extreme values. Therefore, it is not possible to calculate the skewness of variables solely using the mean and median.

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3.

Correlated variables can have zero correlation coeffficient.

True
False
Depends on the analysis
Depends on some other factors

Correct Answer

A. True

Explanation

Correlated variables can have zero correlation coefficient when the relationship between them is not linear. This means that even though there may be a relationship between the variables, it cannot be accurately represented by a straight line. In such cases, the correlation coefficient will be close to zero, indicating no linear relationship between the variables.

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4.

A correlation between a man's health and age is found to be -1.09. Which of these will be reported to the doctor?

The age is a good predictor of health
The age is a poor predictor of health
The man is healthy
None of the above

Correct Answer

A. None of the above

Explanation

The correlation coefficient between a man's health and age is -1.09, indicating a strong negative correlation. This means that as the man's age increases, his health decreases. Therefore, it cannot be concluded that age is a good predictor of health or that the man is healthy. Hence, the correct answer is "None of the above."

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5.

Which of the following is used for predicting continuous dependent variable?

Mean
Gaussian distribution
Linear regression
Logistic regression

Correct Answer

A. Linear regression

Explanation

Linear regression is used for predicting a continuous dependent variable. It is a statistical model that examines the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the dependent variable and the independent variables and uses this relationship to make predictions. In contrast, mean is a measure of central tendency and is not used for predicting variables. Gaussian distribution is a probability distribution and logistic regression is used for predicting categorical dependent variables.

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6.

To test linear relationship of y (dependent) and x (independent) continuous variables, which of the following plot is best suited?

Bar chart
Pictogram
Scatter plot
Histogram

Correct Answer

A. Scatter plot

Explanation

A scatter plot is best suited to test the linear relationship between two continuous variables, y and x. It displays the data points as individual dots on a graph, with the x-axis representing the independent variable and the y-axis representing the dependent variable. By examining the pattern of the dots on the scatter plot, we can determine if there is a linear relationship between the variables. A bar chart is used to compare categorical variables, a pictogram is used to represent data with pictures or symbols, and a histogram is used to display the distribution of a single variable.

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7.

Which of the following describes Heteroskedasticity?

Linear regression with zero error terms
Linear regression with constant error term
Linear regression with no error terms
Linear regression with varying error terms

Correct Answer

A. Linear regression with varying error terms

Explanation

Heteroskedasticity refers to the situation in linear regression where the variability of the error terms is not constant across all levels of the independent variables. In other words, the spread of the residuals or errors is not the same for all values of the predictor variables. This violates one of the assumptions of linear regression, which assumes that the error terms have constant variance. Therefore, the correct answer is "Linear regression with varying error terms."

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8.

Which of these is true of a linear regression model that perfectly fits the training data?

You will always have test zero
You will not have a test zero
Maybe there will be a test zero
None of the above

Correct Answer

A. None of the above

Explanation

A linear regression model that perfectly fits the training data means that it has achieved a perfect fit with no errors or residuals. In this case, it is not necessary that there will always be a zero test error. The model may or may not have a zero test error, depending on the quality and representativeness of the training data. Therefore, the correct answer is "None of the above" as none of the options accurately describe the outcome of a linear regression model that perfectly fits the training data.

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9.

Predicting with trees evaluate ____ within each group of data.

Homogeneity
Equality
Heterogeneity
Inequality

Correct Answer

A. Homogeneity

Explanation

Predicting with trees evaluates homogeneity within each group of data. Homogeneity refers to the similarity or consistency of the data within a group. When predicting with trees, the algorithm splits the data into different groups based on certain conditions, and then evaluates the homogeneity of each group to make predictions. The goal is to create homogeneous groups where the data within each group is similar, as this allows for more accurate predictions.

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10.

Which of the following is a statistical boosting based on additive logistic regression?

Ada
Mboost
Gamboosting
Gbm

Correct Answer

A. Gamboosting

Explanation

Gamboosting is a statistical boosting algorithm based on additive logistic regression. Boosting is a machine learning technique that combines multiple weak learners to create a strong learner. Gamboosting specifically uses generalized additive models (GAMs) as weak learners and combines them through boosting to improve the overall predictive accuracy. GAMs are a flexible and powerful class of models that can capture complex relationships between predictors and the response variable. Therefore, Gamboosting is the correct answer as it is a boosting algorithm based on additive logistic regression using GAMs.

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Advanced Regression Assessment Test

How many coefficients do you need to estimate in a simple linear regression model (one independent variable)?

Quiz Preview

Can the skewness of variables be calculated just using the mean and median?

Correlated variables can have zero correlation coeffficient.

A correlation between a man's health and age is found to be -1.09. Which of these will be reported to the doctor?

Which of the following is used for predicting continuous dependent variable?

To test linear relationship of y (dependent) and x (independent) continuous variables, which of the following plot is best suited?

Which of the following describes Heteroskedasticity?

Which of these is true of a linear regression model that perfectly fits the training data?

Predicting with trees evaluate ____ within each group of data.

Which of the following is a statistical boosting based on additive logistic regression?