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Question 1 of 10
1. Question
Which type of variables are called risk factors?
Correct
In generalized linear modeling, the variability in one variable is explained by the changes in one or more other variables. The dependent or response variable is being explained by the variables, that is known for explaining, and are thus known as the explanatory variables. These are also known as the risk factors or the drivers of risk.
Incorrect
In generalized linear modeling, the variability in one variable is explained by the changes in one or more other variables. The dependent or response variable is being explained by the variables, that is known for explaining, and are thus known as the explanatory variables. These are also known as the risk factors or the drivers of risk.
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Question 2 of 10
2. Question
How do you best describe statistical modeling?
Correct
Statistical modeling is the art or science of designing, fitting, and interpreting a model. A statistical model helps to find out the answers to the following categories; which variables are predictive of the response, and what is the appropriate scale for their inclusion, etc.
Incorrect
Statistical modeling is the art or science of designing, fitting, and interpreting a model. A statistical model helps to find out the answers to the following categories; which variables are predictive of the response, and what is the appropriate scale for their inclusion, etc.
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Question 3 of 10
3. Question
How is the relationship between two continuous variables explored in the Data exploration process?
Correct
The relationship between two continuous variables is explored with a scatterplot. A scatterplot is sometimes included with a third, category, marking it into a variable done by using color and different symbols to mark the complete picture.
Incorrect
The relationship between two continuous variables is explored with a scatterplot. A scatterplot is sometimes included with a third, category, marking it into a variable done by using color and different symbols to mark the complete picture.
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Question 4 of 10
4. Question
Which matters out of these should be resolved before statistical modeling?
Correct
Problems such as missing values and inconsistent or invalid recording must be resolved before statistical modeling.
Incorrect
Problems such as missing values and inconsistent or invalid recording must be resolved before statistical modeling.
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Question 5 of 10
5. Question
Why do you think the problem arises in the data being inconsistent or invalid?
Correct
Those who collect or enter information may not be aware to record the statistical uses as it is opposed to the accounting uses for which the data will be utilized. There may not be the need for consistent recording according to them, across all records. Designers of the data recording system may not be aware of the myriad of cases that need to be accommodated that is why the problem occurs.
Incorrect
Those who collect or enter information may not be aware to record the statistical uses as it is opposed to the accounting uses for which the data will be utilized. There may not be the need for consistent recording according to them, across all records. Designers of the data recording system may not be aware of the myriad of cases that need to be accommodated that is why the problem occurs.
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Question 6 of 10
6. Question
The Bernoulli distribution has how many type/s of possible outcomes?
Correct
The Bernoulli distribution shows only two possible outcomes, usually coded as 0 or 1 and that’s why Ω = {0, 1}. When the event is y = 1, it is called a success, and if it is y = 0, it is called a failure.
Incorrect
The Bernoulli distribution shows only two possible outcomes, usually coded as 0 or 1 and that’s why Ω = {0, 1}. When the event is y = 1, it is called a success, and if it is y = 0, it is called a failure.
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Question 7 of 10
7. Question
For finding a parametric form of the relationship, when should banding or categorizing the explanatory variable be used?
Correct
When the relationship between the explanatory variable and response is not linear, an alternative approach for finding a parametric form of the relationship that can be used is to band or categorize the explanatory variable.
Incorrect
When the relationship between the explanatory variable and response is not linear, an alternative approach for finding a parametric form of the relationship that can be used is to band or categorize the explanatory variable.
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Question 8 of 10
8. Question
When the person found smoking is assessed as follows: x1 the age at which smoking commenced, x2 the number of years since smoking initiation, and x3 the current age, which means x3 = x1 + x2.
What type of dependence is found in these explanatory variables?Correct
The three variables x1, x2, and x3 explain the same as any two of the variables, which means that the individual effects of each of the three variables cannot be assessed and so the variables are called to be perfectly collinear.
Incorrect
The three variables x1, x2, and x3 explain the same as any two of the variables, which means that the individual effects of each of the three variables cannot be assessed and so the variables are called to be perfectly collinear.
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Question 9 of 10
9. Question
What do many programs for linear modeling do, when they encounter perfect linear dependence in variables?
Correct
They exclude one or more variables to ensure the perfect collinearity disappears that is why alternatively, the software may crash or report nonsensical results.
Incorrect
They exclude one or more variables to ensure the perfect collinearity disappears that is why alternatively, the software may crash or report nonsensical results.
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Question 10 of 10
10. Question
In which situation out of the following, is it acceptable to remove an explanatory variable from a final model?
Correct
When the effect of an explanatory variable is statistically significant but small relative to the effects of other explanatory variables, it should be removed from the final model. The magnitude of the effect of the variable, and whether its statistical significance translates into a practically useful effect or relativity is useful for a variable to be mentioned in the final model.
Incorrect
When the effect of an explanatory variable is statistically significant but small relative to the effects of other explanatory variables, it should be removed from the final model. The magnitude of the effect of the variable, and whether its statistical significance translates into a practically useful effect or relativity is useful for a variable to be mentioned in the final model.