Question 1

The idea that patterns of extreme scores will balance out if sampling continues indefinitely or trends are looked at over the long run is known as:

Accepted Answer

This is the concept of regression to the mean. It states that extreme scores will balance out over time or with larger sample sizes. It is not related to attrition, history of the mean, or correlation.

Question 2

Good physical fitness has been correlated with many medical benefits, particularly in relation to blood pressure.A regression equation predicting an individual's blood pressure based on physical fitness level results in a negatively sloped line of best fit.What does this statement mean?

Accepted Answer

The negatively sloped line of best fit indicates that there is a negative correlation between physical fitness level and blood pressure. This means that as physical fitness level increases, blood pressure tends to decrease, and as physical fitness level decreases, blood pressure tends to increase. Therefore, people who are physically fit are predicted to have low blood pressure, while people who have low fitness levels are predicted to have high blood pressure.

Question 3

According to the text, a good statistician examines the data points before proceeding and questions causality after the statistical analysis.What would that statistician be doing during each of these two phases of a regression analysis?

Accepted Answer

The text states that a good statistician examines the data points before proceeding, which includes checking for linearity and outliers. After the statistical analysis, the statistician should consider confounding variables that might help to understand cause, which involves questioning causality. Therefore, option A is the correct answer. Option B is incorrect because the text does not mention the ABC model. Option C is also incorrect because running an experiment may not always be possible or necessary. Option D is incorrect because creating visual displays of data is not always useful in all situations, and creating theories about causation should be done after examining for confounding variables.

Question 4

Predicting an individual's IQ score from two variables, for example, socioeconomic status and education level, would involve the use of:

Accepted Answer

Predicting an individual's IQ score from two variables, socioeconomic status and education level, requires the use of multiple regression. Bivariate regression is used when only one predictor is involved, simple linear regression is used when only one predictor is involved and it assumes a linear relationship between variables, and nonlinear correlation is used when the variables in question do not have a linear relationship. However, since we are using two predictors (socioeconomic status and education level), and we want to account for their combined effects on IQ score, we need to use multiple regression.

Question 5

The standardized regression coefficient, which is equal to the Pearson correlation coefficient in a simply linear regression, is also called:

Accepted Answer

The standardized regression coefficient is commonly referred to as the beta weight. Choice A is incorrect because alpha typically refers to the level of significance in hypothesis testing. Choice B is incorrect because it is not a commonly used term in regression analysis. Choice D is incorrect because slope refers to the change in the dependent variable associated with a one-unit increase in the independent variable, whereas the standardized regression coefficient is a measure of the strength and direction of the relationship between the variables.

Question 6

Dr.Kim thinks his regression equation is very accurate, but he wonders if perhaps the mean is just as good at predicting scores as the regression equation.He knows that the correlation coefficient (r) for the two variables is quite high, -0.82.Should Dr.Kim use the mean or the regression equation to predict scores?

Accepted Answer

The proportionate reduction in error (r²) compares the variability in the dependent variable explained by the regression equation to the total variability in the dependent variable. A high proportionate reduction in error indicates that the regression equation is better at predicting scores than just using the mean. In this case, the high correlation coefficient suggests that the regression equation is likely to have a high proportionate reduction in error, indicating that it is a more accurate predictor than the mean. Therefore, Dr. Kim should use the regression equation.

Question 7

In the social sciences, there are numerous variables that can be discussed and considered as important phenomena, but they cannot be observed directly.These are called _____ variables.However, _____ variables, which can be observed and measured, are used to assess the intangible variables.

Accepted Answer

The terms "latent" and "manifest" are commonly used in the social sciences to refer to intangible and tangible variables, respectively. Latent variables are those that cannot be directly observed or measured, while manifest variables are those that can be observed and measured. Therefore, option B is the correct choice.

Question 8

Dr.Garoule is trying to determine which of his patients has the highest likelihood of depression.He calculates a linear regression equation with the scores on an anxiety measure, which are positively correlated with scores on a scale measuring depression.Dr.Garoule converts patient D's anxiety score to a z score and predicts the z score for the depression scale to be 0.65.Is patient D's raw score for depression above or below the mean and why?

Accepted Answer

A z score of 0.65 indicates that patient D's depression score is 0.65 standard deviations above the mean. Since z scores of 0 indicate the mean, positive z scores indicate scores above the mean, and negative z scores indicate scores below the mean, patient D's raw score for depression is above the mean. Therefore, the correct answer is D.

Question 9

Assume a positive correlation is found between the number of hours students spend studying for an exam and their grade on the exam.If the regression equation for these data is calculated and the y intercept is 65, what conclusion can be drawn?

Accepted Answer

The y-intercept of the regression line gives us the predicted score when the independent variable (number of hours studied) is zero. Therefore, when students do not study at all, the predicted score on the exam is 65.

Question 10

When drawing a line of best fit, it is "best" to use _____ point(s) of _____ value(s).

Accepted Answer

It is important to use at least two points, one with a high value and one with a low value, when drawing a line of best fit. This helps to accurately represent the trend of the data and minimize errors. Including more points can improve the accuracy of the line, but including too many points can lead to overfitting and a less accurate representation of the overall trend. Therefore, using at least two points with a low and high value is the best choice.

Question 11

Dr.Garoule is trying to determine which of his patients has the highest likelihood of depression.He calculates a linear regression equation with the scores on an anxiety measure, which are positively correlated with scores on a scale measuring depression.Dr.Garoule converts patient D's anxiety score to a z score and predicts the z score for the depression scale to be -0.35.Is patient D's raw score for depression above or below the mean and why?

Accepted Answer

A negative z score indicates that the score is below the mean.

Question 12

Body weight can be predicted based on the amount of calories consumed by an individual due to the positive correlation between the two variables.When looking at the line of best fit for the linear regression, the data points are clustered close together.Predicted shyness based on the number of friendships a person has is also correlated, but the data points are more scattered around the line of best fit, showing a general negative correlation.Which has the higher predictive power and why?

Accepted Answer

The higher predictive power is given to the correlation with lower variance. The scatter in the data points for shyness and number of friendships indicates that there are other factors that can influence a person's shyness, whereas the data points for calories consumed and body weight show a more consistent relationship with fewer external factors, making it a more reliable predictor. Thus, choice B is correct because the variance is lower in the relationship between calories consumed and body weight.

Question 13

In looking at a graph of data, there seems to be a curved pattern, possibly because of the influence of a third variable.Should simple linear regression be used?

Accepted Answer

Simple linear regression is not appropriate for nonlinear data, which seems to be the case in the given scenario. Therefore, option D is the best choice.

Quiz 16: Regression

The idea that patterns of extreme scores will balance out if sampling continues indefinitely or trends are looked at over the long run is known as:

Good physical fitness has been correlated with many medical benefits, particularly in relation to blood pressure.A regression equation predicting an individual's blood pressure based on physical fitness level results in a negatively sloped line of best fit.What does this statement mean?

According to the text, a good statistician examines the data points before proceeding and questions causality after the statistical analysis.What would that statistician be doing during each of these two phases of a regression analysis?

Predicting an individual's IQ score from two variables, for example, socioeconomic status and education level, would involve the use of:

The standardized regression coefficient, which is equal to the Pearson correlation coefficient in a simply linear regression, is also called:

In the social sciences, there are numerous variables that can be discussed and considered as important phenomena, but they cannot be observed directly.These are called _ variables.However, _ variables, which can be observed and measured, are used to assess the intangible variables.

Assume a positive correlation is found between the number of hours students spend studying for an exam and their grade on the exam.If the regression equation for these data is calculated and the y intercept is 65, what conclusion can be drawn?

When drawing a line of best fit, it is "best" to use _ point(s) of _ value(s) .

In looking at a graph of data, there seems to be a curved pattern, possibly because of the influence of a third variable.Should simple linear regression be used?

Quiz 16: Regression

The idea that patterns of extreme scores will balance out if sampling continues indefinitely or trends are looked at over the long run is known as:

Good physical fitness has been correlated with many medical benefits, particularly in relation to blood pressure.A regression equation predicting an individual's blood pressure based on physical fitness level results in a negatively sloped line of best fit.What does this statement mean?

According to the text, a good statistician examines the data points before proceeding and questions causality after the statistical analysis.What would that statistician be doing during each of these two phases of a regression analysis?

Predicting an individual's IQ score from two variables, for example, socioeconomic status and education level, would involve the use of:

The standardized regression coefficient, which is equal to the Pearson correlation coefficient in a simply linear regression, is also called:

In the social sciences, there are numerous variables that can be discussed and considered as important phenomena, but they cannot be observed directly.These are called _____ variables.However, _____ variables, which can be observed and measured, are used to assess the intangible variables.

Assume a positive correlation is found between the number of hours students spend studying for an exam and their grade on the exam.If the regression equation for these data is calculated and the y intercept is 65, what conclusion can be drawn?

When drawing a line of best fit, it is "best" to use _____ point(s) of _____ value(s) .

In looking at a graph of data, there seems to be a curved pattern, possibly because of the influence of a third variable.Should simple linear regression be used?

In the social sciences, there are numerous variables that can be discussed and considered as important phenomena, but they cannot be observed directly.These are called _ variables.However, _ variables, which can be observed and measured, are used to assess the intangible variables.

When drawing a line of best fit, it is "best" to use _ point(s) of _ value(s) .