hw #2 exam 2 review 10points

1. For a given set of data, the larger the explained variance,

Question 1 options:

	The larger the total variance
	The larger is the R-squared value
	The greater the slope of the regression line
	The greater the total of the sum of the dependent variable values

Question 2

In logistic regression, the Y intercept

Question 2 options:

	Is equal to the square of (y-bar)
	Is equal to 1 (one) over the slope of Y
	Is a value between 0 (zero) and 1 (one)
	Any value (it can't be determined from the question)

Question 3

Temperature, where the differences between any two degrees represents the same change (difference) as the difference between any other two degrees, would be an example of

Question 3 options:

	Ratio scale
	Interval scale
	Partial correlation

Question 4

In a traditional regression analysis, the predictors are

Question 4 options:

	The values obtained after subtracting the Y values and the mean of Y
	The X values
	The beta coefficients
	The expected values of Y given any value of X

Question 5

In multiple regression, beta coefficients

Question 5 options:

	Can be different for each variable
	Can only be positive values or 0 (zero)
	Become larger in value as the number of predictor variables increase

Question 6

Assume a partial correlation is computed (with X, Y and Z). If the correlation between the original X and original Y values remains the same (as compared to the outcome of the partial correlational analysis) then the

Question 6 options:

	Controlled variable (third variable) explains all of the original correlation
	Correlation between the original X and Y values must be 1.0
	Correlation between either X and Z or Y and Z must be 1.0
	Influence of the third variable is 0 (zero)

Question 7

If a regression line improves upon predicting the Y mean value for any X value, then based on the regression line, the

Question 7 options:

	Sum of squares explained increases
	Sum of square total increases
	The slope of the regression line increases
	The beta weights approach 0 (zero)

Question 8

R-squared (r²or R²) describes

Question 8 options:

	Explained variance is about 0 (zero)
	The total sum of squares (sum of squares total)
	The strength of a regression model

Question 9

A regression analysis is run and a best fit line is computed. If all of the actual Y values are located on the regression line,

Question 9 options:

	Explained variance is about 0 (zero)
	Unexplained variance is positive infinity
	The unexplained variance is about negative infinity
	None of the above

Question 10

As compared to a correlation analysis, an advantage of a scientific experiment is

Question 10 options:

	Experiments can be completed in any set of circumstances while correlations are dependent on randomizing participants
	Experiments produce computed beta/coefficient weights while with correlation these values are only estimated
	Causality

Question 11

The Y intercept is

Question 11 options:

	0 (zero)
	1 (one)
	Equal to the sum of squares total
	The point where a regression line crosses the Y axis

Question 12

In a regression analysis, as the predicted outcome (value) of a variable increases in distance from what was actually measured for the variable,

Question 12 options:

	The residual value increases
	The explained variance increases
	The mean of the outcome variable increases
	The modal (the mode) value of the outcome variable approaches 0 (zero)

Question 13

In a regression analysis, the dependent variable

Question 13 options:

	Reflects what the regression model is trying to predict
	Has a beta weight between the values of 0 (zero) and 1 (one)
	Is the confound variable

Question 14

If the sum of squares for the residuals is 0 (zero)

Question 14 options:

	The residuals will each be 1 (one) standard deviation (of the independent variable) from the regression line
	The difference between the predicted values of Y and the actual values is also 0 (zero)
	None of the actual Y values equal any of the predicted values for Y

Question 15

If the regression line is equal to the mean of the Y values (the intercept is the Y mean and the slope is 0 (zero))

Question 15 options:

	The regressor values must each also equal 0 (zero)
	The sum of squares residuals (explained) equals the sum of squares unexplained
	The slope of the regression line is not computable

Question 16

In a partial correlation of three variables (for example, X, Y and Z)

Question 16 options:

	The partial correlation is simply the original r value divided by 3
	All three variables are considered outcome variables
	The computed partial correlation can be any value between -1.0 and 1.0

Question 17

According to the concept "regression to the mean"

Question 17 options:

	The likelihood of an extreme value occurring is 0 (zero)
	If an event produces two outcomes, those outcomes are correlated
	An extreme outcome of any event is likely to be followed by a less extreme outcome
	A perfect regression line is one that is fit to the mean of the Y values

Question 18

In the ordinal scale, the difference between any two points of the scale

Question 18 options:

	Is equal to 0 (zero)
	Is as large as the number of points making up the scale
	Can vary
	Is consistent (can't vary)

Question 19

In the following "r_xy.z", the possible confound is

Question 19 options:

	X
	Y
	Z
	r

Question 20

After a linear regression analysis has been completed and a linear model has been produced,

Question 20 options:

	The slope of the regression line must be equal to the mean of the Y variable
	The intercept of the regression line is always where Y = 0 (zero)
	If, for any X, Y is a negative number, the linear model is invalid
	A dependent variable value can be computed for any value of the independent variable

Question 21

In correlational analyses (as discussed in class), the term "control"

Question 21 options:

	Refers to randomization of participants into different groups
	Is synonymous (means the same) with "causality"
	Refers to factoring out any impact of a confound
	Is synonymous (means the same) with "independent variable"

Question 22

In a linear regression analysis, a goal is to

Question 22 options:

	Separate data points into one of two different outcome conditions
	Find the mean of the Y variable
	Produce a model that predicts the values of the independent variable
	Produce a model that predicts the values of the dependent variable

Question 23

An attempt to predict the probability of correctly placing an observation into one of two different conditions

Question 23 options:

	Is an example of logistic regression
	Involves grouping values defined by nominal scale measurements
	Can only occur if the slope of a best fit regression line is a non-zero value
	Requires partial correlations

Question 24

Regressor values are

Question 24 options:

	The independent variable values
	The dependent variable values
	In any linear models, those values with the smallest residuals
	Any dependent variable values that are greater than the mean of the Y variable

Question 25

The (numeric) value of the Coefficient of Determination

Question 25 options:

	Can never be 0 (zero)
	Can be any value
	Must be between 0 (zero) and 1 (one)
	Will always be higher than the value of r

Question 26

The measure of central tendency for the Ratio scale is

Question 26 options:

	The coefficient of determination
	The mean
	The mode
	0 (zero)

Question 27

Unexplained variance can be directly computed using

Question 27 options:

	1 (one) and R2 (R squared)
	X values and the mean of X
	The predicted Y values and the mean of X
	The Y intercept and the mean of Y ()

Question 28

In measurement scale, the value 0 (zero) can be part of

Question 28 options:

	Ordinal scales
	Interval scales
	All of the scales
	Only the ratio scale

Question 29

(Y-bar) is

Question 29 options:

	Involved with calculating the regressors
	0 (zero) when the slope of a regression line is 0 (zero)
	The symbol for the mean of the Y variable values

Question 30

Which of the following involves a bivariate condition

Question 30 options:

	Pearson's r
	The mean of the Y-axis values
	Values associated with a measurement based on the interval scale
	Regression predictors

Question 31

In correlation, a confound is

Question 31 options:

	Another name for the correlation coefficient
	A computational error (e.g. r = 5)
	A possible explanation for a potential association that extends beyond the variables used in the original correlational analysis
	Any value among the data that is considered an outlier and artificially alters the computed value of the correlational analysis

Question 32

The sum of squares (sum of squares total, sum of squares residual and sum of squares explained) is squared because

Question 32 options:

	R-squared is also a square
	To keep the sum of squares between the values of 0 (zero) and 1 (one)
	Otherwise all of the values involved in the sum of squares would be negative values
	All of the relevant values would cancel out and add up to a total of 0 (zero)

Question 33

A regression line

Question 33 options:

	Represents all of the predicted X values
	Has a slope equal to the explained variance
	Can't be a horizontal line (parallel to the X axis)
	Represents the predicted Y values for any value of X