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1. ASH BUS 308 Week 4 Quiz (3 Set) NEW
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Question 1. The t Stat value is used to determine the statistical
significance of each of the variables listed in a regression
analysis.
Question 2. A correlation of .90 and above is generally
considered too strong to be of any practical significance.
Question 3. A p-value of 9.22E-36 equals
0.00000000000000000000000000000000000922 and is less
than .05
Question 4. If two variables are known to be correlated, it is
possible to predict the value of y (dependent variable) from an
x (independent) variable.
Question 5. When determining statistical significance of
correlations, (as a rule of thumb), variable pairs with
coefficients greater than (>) 70% are generally not very
valuable for prediction purposes.
Question 6. Which statement does not belong?
Question 7. Pearson Correlation Coefficient is a mathematical
value that shows the strength of the linear (straight line)
relationship between two variables.
Question 8. A regression analysis uses two distinct types of
data. The first are variables that are at least nominal level.
Question 9. The ANOVA table provides the Significance of F to
use to see if we reject or fail to reject the null hypothesis of no
significance. The Significance of F is also known as the P-value.
Question 10. When performing a regression analysis using the

2. Regression option in Data Analysis, the input for the Y range is
the independent variable (can generally control) and the input
X range is for the dependent variables.
BUS 308 Week 4 Quiz Set 2
Question 1. When determining statistical significance of
correlations, (as a rule of thumb), variable pairs with
coefficients greater than (>) 70% are generally not very
valuable for prediction purposes.
Question 2. A p-value of 9.22E-36 equals
0.00000000000000000000000000000000000922 and is less
than .05
Question 3. Pearson Correlation Coefficient is a mathematical
value that shows the strength of the linear (straight line)
relationship between two variables.
Question 4. A Pearson correlation of +1.00 is considered a
“perfect positive correlation”. This means….
Question 5. Spearman’s rank order correlation (rho) can be
performed on ordinal or any ranked data.
Question 6. The t Stat value is used to determine the statistical
significance of each of the variables listed in a regression
analysis.
Question 7. Pearson’s Correlation requires at least interval
level data.
Question 8. If two variables are known to be correlated, it is
possible to predict the value of y (dependent variable) from an
x (independent) variable.
Question 9. A correlation of .90 and above is generally
considered too strong to be of any practical significance.
Question 10. When looking at a regression statistics table,
Multiple R displays the percent of variation in common
between the dependent and all of the independent variables.
BUS 308 Week 4 Quiz Set 3

3. Question 1. Pearson’s Correlation requires at least interval
level data.
Question 2. A p-value of 9.22E-36 equals
0.00000000000000000000000000000000000922 and is less
than .05
Question 3. When plotting variables on a scatter diagram, the
variables plotted on the Y-axis is the horizontal axis and the X-
axis is the vertical axis.
Question 4. If two variables are known to be correlated, it is
possible to predict the value of y (dependent variable) from an
x (independent) variable.
Question 5. When determining statistical significance of
correlations, (as a rule of thumb), variable pairs with
coefficients greater than (>) 70% are generally not very
valuable for prediction purposes.
Question 6. A correlation of .90 and above is generally
considered too strong to be of any practical significance.
Question 7. A Pearson correlation of +1.00 is considered a
“perfect positive correlation”. This means….
Question 8. When looking at a regression statistics table,
Multiple R displays the percent of variation in common
between the dependent and all of the independent variables.
Question 9. Which statement does not belong?
Question 10. The t Stat value is used to determine the
statistical significance of each of the variables listed in a
regression analysis.