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BUS 308 N Week 4 Quiz Set 2.pdf [ Preview Here ]
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BUS 308 Week 4 Quiz

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 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

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.

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