Question 1

Which of the following can be used to check the normality assumption?
A) Histogram
B) Stem-and-lead display
C) Box-and-whisker plot
D) All of the above

Accepted Answer

All of the options listed (Histogram, Stem-and-leaf display, and Box-and-whisker plot) can be used to visually assess the normality of a dataset. Histograms show the distribution of data, stem-and-leaf displays provide a way to see the shape of the data distribution, and box-and-whisker plots can indicate symmetry or skewness in the data, all of which are relevant to assessing normality.

Question 2

If you collected data over time,avoid using the Durbin-Watson test to check the independence assumption.

Accepted Answer

The Durbin-Watson test is used to check for autocorrelation in time series data, which is a violation of the independence assumption. Therefore, it is appropriate to use the Durbin-Watson test to check for independence in data collected over time.

Question 3

Instruction 12.40
The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousands of dollars) for individual stores based on the number of customers who made purchases. A random sample of 12 stores yields the following results:
 $$\begin{array} { | l | c | } 
\hline \text { Customers } & \text { Sales (Thousands of Dollars) } \
\hline 907 & 11.20 \
\hline 926 & 11.05 \
\hline 713 & 8.21 \
\hline 741 & 9.21 \
\hline 780 & 9.42 \
\hline 898 & 10.08 \
\hline 510 & 6.73 \
\hline 529 & 7.02 \
\hline 460 & 6.12 \
\hline 872 & 9.52 \
\hline 650 & 7.53 \
\hline 603 & 7.25 \
\hline
\end{array}$$
-Referring to Instruction 12.40,construct a 95% prediction interval for the weekly sales of a store that has 600 purchasing customers.

Accepted Answer

The answer of Instruction 12.40
The management of a chain electronic...

Question 4

To avoid the pitfalls of regression,you should start with a scatter plot to observe the possible relationship between X and Y.

Accepted Answer

Starting with a scatter plot can help identify any potential patterns or trends in the data, which can then inform the choice of regression model and help avoid common mistakes.

Question 5

Because causation implies correlation,correlation implies causation.

Accepted Answer

Causation and correlation are two different concepts. While a correlation indicates a relationship between two variables, it does not prove that one caused the other. Causation requires additional evidence beyond just observing a correlation. Therefore, correlation does not imply causation, and thus the first statement, "Because causation implies correlation, correlation implies causation" is false.

Question 6

It is suitable to make predictions and forecasts outside the relevant range of the independent variable.

Accepted Answer

It is not suitable to make predictions and forecasts outside the relevant range of the independent variable, as the relationship between the independent and dependent variable may not hold outside of the observed range.

Question 7

To determine whether the linear model is appropriate and to check the equal variance assumption,plot residuals against the independent variable.

Accepted Answer

Plotting residuals against the independent variable is a common method for assessing both the linearity assumption and the equal variance assumption in linear regression. By examining the scatter of residuals around the zero line, we can check whether the relationship between the dependent and independent variables is linear and whether the variance of the residuals is roughly constant across all levels of the independent variable.

Quiz 12: Simple Linear Regression

Which of the following can be used to check the normality assumption?

If you collected data over time,avoid using the Durbin-Watson test to check the independence assumption.

To avoid the pitfalls of regression,you should start with a scatter plot to observe the possible relationship between X and Y.

Because causation implies correlation,correlation implies causation.

It is suitable to make predictions and forecasts outside the relevant range of the independent variable.

To determine whether the linear model is appropriate and to check the equal variance assumption,plot residuals against the independent variable.