Question

Main Post: Consider the dataset that you analyzed in Unit 1. If your dataset did not have two quantitative variables or if you would prefer using a different dataset, visit the dataset link to select a new data set of interest to you. See Example and DB starter video in Unit 8 LiveBinder.

Determine the following information on your selected data set. Be sure to answer all questions using complete sentences.

1.   State the dataset and the two quantitative variables of interest. Do you think there might be a correlation between the two variables (before you analyze the data)? Why or why not.

2.   Create a scatterplot with a simple linear regression. Attach the scatterplot to your post.

3.   Run the regression analysis in Excel Data Analysis. Share the output.

4.   What is the coefficient of determination r²? Summarize the results by stating whether this is a strong or weak relationship. State whether it is a positive or negative relationship. Is this result what you expected?

5.   Use the regression analysis output to determine the linear regression (best fit prediction line) equation.

Average US Gas Price 1991-2015 (in dollars per gallon)
Year	Average US Gas Price	2 Period Moving Average	3 Period Moving Average
1991	1.140	N/A	N/A
1992	1.130	1.135	N/A
1993	1.110	1.120	1.127
1994	1.110	1.110	1.117
1995	1.150	1.130	1.123
1996	1.230	1.190	1.163
1997	1.230	1.230	1.203
1998	1.060	1.145	1.173
1999	1.170	1.115	1.153
2000	1.510	1.340	1.247
2001	1.460	1.485	1.380
2002	1.360	1.410	1.443
2003	1.590	1.475	1.470
2004	1.880	1.735	1.610
2005	2.300	2.090	1.923
2006	2.590	2.445	2.257
2007	2.800	2.695	2.563
2008	3.270	3.035	2.887
2009	2.350	2.810	2.807
2010	2.790	2.570	2.803
2011	3.530	3.160	2.890
2012	3.640	3.585	3.320
2013	3.530	3.585	3.567
2014	3.370	3.450	3.513
2015	2.450	2.910	3.117

Answer 1

1.   State the dataset and the two quantitative variables of interest. Do you think there might be a correlation between the two variables (before you analyze the data)? Why or why not.

The two quantitative variables of interest are 2 Period Moving Average and 3 Period Moving Average. Yes, there is a correlation between the two variables because both are Period Moving Average.

2.   Create a scatterplot with a simple linear regression. Attach the scatterplot to your post.

3 Period Moving Average HHHHHHHHr ++ ++ + +++ ++ ++ +++ + ++ ++ ++ ++ ++ ++ 日 152253354 2 Period Moving Average

3.   Run the regression analysis in Excel Data Analysis. Share the output.

A293 face - A X B C D E F G 293 294 Regression Analysis 295 296 2 0.981 297 10.991 298 Std. Error 0.126 n 23 300 k 1 Dep. Var. 3 Period Moving Average 299 301 302 MS 17.3766 0.0159 - 1096.14 p-value 1.31E-19 303 ANOVA table 304 Source SS 305 Regression 17.3766 306 Residual 0.3329 307 Total 17.7095 308 309 310 Regression output 311 variables coefficients std. errort 312 Intercept 0.0156 313 Moving Average 0.9723 0.0294 (df=21) confidence interval p-value 95% lower 95% upper 33.108 1.31E-19 0.9113 1.0334 314

4.   What is the coefficient of determination r²? Summarize the results by stating whether this is a strong or weak relationship. State whether it is a positive or negative relationship. Is this result what you expected?

The coefficient of determination r² is 0.981. There is a strong positive relationship as expected.

5.   Use the regression analysis output to determine the linear regression (best fit prediction line) equation.

The best-fit prediction line is:

3 Period Moving Average = 0.0156 + 0.9723*2 Period Moving Average

Please give me a thumbs-up if this helps you out. Thank you!