AMAZON	EBAY
Date	Close	Close
2023-12-05	146.88	41.48
2023-12-06	144.52	41.47
2023-12-07	146.88	41.48
2023-12-08	147.42	41.29
2023-12-11	145.89	41.76
2023-12-12	147.48	41.11
2023-12-13	148.84	41.96
2023-12-14	147.42	42.65
2023-12-15	149.97	41.75
2023-12-18	154.07	42.52
2023-12-19	153.79	43.67
2023-12-20	152.12	42.98
2023-12-21	153.84	43.73
2023-12-22	153.42	43.82
2023-12-26	153.41	43.48
2023-12-27	153.34	43.38
2023-12-28	153.38	43.47
2023-12-29	151.94	43.62
2024-01-02	149.93	43.87
2024-01-03	148.47	43.55
2024-01-04	144.57	42.53
2024-01-05	145.24	42.79
2024-01-08	149.1	42.84
2024-01-09	151.37	42.14
2024-01-10	153.73	42.56
2024-01-11	155.18	41.87
2024-01-12	154.62	41.21
2024-01-16	153.16	40.7
2024-01-17	151.71	40.67
2024-01-18	153.5	40.79
2024-01-19	155.34	41.13
2024-01-22	154.78	41.06
2024-01-23	156.02	41.41
2024-01-24	156.87	41.61
2024-01-25	157.75	42.16
2024-01-26	159.12	42.69
2024-01-29	161.26	42.62
2024-01-30	159	41.95
2024-01-31	155.2	41.07
2024-02-01	159.28	41.7
2024-02-02	171.81	41.94
2024-02-05	170.31	41.33
2024-02-06	169.15	42.66
2024-02-07	170.53	42.34
2024-02-08	169.84	42.02
2024-02-09	174.45	42.43
2024-02-12	172.34	43.49
2024-02-13	168.64	41.13
2024-02-14	170.98	42.18
2024-02-15	169.8	42.62
2024-02-16	169.51	43.45
2024-02-20	167.08	43.59
2024-02-21	168.59	43.8
2024-02-22	174.58	44.28
2024-02-23	174.99	44.01
2024-02-26	174.73	43.88
2024-02-27	173.54	44.39
2024-02-28	173.16	47.89

Solve the above by applying a linear regression model; determine the strength, direction, and possible correlation between variables. Write a prediction to make a managerial decision.

PART A

Step 1. Identify two pair of variables that have a possible relationship.

Step 2. Using the Excel spreadsheet, determine the ONLY the correlation coefficient (Pearson Correlation or R) for the first pairof variables that you think they will have a relationship. Remember "correlation does not imply causation."Determine strength and direction. (Do not forget to check the significance F)

Step 3. Determine correlation for the second pair of variables (R and its interpretation).

Step 3. For this second pair of variables, you must test for causation as well (simple linear regression analysis coefficient of determination or R²).

	First Pair	Second Pair
Correlation	Check significance F Determine R Determine strength and direction	Check significance F Determine R Determine strength and direction
Causation		Check significance F Construct a scatterplot Add the trendline Get the linear equation and R2

Step 5. Using the linear equation perform a prediction or forecast.

Step 6. Copy the tables, graphs, and equations in a Word file.

Step 7. Write a report (story) using the tables, graphs and equations and describe the results and conclusions:

7.1 Why did you choose those variables? What does the correlation coefficient is telling you about the relationship between both variables? Is it statistically significant? What is the strength and direction of the relationship?

7.2 After computing the linear regression equation for the second pair of variables, explain the relationship between your dependent and independent variable. Use the R²to explain.

7.3 Is your model statistically significant?

7.4 The forecast is what you desire for your company? Write the conclusion about the reliability of your forecasting and possible future decisions.

PART B

Listed below is the selling price for a share of PepsiCo, Inc., at the close of the year.

Year	Price
1990	12.9135
1991	16.825
1992	20.6125
1993	20.3024
1994	18.316
1995	27.7538
1996	29.0581
1997	36.0155
1998	40.6111
1999	35.023
2000	49.5625
2001	48.68
2002	42.22

• Use Excel to plot the data.
• Use Excel to determine the least square trendline equation and provide a trend line.
• Estimate the selling price in 2006.
• Estimate the selling price in 2023.

Add the plot, equation, and results to the main word file.