This assignment covers the Improvement phase of a Lean Six Sigma project. Story In the east process, you believe that excessive variation in CBD content occurs during the cannabis oil extraction process. Several workers operate several different machines that convert cannabis plants into refined CBD oil. Those differences could affect the CBD levels. To learn more about this process, you conducted 2 experiments.

In the first experiment, you processed 56 batches of oil in one machine for various lengths of time and at different ambient temperatures, and then measured the CBD content in grams.

In the second experiment, you processed 56 batches of oil using various combinations of machines and workers, and then measured the CBD content in grams. Your goal is to find the "best" combination of time, temperature, and machine. The workers will then use this information to create their standard work instructions. Your main priority is to maximize CBD content. Your secondary priority is to make the process more robust so that a small mistake in setting the time or temperature would only slightly decrease the yield.

Questions

1. Use linear regression to analyze the relationship that processing time and ambient temperature might have with CBD content in the first 56 batches of oil. In Excel, you can use the "Data - Data Analysis - Regression" feature; select the "Line Fit Plot", "Residual Plot", and "Normal Probability Plot" options. In SPSS, you can use the "Regression - Linear" feature.

Qa) Include the regression results table in your assignment. You will need to adjust the table's format to make it more readable.

Qb) For each explanatory variable, briefly interpret the coefficient B and its significance p. Also interpret the R 2 of the overall model. What does each of these values indicate?

Q c) Include 5 plots in your assignment as follows. You will need to adjust their formatting.

1. The line fit plot relative to time. Ensure that there are only 2 sets of data: the actual measurements should be represented by dots (with no line), and the predicted measurements should be represented by a line (with no dots).

2. The line fit plot relative to temperature. Ensure that there are only 2 sets of data: the actual measurements should be represented by dots (with no line), and the predicted measurements should be represented by a line (with no dots). (This will be messy.)

3. The residual plot relative to time. Do not include a line.

4. The residual plot relative to temperature. Do not include a line.

5. The normal probability plot. A line is optional.

Qd) Briefly interpret these plots. Is linear regression reasonable for this data? (Report any problems you see, but do not fix them.)

The Excel table is below. I want the answers to the above questions. All the plots and the regression table as well.

Yield versus Time & Temperature
Minutes	Degrees C	grams
Time	Temperature	CBD
30	20.5	4.21
35	21.7	10.20
40	23.1	13.07
45	19.5	26.23
50	20.7	35.39
55	22.7	46.29
60	21.0	54.03
65	21.8	65.46
70	22.3	77.64
75	19.5	79.51
80	21.9	79.72
85	22.7	80.93
90	21.7	83.13
95	23.0	83.56
30	22.1	9.23
35	20.1	6.06
40	21.3	17.58
45	22.2	28.25
50	21.9	37.53
55	21.3	49.34
60	22.5	60.31
65	19.7	65.60
70	21.8	72.79
75	22.9	75.65
80	21.7	75.05
85	22.5	75.71
90	23.3	79.76
95	20.6	86.37
30	20.9	6.79
35	22.8	2.13
40	20.2	16.56
45	21.4	25.65
50	23.5	36.26
55	19.8	49.49
60	21.3	50.64
65	21.4	70.34
70	20.3	74.15
75	21.1	76.62
80	23.8	80.06
85	20.1	86.07
90	21.3	85.68
95	21.7	77.98
30	21.2	4.63
35	22.4	9.16
40	23.3	15.41
45	19.2	23.17
50	21.5	37.26
55	22.3	48.41
60	21.0	54.69
65	22.0	64.79
70	23.2	74.65
75	20.8	81.69
80	21.6	80.49
85	21.8	85.49
90	19.2	81.77
95	21.5	78.82