A study in Computers and Electronics in Agriculture(Vol. 174, 2020) provides a method for obtaining uniform seed varieties from crop production. A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification.

Images of 13,611 grains of the seven beans were taken with a high-resolution camera for the classification model. The images obtained using the computer vision system were subjected to segmentation and feature extraction stages, and a total of 16 features, 12 dimensions, and 4 shape forms were obtained from the grains.

Here, you are going to analyze the relationship between the major axis length (y) (in pixels), and the minor axis length (x) (in pixels) of the beans, in which yis the distance between the ends of the longest line that can be drawn from a bean and xis the longest line that can be drawn from the bean while stationed perpendicular to the main axis. The data are saved in a file named "BeanDataset.csv" accompanied by this project on the Blackboard.

You have to conduct a complete simple linear regression analysis of the data. Summarize your findings in a professional report.

Below is the data I found regarding the data given

* mean = 202.2707 x_variance = 2022.3091 x_std_dev = 44.9701 y mean = 320. 1419 y_variance = 7343.4935 y_std_dev = 85. 6942 SS xx = 27523627.0007 SS_yy = 99944946.6334 SS_xy = 43325182.6605 Model: y = 1. 7458 + 1.5741x SSE = 31746396. 6197 g *2 - 2332. 7501 48.2986 s_betal = 0.0092 t c = 170.9832 r = 0. 8261 r*2 = 0.6824 >>>END >> END