Scatter Plots

Scatter Plots
Student’s Name
Institution Affiliation
Scatter Plots
Health and Nursing

Equation: y = 2613.2x – 5E+06R² = 0.9658
The prediction value = 73663.70862 = 9%
The data has consistency in rising values over the years, leading to a high prediction in the future years. The prediction is of a high likelihood since the previous scatter points are close to the trend line.
Criminal Justice
Equation: y = 24337x – 4E+07R² = 0.3199
The 2018 prediction value = 7240115.238.
There is a reduction in the “fit” of the data, which decreases the accuracy of the predicted values. According to Brown (2001), “The higher the fit, the higher the accuracy,” (p. 191). The estimated value for the years 2018 has a likelihood of being the estimated prediction over the years since the year 2000. However, the amount might be distant from the actual occurrence since the scatter points are far from the trend line across the period. Furthermore, there has been a consistent drop in the values between 2010 and 2018, yet the predicted value ranges at a high point in the scatter plot.
3 Human and Sciences.
The equation: y = 1813.1x – 4E+06R² = 0.8288
The 2018 prediction value is 41448.8683.
Topic 4: Social Sciences

The equation: y = -6.0294x + 12149R² = 0.9677
The 2018 prediction value is -18.55882353, implying that there will be no teens using face to face communication by 2018.
The trend line in the scatter plot suggests a consistent drop in the number of teens who use face to face communication among the high school goers.

In 2018, the scatter plot predicts a value less than zero teens utilizing face to face communication.
Topic 5: Business

The equation y = -442.2x + 899919R² = 0.4604
The 2018 prediction value is 7555.981203. The trend line in the scatter plot suggests a decrease in the number of the tires bought over the years. Therefore, there will be less demand for the tires by 2018.
Topic 6: Education.
The equation is y = -1.4286x + 3393.7R² = 0.8197
The 2018 prediction value is 510.8571429.
Reference
Brown, A. M. (2001). A step-by-step guide to non-linear regression analysis of experimental data using a Microsoft Excel spreadsheet. Computer Methods and Programs in Biomedicine, 65(3), 191-200.