Question 4: After constructing a linear regression of the data, answer the following questions. Include a screenshot or image upload showing the trendline equation and R value. a. What is the equation of the trendline? The equation of the trendline is Y = 11.59 -0.345*X b. What is the coefficient of determination (R)? The coefficient of determination (R) is 0.2627 or 26.27% c. Write a contextualized summary about the slope, y-intercept, and coefficient of determination. Include whether the data makes sense in the real-world context. Name: Section Number: Slope (-0.3445): The negative slope suggests that as age increases, the answer to my numerical question tends to decrease. In other words, older participants tend to provide lower answers to the numerical question compared to younger participants, according to my regression model and data. Y-intercept (11.586): The y-intercept represents the predicted value of the answer to the numerical question when age is 0. However, since age cannot be 0 in real-world scenarios, the interpretation of the y- intercept may not be meaningful in my given context. Coefficient of determination (R2 = 0.2627): The R2 value indicates that age explains approximately 26.27% of the variability in the answer to my numerical question. This suggests that age may not be a strong answer to my numerical question, as other factors not included in the regression model also influence the responses. Additionally, the relatively low R2 value indicates that age does not fully explain the variability in the answer to the numerical question. Real-World Context: While the model provides good insights, it's important to consider other factors (such as interests, lifestyle, and technology adoption) that influence social media behavior. My low R implies that age alone does not fully account for the variation in social media usage. Real-world behavior is likely influenced by a combination of factors beyond age. Concept of linear regression is presented but with major Concept of linear regression does not appear to be understood. Missing from project 3/4 Linear Regression Explanation and Analysis (Question 4) Concepts of linear regression seems to be understood. The explanation clearly connects the meaning of slope and y- intercept to your data, and accurately discusses whether it makes sense in this context. Concept of linear regression seems to be understood with slight misconception(s). Slope and y- intercept are explained clearly, and an attempt is made to match this description to your data. misconceptions. The slope and y- intercept values are not connected to data from your project. Criterion Feedback You need to be more specific in regard to what your slope means. Again, need to be more specific in regard to the y-intercept. Linear Regression Calculations: - Equation of trend-line - Coefficient of Calculations are correct and clearly shown. A screenshot or upload of the trendline with the equation and R Calculations are mostly correct with slight calculation errors or are not clearly shown. Calculations have major calculation errors but are on the right track. Calculations are developing and do not demonstrate understanding of the concept. determination value is included. (Question 4) Criterion Feedback I do not see anything supporting your calculations. Missing from project 0/4 Age Hours spent per day on social media 2 2 25 23 21 24 19 23 20 21 20 19 22222222222222222222 2.5 4 3 5 5 26 1 4.5 6 27 1.5 4 4 6 5.5 7 20 24 20 2.5 5 5.5 4.5 Number of hours spent on social media Hours spent per day vs. age 0 10 15 20 25 Age