Data were collected that included information on the weight of the trash (in pounds) on the street one week and the number of people who live in the house. The figure shows a scatterplot with the regression line. Complete parts (a) through (d) below. W Click the icon to view the scatterplot and the regression line a. Is the trend positive or negative? What does that mean? The trend is positive. As the number of people per household increases, the weight of trash tends to increase. b. Now calculate the correlation between the weight of trash and the number of people (Use R-Sq from the figure and take the square root of it.) (Round to two decimal places as needed.) Scatterplot and Regression Line Predicted Trash = 9.400 + 8.66 People 100 80- R-sq= 46.3% 60- 40- 20- People per Household Print Done

Data were collected that included information on the weight of the trash (in pounds) on the street one week and the number of people who live in the house. The figure shows a scatterplot with the regression line. Complete parts (a) through (d) below. W Click the icon to view the scatterplot and the regression line a. Is the trend positive or negative? What does that mean? The trend is positive. As the number of people per household increases, the weight of trash tends to increase. b. Now calculate the correlation between the weight of trash and the number of people (Use R-Sq from the figure and take the square root of it.) (Round to two decimal places as needed.) Scatterplot and Regression Line Predicted Trash = 9.400 + 8.66 People 100 80- R-sq= 46.3% 60- 40- 20- People per Household Print Done

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

The output of a another regression analysis on the effect of radio advertising spending, television advertising spending and online advertising spending on sales is given below. For this study they explored 3 different types of advertising spending (radio, television and online). They modelled it as y = ao + a1¤1+a2x2 + a3X3. The table below gives the summary output of this regression analysis. Regression Analysis: Sales versus Spend Analysis of Variance Source DF s MS F-Value P-Value Regression d freg 23.897.37 MSR F-val p-val Error dferr 552.44 MSE Total 36 Find the F-value in the above table. Give your answer to 3 decimal places.
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)
The data show the number of viewers for television stars with certain salaries. Find the regression equation, letting salary be the independent (x) variable. Find the best predicted number of viewers for a television star with a salary of $7 million. Is the result close to the actual number of viewers, 5.2 million? Use a significance level of 0.05. Salary (millions of $) 108 14 5 8 5.1 4.3 7.8 2.5 Viewers (millions) Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=+x (Round to three decimal places as needed.) X 1 1 5 8 5.7 8.2 10.9 4.4
Please help me answer the following, thanks. ASAP
Help please!
Find the residuals for the data. Graph a separate residual plot. Use Desmos and upload a screen shot below. Show Your Work
Using the data, run a regression where you control for “Promotion,” and test the effect of “Wins” on “Attendance”. Does “Promotion” have a significant effect on “Attendance”? Wins Promotion Attendance 4 29500 36300 6 55700 40100 6 71300 41200 8 87000 53000 6 75000 44000 7 72000 45600 5 55300 39000 7 81600 47500
Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported as y=85.73x+27.28 and the r=0.518.What proportion of the variation in y can be explained by the variation in the values of x?r² = __?__%
Please help me. Thankyou.