a) Draw a best-fit line by hand or use the linear-regression feature of GraphicalAnalysis, and determine the slope. The slope can be used to determine the acceleration of the ball on an incline of any angle.b) Repeat the analysis for the low friction dynamics cart. DATA TABLENumber ofbooks123Number ofbooks123Height ofbooks, h(m)0.760-1150.154Height ofbooks, h(m)0.0760.1150.154Length ofincline, x(m)0.80.80.8Length ofincline, x(m)0.80.8Data using ballsin(6)trial 1(m/s²)sin(8)7.64IS. IS15.86Data using carttrial 1(m/s²)2.2263.5184.17Determining g on an InclineAccelerationtrial 2(m/s²)trial 3(m/s²)7.64 7.5516.5813.0919.62Accelerationtrial 2(m/s²)21.14trial 3(m/s²)2.328 2.2993.8153.6404.084.08Averageacceleration(m/s²)7.6114.9418.87Averageacceleration(m/s²)2-2843.6584.11 average acceleration141210HENOX: dala using ball•: data using corttootAOdata using baltdata using cartsine#

How to find :Now use this formula to find .

DATA TABLE Number of books 1 2 3 1 2 3 Height of books, h (m) 0.76 0-115 0.154 Number of Height of books books, h (m) 0.076 0.115 0.154 Length of incline, x (m) 0.8 0.8 0.8 Length of incline, x (m) 0.8 0.8 0.8 Data using ball sin(8) trial 1 (m/s²) sin(8) 7.64 15. 15 15.86 Data using cart trial 1 (m/s²) 2.226 3.518 4.17 Determining g on an Incline Acceleration trial 2 (m/s²) 7.64 7.55 16.58 13.09 19.62 21.14 Acceleration trial 2 (m/s²) trial 3 (m/s²) 2.328 3.815 4.08 trial 3 (m/s²) 2.299 3.640 4.08 Average acceleration (m/s²) 7.61 14.94 18.87 Average acceleration (m/s²) 2.284 3.658 4.11

DATA TABLE Number of books 1 2 3 1 2 3 Height of books, h (m) 0.76 0-115 0.154 Number of Height of books books, h (m) 0.076 0.115 0.154 Length of incline, x (m) 0.8 0.8 0.8 Length of incline, x (m) 0.8 0.8 0.8 Data using ball sin(8) trial 1 (m/s²) sin(8) 7.64 15. 15 15.86 Data using cart trial 1 (m/s²) 2.226 3.518 4.17 Determining g on an Incline Acceleration trial 2 (m/s²) 7.64 7.55 16.58 13.09 19.62 21.14 Acceleration trial 2 (m/s²) trial 3 (m/s²) 2.328 3.815 4.08 trial 3 (m/s²) 2.299 3.640 4.08 Average acceleration (m/s²) 7.61 14.94 18.87 Average acceleration (m/s²) 2.284 3.658 4.11

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

See similar textbooks

Similar questions

An object weighting w pounds on the surface of the earth, will weight wR² W(x) = x2 R. at distance z from the center of the earth, where R = 3,960 miles is the radius of the earth. Use linear approximation to estimate the weight lost by a 160 lb passenger flying in an airplane at an altitude h of 6 miles.
Write the resulting regression equation.
1) Check Image 2) Create a scatterplot of the data 3) Identify a characteristic of the data that is ignored by the regression line. There is no trend in the data. The data has a pattern that is not a straight line. There is an influential point that strongly affects the graph of the regression line. There is no characteristic of the data that is ignored by the regression line.
a) draw a scatterplot that is suitable for predicting test scores based on study time.please include labels for the x and y axis b) determine the shape of the best-fit model for the data (linear , quadratic, exponential, logarithmic sinusoidal). Why? c) find the best-fit regression model for the following data. d) what does the residual plot look like? e) using the model found in predict the score of a who studies for show your for f)what does a residual of 2 mean in context study time and test be?
The volume (in cubic feet) of a black cherry tree can be modeled by the equation y = - 50.7 + 0.3x, + 5.2x2, where x, is the tree's height (in feet) and x2 is the tree's diameter (in inches). Use the multiple regression equation to predict the y-values for the values of the independent variables. X1 = 70, x, = 8.9 The predicted volume is cubic feet. (Round to one decimal place as needed.)
The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…
I need help to interpret the slope of the scatter plot and to do the linear regression equation
Þata on the relationship between plant age and dry matter in the stalk are given in the Table. X (Age) 4 6 8 10 12 14 16 18 Y (Dry matter, %) 24 28 35 48 44 49 51 | 51 i) Express the relationship between the exhausted age and dry matter level with the regression equation (Ý = aX + b). ii) Using the obtained equation, estimate what the dry matter% of plants with ii) a age of 22 weeks will be. iii) By drawing the skater diagram, find the correlation coefficient and interpret the found values. iii)
Please answer this ASAP
Solve b
How do I go about these two questions followin this asnwer above: B) Ater removing the point with coordinates (2,9), use the pairs of values for the remaining 9 points and find the equation of the regression line. c. Compare the results from parts (a) and (b).
The accompanying data are the length (in centimeters) and girths (in centimeters) of 12 harbor seals. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a)x= 140 cm (b)x= 172 cm (c) x = 164 cm (d) x = 158 cm Click the icon to view the table of lengths and girths. The equation of the regression line is y=x+7 (Round to two decimal places as needed.). 4