Problem 4. Let the input x be the total seasonal depth of applied water in inches, and let the output ybe the average yield of alfalfa in tons per acre. The data given by the table below was compiled by the UCExperimental Station.I121824303642y5.275.686.25.7.218.208.71Compute the formula for the empirical regression line corresponding to this data. This is a problem inChapter 14 of the text.

From the information, given that

Answered: Problem 4. Let the input a be the total…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Given the following data, which is the equation of the regression line? X 0 3 4 5 8 Y 12 6 3 3 1 A. y=4.88+0.525x B. y=10.529+1.382x C. y=−10.529−1.382 D. y=10.529−1.382x
QUESTION A9 (5 Consider the set of data below for the yield (in kg) of tomatoes with fertilizer and the number of days. Day 65 70 75 Yield (kg) 30 44 53 55 62 80 85 (a) Plot a scatter diagram for the data (No graph paper or excel required). (b) A regression line equation y on x is y = 1.5x + 63.7. Draw this regression line on the scatter diagram drawn in part (a).
Question 2 The following table shows the mean weight in kilograms of members of a group of young children of various ages. Age (x years) 1.6 2.5 3.3 4.4 5.6 Weight ( y kg) 12 15 16 17 20 The relationship between the variables is modeled by the regression line with equation y=ax+by=ax+b
You are interesting in how rats' weights influence their running speed. For a group of 6 rats, you weigh them (in kg) and then time them in a race (in s). The data are given in the following table. Subject A B С OE D E Weight (kg) Time (s) 1.1 0.8 0.9 0.7 1.2 1.1 F Find the regression line that represents the relationship between your predictor variable (Weight) and your criterion variable (Time). SP or SSXY = [Select] SSX or SSXX= [Select] 110 90 70 80 100 SSY or SSYY = [Select] 85 The slope of the line (b) = [Select] The intercept of the line (a) = [Select] Find the standard error of the estimate for the regression above: [Select]
answer 4&5
question d
7
Question 3 A. The information below shows the age and the number of sick days taken for 6 employees at a biscuit factory. Age(x) 18 26 39 48 53 58 Number of sick days(Y) 16 12 9 5 6 2 Table 3. Using the information above: i. Determine the product-moment coefficient (r) ii. Calculate the coefficient of determination and interpret your answer iii. Determine the equation of the regression line iv. Use the equation of the regression line to estimate the number of sick days that would be taken by an employee who is 47.
Indicate the steps on creating letter b and the graphing tool used.
Parts A, C, and D. Can you put them all on one excel graph please. It should be one figure with three lines.
Answer the following problem based on the concept of Linear Correlation and Regression. Do not round off to the process of computing it but on the answers on this, round off your answers based on the instructions given. Use the formula of Pearson for computing the value of r. PROBLEM 1 The table below lists pulse rates and systolic blood pressures (in mm Hg). Is there sufficient evidence to conclude that there is a linear correlation between pulse rates and systolic blood pressures? Use alpha = 0.05. (show the solution) Pulse Rates (x) Systolic Blood Pressure (y) 80 100 84 112 94 134 74 126 50 114 60 134 52 118 58 138 66 114 62 124 52 138 a. What is the conclusion? There is a significant linear relationship between the pulse rates and the systolic blood pressure. There is no significant linear relationship between the pulse rates and the systolic blood pressure. b. Based on the problem and solving for the regression line y_pred = mx + b, what is the…
Let Yt be the sales during month t (in thousands of dollars) for a photography studio, and let Pt be the price charged for portraits during month t. The data are in the file Week 4 Assignment Chapter 12 Problem 64. Use regression to fit the following model to these data:Yt = a + b1Yt−1 + b2Pt + etThis equation indicates that last month’s sales and the current month’s price are explanatory variables. The last term, et, is an error term. If the price of a portrait during month 21 is $10, what would you predict for sales in month 21? Sales Price $400,000 $15 $1,042,000 $12 $1,129,000 $24 $1,110,000 $18 $1,336,000 $18 $1,363,000 $30 $1,177,000 $27 $603,000 $24 $582,000 $36 $697,000 $27 $586,000 $24 $673,000 $27 $546,000 $30 $334,000 $33 $27,000 $24 $76,000 $27 $298,000 $30 $746,000 $18 $962,000 $21 $907,000 $24

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