O Bio Notes Shaping Evolutionary xA Acellus - The Science of Learning xA To-doellus.com/StudentFunctions/Interface/acellus_engine.html?ClassID=410244051- MathGrades K to 12A A Acellus - The Scienc.M My GmailA My ClassesO My Drive - Google D.O Algebra 2 FolderO My MathbookW DesmoIntro to Slope-Intercept FormellusWhat is the slope-intercept equation for this line?y = [?]x +17Enter© 2003 - 2021 Acellus Corporation. All Rights Reserved.Sign out

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Answered: What is the slope-intercept equation…

Math

Algebra

What is the slope-intercept equation for this line? y = [?]x+[ ] -5 Enter

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter2: Equations And Inequalities

Section2.7: More On Inequalities

Problem 44E

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Jóhannes plans to examine the salary of a middle manager in a large company. He collects data on salaries and how long they have been employed as middle managers with the aim of creating a model that can predict salaries with work experience. Jóhannes has data from 20 randomly selected middle managers who range from being new to the job to having 12 years of work experience. Jóhannes started by drawing the data and sees that the relationship is linear. He also calculates some light sizes that can be seen below: Convert Average Standard deviation Salary 761041 238473 Job experience 5,65 3,71 He also calculated the correlation coefficient between wages and work experience and obtained a r = 0.82. a) Find an equal regression line for Jóhannes's modelb) What is the explanatory power of the model and what does it tell us?c) How much does a middle manager's salary increase in five years according to the model?d) Use the model to predict the salary of a middle manager with 20 years…
Consider the following variables: Y=daily productivity score (measured in points) X1=0 if undergraduate student,1 if graduate student X2=hours of sleep per night 1. If you want to test whether type of student modifies the association between hours of sleep per night and daily productivity score, which model (from attached image) should you consider and what is the null hypothesis for this test? A. Use Model 3 and test H0: β2=0 B. Use Model 5 and test H0: β3=0 C. Use Model 5 and test H0: β1=0 D. Use Model 5 and test H0: β2=0 E. Use Model 2 and test H0: β1=0 2. Suppose that you decide to use Model 5 (from pocture attached) to describe the relationship between type of student, hours of sleep per night, and daily productivity score. You use the method of least squares to obtain the following: Y= -0.5 + 3(X1) + 1.5(X2) + 2.5(X1X2) What is the estimated slope relating hours of sleep per night and daily productivity score among undergraduate students? A. 2 B. None of…
QU 4. (i) How would you determine whether two or more continuous variables of a population are associated? By what features may the characteristic nature of the relationships existing between the variables be explained?(ii) Using the data provided below conduct a biostatistical analysis to demonstrate that the variables of interest in the populations from which these samples were drawn are associated or not and characterize the nature of the relationships existing between the variables? X 5 8 13 15 19 6 12 20 25 28 27 30Y 125 126 130 131 132 126 129 135 140 145 143 142
Suppose a researcher wants to see if the proportion of college educated adults is higher in New York compared to Florida. The researcher randomly samples 500 adults in New York and found that 196 had a college education, while a random sample of 600 adults in Florida found that 227 had a college education.At the 0.05 level of significance, does the data provide evidence to suggest the proportion of college educated adults is higher in New York than Florida? Step 1: Define the parameter & setup the testStep 2: State the Level of SignificanceStep 3: Find the value of the Test StatisticsStep 4: Find P-Value OR Find Critical ValueStep 5: State Conclusion and why
Snowpacks contain a wide spectrum of pollutants thatmay represent environmental hazards. The article“Atmospheric PAH Deposition: Deposition Velocitiesand Washout Ratios” (J. of EnvironmentalEngineering, 2002: 186–195) focused on the depositionof polyaromatic hydrocarbons. The authors proposeda multiple regression model for relating depositionover a specified time period (y, in mg/m2) to tworather complicated predictors x1 (mg-sec/m3) and x2 (mg/m2), defined in terms of PAH air concentrations forvarious species, total time, and total amount of precipitation.Here is data on the species fluoranthene andcorresponding Minitab output:obs x1 x2 flth1 92017 .0026900 278.782 51830 .0030000 124.533 17236 .0000196 22.654 15776 .0000360 28.685 33462 .0004960 32.666 243500 .0038900 604.707 67793 .0011200 27.698 23471 .0006400 14.189 13948 .0004850 20.6410 8824 .0003660 20.6011 7699 .0002290 16.6112 15791 .0014100 15.0813 10239 .0004100 18.0514 43835 .0000960 99.7115 49793 .0000896 58.9716 40656…
The variables in our dataset include the college grade point average (colGPA), high school GPA (hsGPA), and achievement test score (ACT) for a sample of 141 students from a large university; both college and high school GPAs are on a four- point scale. We estimate the following model by OLS: colGPA=β₁+β₂hsGPA+β₃ACT+u,and obtain the following parameter estimates β₁=1, β₂=0.5, and β₃=0.02. Predict difference of college GPA's of two students (colGPAB-colGPAA) where the student B has a 0.4 higher GPA in high school and 5 points higher ACT score compared to student A.
The Tiliche Corp. analyst conducted 10 independent timing studies in the manual spray painting section of the finishing department. The product line under study revealed a direct relationship between spray painting time and product surface area. The following data were collected (ignore the qualification factor): (image) a) Find the linear model relating standard time (y) to surface area (x) using linear regression.(x) using linear regression.b) How much time would you allocate to spray painting a new part with a surface area of 250 in2?surface area of 250 in2?c) Obtain the fitted value of y and the corresponding residual for a particular assembly (study #3), with a surface area of 250 inches2?(study #3), with an area of 150 square inches.d) Perform a significance test of the regression using α= 0.05. Find the P-value for this test.What are your conclusions?e) Estimate the standard errors of the slope and the value of the intercept.f) Calculate the coefficient of determination R2 .g)…
Consider the following variables: Y=daily productivity score (measured in points) X1=0 if undergraduate student,1 if graduate student X2=hours of sleep per night 1. The plot shown below could possibly be the graph of which model? 2. If you want to test whether type of student modifies the association between hours of sleep per night and daily productivity score, which model should you consider and what is the null hypothesis for this test? 3. Suppose you use Model 5 to describe the relationship between type of student, hours of sleep per night, and daily productivity score. You use the method of least squares to obtain: Y = -0.5 + 3 (X1) + 1.5 (X2) + 2.5 (X1)(X2).
Describe and compare the measurement of the central tendency and thedispersion of the electric usage before and after implementing the newelectric meter based on the histogram and box plots given.
The data in the following table show the amount of water in milliliters a professional tennis player loses for various times, in minutes, of play during a tennis match. Time of Workout(in minutes), x 10 20 30 40 50 60 Water Lost(in milliliters), y 500 900 1,200 1,500 2,000 2,200 (a) Use the points (10, 500) and (60, 2,200) to find a linear model that will predict the water lost y (in mL) by a player who has been playing for x minutes. y = (b) Use your linear model to estimate the amount of water lost (in mL) by a player who has been playing for 15 minutes. mL
(a)The following table shows the amount of melted plastic (in grams) at various temperatures of the plastic (in Celsius). Temperature (Celcius) 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 Melted Plastic (in grams) 8.1 7.8 8.5 9.8 9.5 8.9 8.6 10.2 9.3 9.2 10.5 (I)Find the linear regression equation of the amount of melted plastic temperature. (ii)Estimate the amount of melted plastic at temperature 1.75 Celcius. (iii)Calculate the coefficient of correlation and comment on the relationship between temperature and amount of melted plastic. (iv)Calculate the Spearman’s rank correlation coefficient for the above data. (b)A random sample of twelve students were chosen, and their midterm test score ( y), assignment score (x1), and missed classes (x2) were…