Question 1Consider the following data on the minimum temperatures in SLC by year. Create a linear regressionmodel by year and predict the minimum temperature for 2020.Data: SLC yearly minimum temperatureYear19701963201019521975197820131990 1995Min temp39.638.242.039.439.641.542.741.542.1324043.2

Answered: Image /qna-images/answer/145f9380-8af6-482b-aaf9-147ba5e6c27d.jpg

Answered: Consider the following data on the…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 5PPS

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Planetary Velocity The following table gives the mean velocity of planets in their orbits versus their mean distance from the sun. Note that 1AU astronomical unit is the mean distance from Earth to the sun, abut 93 million miles. Planet d=distance AU v=velocity km/sec Mercury 0.39 47.4 Venus 0.72 35.0 Earth 1.00 29.8 Mars 1.52 24.1 Jupiter 5.20 13.1 Saturn 9.58 9.7 Uranus 19.20 6.8 Neptune 30.05 5.4 Astronomers tell us that it is reasonable to model these data with a power function. a Use power regression to express velocity as a power function of distance from the sun. b Plot the data along with the regression equation. c An asteroid orbits at a mean distance of 3AU from the sun. According to the power model you found in part a, what is the mean orbital velocity of the asteroid?
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?
Life Expectancy The following table shows the average life expectancy, in years, of a child born in the given year42 Life expectancy 2005 77.6 2007 78.1 2009 78.5 2011 78.7 2013 78.8 a. Find the equation of the regression line, and explain the meaning of its slope. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 2019? e. Based on the trend of the regression line, what do you predict as the life expectancy of a child born in 1580?2300
Which of the variables is the indepenent variable and dependent variable for the following question. fit a simple linear regression model to predict latitudes using average monthly range lat= latitudes range= the average monthly range between mean montly maximum and minimum temperatures for a selected set of US cities.
Use the scatterplot of Vehicle Registrations below to answer the questions Vehicle Registrations in the United States, 1925- 2011 Vehicles millions 300 y = 3.0161x - 5819.5 R² = 0.9695 250 200 150 100 50 1920 -50 1940 1960 1980 2000 2020 Year State the trend line (regression line). y= 3.0161 x -5819.5 year number of vehicle registrations R^2 = 0.9695 not enough information to determine Registrations (in millions)
Which of the following data sets or plots could have a regression line with a negative slope? Select all that apply. Select all that apply: the difference in the number of ships launched by competing ship builders as a function of the number of months since the start of last year the number of hawks sighted per day as a function of the number of days since the two-week study started the total number of ships launched by a ship builder as a function of the number of months since the start of last year the average number of hawks sighted per day in a series of studies as a function of the number of days since the ten-week study started
The global mean sea level variations taken from satellite observations in the month of May are given in the table below. The variations are calculated using the base May 1997. for example, the mean sea level in May 2008 was 34.37 mm higher than in May 1997. Find the linear model for the data and use it to predict the change in sea level for the year in 2017. year 1997 1999 2001 2005 2008 2012 2014 Sea level change (mm) 0 4.77 15.25 30.75 34.37 47.84 54.13 A)what method will you use to find the model, polynomial interolation or least squares, why? b) write the equation that must be used to find the model and then write the system of equations. c) wirte the vandermonde matrix and the other matrices that must be used. e) write the MatLab formula to find the model. f) wirte the cubic model(find the values using MatLab)
Perform Linear Regression Analysis for all three tabs:a. Ln Safety Belt Rate Fatalities Ln Safety Belt Rate 1071 3.951243719 1138 4.058717385 996 4.257030144 991 4.374498368 1038 4.365643155 1004 4.348986781 1154 4.382026635 1148 4.404277244 1207 4.417635062 1110 4.410371108 969 4.455509411 848 4.49980967 862 4.515245478 895 4.477336814 865 4.494238625 853 4.577798989 820 4.561218298 850 4.535820108 1083 4.521788577 948 4.531523646 953 4.519612298 856 4.525044142 b. Ln Unemployment Rate Fatalities Ln Unemployment Rate 1071 1.504077397 1138 1.547562509 996 1.526056303 991 1.648658626 1038 1.774952351 1004 1.791759469 1154 1.722766598 1148 1.481604541 1207 1.386294361 1110 1.410986974 969 1.757857918 848 2.312535424 862 2.341805806 895 2.261763098 865 2.104134154 853 1.987874348 820 1.902107526 850 1.808288771 1083 1.774952351 948 1.526056303 953 1.360976553 856 1.098612289 c. Ln Median…
Ocean currents are important in studies of climate change, as well as ecology studies of dispersal of plankton. Drift bottles are used to study ocean currents in the Pacific near Hawaii, the Solomon Islands, New Guinea, and other islands. Let x represent the number of days to recovery of a drift bottle after release and y represent the distance from point of release to point of recovery in km/100. The following data are representative of one study using drift bottles to study ocean currents. x days 76 79 36 95 207 y km/100 14.2 19.6 5.4 11.2 35.9 (a) Verify that Σx = 493, Σy = 86.3, Σx2 = 65,187, Σy2 = 2029.21, Σxy = 11317.3, and r ≈ 0.93885. Σx Σy Σx2 Σy2 Σxy r (b) Use a 1% level of significance to test the claim ρ > 0. (Use 2 decimal places.) t critical t (c) Verify that Se ≈ 4.6183, a ≈ 0.5575, and b ≈ 0.1694. Se a b Find the predicted distance (km/100) when a drift bottle has been floating for 60 days. (Use 2…
Which of the following data sets or plots could have a regression line with a negative slope? Select all that apply: the number of miles a ship has traveled as a function of the number of years since it was launched the number of miles a ship has traveled each year as a function of the number of years since it was launched the number of cats living in an abandoned lot as a function of the number of years since the building was torn down the number of cats born each year in an abandoned lot as a function of the number of years since the building was torn down
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right am blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 100 mm Hg Use a significance level of 0.05. Right Arm 103 102 95 78 Left Arm 175 169 147 146 144 m Click the icon to view the critical values of the Pearson correlation coetticient r The regression oquation is y =+O (Round to one decimal place as needed) Given that the systolic blood pressure in the right arm is 100 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg (Round to one decimal place as needed.)

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