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

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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 Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + B₂hwrte + Â¸priGPA+ Â¹ ACT reg final atndrte hwrte priGPA ACT Source Model 3094.70776 Residual 11929.9465 SS Total 15024.6543 final atndrte hwrte priGPA ACT _cons df MS 773.676939 4 669 17.832506 673 22.324895 Coef. Std. Err. Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE t P>|t| .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 1.46863 9.225908 Construct a 95% confidence interval about 33. The lower bound is |||||||||||| 674 [95% Conf. Interval]
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…
Identify the description(s) that adhere to the requirements for testing a population correlation between variables X and Y. The log-transformed version of Y and X have a linear relationship. The shape of the scatter plot for the data collected for the X and Y variables is shaped like a funnel. The scatter plot for the data collected for the X and Y variables do not show any outliers. The frequency histograms for X and Y separately are each normally distributed.
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + B₂hwrte + ßpriGPA+ BACT . reg final atndrte hwrte priGPA ACT Source Residual Yes No Model 3094.70776 11929.9465 Total 15024.6543 final atndrte hwrte priGPA ACT _cons SS df 4 669 Not enough information. Coef. Std. Err. .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 9.225908 1.46863 MS 673 22.324895 773.676939 17.832506 Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE Is the estimate of B2 statistically different from zero at a significance level of .05? t P>|t| |||||||||||| 674 [95% Conf. Interval]
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final = 30 +3₁atndrte + B₂hwrte +33priGPA + B ACT .reg final atndrte hwrte priGPA ACT Source Model 3094.70776 Residual 11929.9465 Total final . reg atndrte hwrte priGPA ACT _cons Total .reg final priGPA ACT final priGPA ACT _cons Source Model 2950.00785 Residual 12074.6465 Source ss 15024.6543 Total atndrte hwrte priGPA ACT _cons Coef. Std. Err. SS atndrte hwrte priGPA ACT .0138725 .0183476 .010863 1.906347 -3750236 .3990516 .0535332 9.225908 1.46863 15024.6543 Calculate the se(3₁). Model 89295.5136 Residual 93922.8539 df 4 669 SS 183218.367 673 22.324895 Coef. Std. Err. df 2.390227 .3233916 .3534674 .0505019 11.74854 1.147688 673 MS 773.676939 17.832506 2 1475.00393 671 17.9950022 df MS 22.324895 t P>|t| [95% Conf. Interval] MS Number of obs F(4, 669) Prob > F R-squared 3 29765.1712 670 140.183364 Adj R-squared Root MSE 673 272.241259 Number of obs F(2, 671)…
Use the output below to test to see if there is a significant negative linear relation between atmospheric pressure and maximum wind speed of hurricanes. Use a 1% level of significance. Correlations: Pressure, WindSpeed Pearson correlation of Pressure and WindSpeed -0.958 %3D P-Value = 0.000
Seedlings of understory trees in mature tropical rainforests must survive and grow using intermittent flecks of sunlight. How does the length of exposure to these flecks of sunlight (fleck duration) affect growth? Researchers experimentally irradiated seedlings of the Southeast Asian rainforest tree with flecks of light of varying duration while maintaining the same total irradiance to all the seedlings. Below is the data. Fit a linear model to the data. Tree Mean Fleck (min) Relative growth rate (mm/mm/week 1 3.4 0.013 2 3.2 0.008 3 3 0.007 4 2.7 0.005 5 2.8 0.003 6 3.2 0.003 7 2.2 0.005 8 2.2 0.003 9 2.4 0 10 4.4 0.009 11 5.1 0.01 12 6.3 0.009 13 7.3 0.009 14 6 0.016 15 5.9 0.025 16 7.1 0.021 17 8.8 0.024 18 7.4 0.019 19 7.5 0.016 20 7.5 0.014 21 7.9 0.014 a)What is the rate of change in relative growth…

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