Please answer number 9 with complete solution 8. The regression equations of two variables x and y are x =y = 0.3x +2.8. Find the means of the two variables and the coefficient of0.7y + 5.2,correlation between them.9. If the coefficient of correlation between two variables x and y is 0.5 and the acuteangle between their lines of regression is tan-132, show that ox =8dy2+v3lab data, following resultsretrieved:were10. Froma partially destroyedLines of regression are:8x =10y - 66 and 18y = 40x-214, ox = 3%3DFind x,y, o, and r for the existing data.

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Answered: 9. If the coefficient of correlation…

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 4PPS

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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?
In a laboratory experiment on correlation research study the equation of the two regression lines were found to be 2X–Y+1=0 and 3X–2Y+7=0 Find the correlation between the two variables X and Y. Interpret the result as well.
For 40 eruptions of the Old Faithful geyser in Yellowstone National Park, duration times (sec) were recorded along with the heights (ft) of the eruptions. The linear correlation coefficient is r = 0.0915 and the regression equation is = 119 + 0.0331x, where x represents duration time. The mean of the 40 duration times is 245.0 sec and the mean of the 40 heights is 127.2 ft. What is the best predicted height of an eruption with a duration time of 120 min?
4b) The data shows a systolic and a diastolic blood pressure of certain patients. Find the linear regression equation, using the first variable x (systolic) as the independent variable. Find the best predicted diastolic blood pressure for a patient with a systolic blood pressure (y) reading of 140. What is the correlation coefficient, r? Using a significance level of a = 0.05, is there a significant linear relationship between systolic and diastolic blood pressure? Blood Pressure: Systolic Diastolic 112 125 115 136 143 116 123 124 elimii 70 89 65 90 97 64 SUTT nisinoo aqdM 21.SS bns aqdM 78 ahoqnis erit te zbesqz steb ils to 69 bns ago 20.EI to adimil srit terit sonabilnos 2 nistnoo aqdM 21.SS bnc agdM sgsavs arit ferli mislo a hoqnis orti roqque lovedni sonsbilnos 3028 wolsd insmsisiz tomo artezorio SeqdM 2.55 al 2.SS to sulavadi znistmoodi ezusaed mish ads toqque ton zaob 2.55 to sulsy sdt anistroo ti sausosd mislo ert hoqquz 200b to sulav orit nisinoo ton zoob 11 saussed misbb adi…
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, ŷ = bỏ + b₁x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.01 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations 23 39 9 Average Temperature (°F) 36 29 Snow Accumulation (in.) 5 13 21 38 24 35 22 25 37 13 21 23 13 12 9 Copy Data
1) Match each of the five scatterplots to the appropriate correlation coefficient and equation of regression line. Explain how you know that you have made a correct match. a. r= 0.83, y= -2.1 + 1.4x d. r= -0.83, y = 11.8 – 1.4x b. r= -0.31, y = 7.8 – 0.5x e. r= 0.41, y=1.4 + 1.4x c. r= 0.96, y= -2.1 + 1.4x Scatterplot 1 Scatterplot 2 Scatterplot 3 Scatterplot 4 Scatterplot 5
Two variable are found to have a strong positive linear correlation. Pick the regression equation that best fits this scenario. y = -0.34x + 25 y = 0.84x25 Oy = -0.84x + 25 Oŷ = 0.34x - 25
The slope of a regression line (y on x) is -0.24, we know that SDy=3.2 and SDx=4.0, then the correlation coefficient between the two variables is:A. 0.3B. -0.3C. 0.8D. -0.8
In an experiment, the independent variable is the percentage of hydrocarbons and the dependent variable is the purity of oxygen produced in a chemical distillation process that are present in the main condenser of the distillation unit. The simple linear regression and correlation analysis is performed in a sample of 9 observations. Results are as shown below: SSxx : 113.7356 SSyy = 0.5156 уу a = -4.2869 b = 0.0648 %3D
Consider the equation of the regression line y = 25 – 2.5x and ỹ = 12, %3D Find y when x = 6 assuming the linear correlation is not significant. %3D
It is known that the linear regression equation: y= -2.88+1.77x, with a coefficient of determination of 0.81. Based on the two information, the correlation coefficient is

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9. If the coefficient of correlation between two variables x and y is 0.5 and the acute angle between their lines of regression is tan-1, show that ox Oy 2+V3