69Q1. [6 points] :From the following data set3\9.16.5.411161.find correlation coefficients2. Estimate the simple linear regression model3. Find the predicted value of y when x-10

Given table : X Y 1 4 2 8 3 9 6 11 5 16

Answered: 002 49 Q1. [6 points] :From the…

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

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 2AGP

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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?
Can the average rate of change of a function be constant?
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For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
3- Find the equation of the regression line and find the y_ value for the specified x value. Calories and Cholesterol Calories and cholesterol are as follows: Calories X 390 535 720 300 430 500 440 Cholesterol y 43 45 80 50 55 52 60 Find y-hat when x = 600 calories. LO LO
At one of the San Diego area beaches, there has been a problem with thefts in the summer months. A random sample of summer days was selected. For each day, the number of street cameras (X) in the beach area and the number of reported thefts (Y) were recorded. The data was analyzed to produce the results below: The regression equation is: Thefts = 9.78 – 0.493*(cameras) On a certain day there were 5 cameras in the area and 3 reported thefts. Calculate the residual for this day. 8.301 -4.315 4.315 3.301 7.315
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, yˆ=b0+b1xy^=b0+b1x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.010.01 level of significance. Average Temperatures and Snow Accumulations Average Temperature (℉℉) 38 30 17 39 45 22 34 24 29 38 Snow Accumulation (in.in.) 6 19 27 5 13 26 26 14 13 5 Copy Data
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, yˆ=b0+b1xy^=b0+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. Average Temperatures and Snow Accumulations Average Temperature (℉℉) 45 34 24 45 39 20 31 19 35 44 Snow Accumulation (in.in.) 9 16 24 9 15 28 25 18 16 5 1. Regression equation: y=__________ 2. Is the equation appropriate? yes or no
10) A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.767 b=31.009 r2=0.609961 r=-0.781 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)
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002 49 Q1. [6 points] :From the following data set 1 4. 6. 11 16 1.find correlation coefficients 2. Estimate the simple linear regression model 3. Find the predicted value of y when x-10