x, Number of Years y, U.S. Population (millions) after 1969 1 (1970) 203.3 11 (1980) 226.5 21 (1990) 248.7 31 (2000) 281.4 41 (2010) 308.7

Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child ( height), height of the mother ( mothersheight ), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS 208.008457 314.295372 522.303829 df 2 104.004228 8.49446952 37 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared = Root MSE = = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What are the null and alternative hypotheses…

Refer to the data set:(-1, 2), (1, 3), (1, 5), (2, 7), (3, 8), (4, 11)Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 10

Dr. Lillian Fok, a New Orleans psychologist, specializes in treating patients who are agoraphobic (i.e., afraid to leave their homes). The following table indicates how many patients Dr. Fok has seen each year for the past 10 years. It also indicates what the robbery rate was in New Orleans during the same year. Year Number of Patients Robbery Rate per 1,000 Population The simple linear regression equation that shows the best relationship between the number of patients and the robbery rate is (round your responses to three decimal places) where y Number of Patients and x = Robbery Rate. = 1 2 3 4 6 7 36 33 40 41 55 60 58.3 61.1 73.4 75.7 81.1 89.0 101.1 5 40 8 54 94.8 9 58 103.3 10 61 116.2

Find the equation of the regression line for the given data. (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The table below shows the heights​ (in feet) and the number of stories of six notable buildings in a city. What is the regression equation?  Predict the value of y for x=503. (Please show me how you figured this out) Predict the value of y for x=641.

Biochemical oxygen demand (BOD) measures organic pollutants in water by measuring the amount of oxygen consumed by microorganisms that break down these compounds. BOD is hard to measure accurately. Total organic carbon (TOC) is easy to measure, so it is common to measure TOC and use regression to predict BOD. A typical regression equation for water entering a municipal treatment plant is BOD = -55.47 + 1.505 TOC Both BOD and TOC are measured in milligrams per liter of water. (a) What does the slope of this line say about the relationship between BOD and TOC? TOC rises (falls) by 1.505 mg/l for every 55.47 mg/l increase (decrease) in BOD BOD rises (falls) by 55.47 mg/l for every 1 mg/l increase (decrease) in TOC TOC rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in BOD BOD rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in TOC

What are the odds in favor of drawing a face card from a standard deck of 52 playing cards? 10:3 13:3 3:13 3:10 auto proctoru.com is sharing your screen Stop sharing Hide

Find the regression equation, letting the first variable be the predictor (x) variable. Find the best predicted Nobel Laureate rate for a country that has 78.5 Internet users per 100 people. How does it compare to the country's actual Nobel Laureate rate of 1.9 per 10 million people? Click the icon to view the data. Data table Find the equation of the regression line. Internet Users Per 100 Nobel Laureates V = + (Round the constant to one decimal place as needed. Round the coefficient to three decimal places as needed.) 79 5.4 79.6 24.5 78.7 8.5 44.7 0.1 83.6 6.1 38.6 0.1 90.2 25.5 89.4 7.7 79.1 9. 83.1 12.8 52.7 1.9 76.8 12.7 57.1 3.3 79.8 1.5 92.3 11.5 93.8 25.7 64.7 3.1 58.2 1.9 67.3 1.7 94 31.8 84.5 31.3 87 18.9 77.1 10.7

The data show the bug chirps per minute at different temperatures. Find the regression​ equation, letting the first variable be the independent​ (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted​ value?   Chirps in 1 min 784 838 822 949 1188 813   Temperature ​(°​F) 65.7 67.8 66.2 79.9 92.3 73.5   What is the regression​ equation?   What is the best-predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per​ minute?   The best predicted temperature when a bug is chirping at 3000chirps per minute are?   What is wrong with this predicted​ value? Choose the correct answer below.     A. It is unrealistically high. The value 3000 is far outside of the range of observed values.   B. The first variable should have been the dependent variable.   C. It is only an…

Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…