Example 9: The equations of two regression lines are 7x-16y+9=0 and 5y-4x-3 = 0.. Find the coefficient of correlation and the means of r and u
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A: Solution: The least square estimated regression equation is y^= 784.6x+12431 Where y is the median…
Q: The table below gives the number of hours spent unsupervised each day as well as the overall grade…
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A: Note: Since we only answer up to 3 sub-parts, we'll answer the first 3. Please resubmit the…
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A: y=21-8x
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A: The correlation coefficient is calculated as r=∑(xi-x¯)(yi-y¯)∑(xi-x¯)2(yi-y¯)2
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Q: Consider a regression and correlation analysis where = 1. We know that, Multiple Choice SSE must be…
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Q: Find the coefficient of determination and interpret the result.
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Q: Hours Unsupervised 0 1 3 4 5 Overall Grades 95 92 85 81 62 Table Step 6 of 6 : Find the value…
A: (X) : { 0,1,3,4,5 } (Y) : { 95,92,85,81,62 } Here : X = hours Unsupervised Y = Overall Grades…
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Q: The table below gives the number of hours spent unsupervised each day as well as the overall grade…
A: Hours Unsupervised(x) Overall Grades(y) 1 99 2 81 2.5 73 3.5 72 4 67 5.5 65 6 63
Q: per ge The proportion of the variability in miles per gallon explained by the relation between…
A: Answer:----. Date:----12/10/2021 r = -0.984 So, r^2 = 0.968256
Q: how long they stay underwater. For all but the shallowest dives, there is a linear relationship that…
A: Given that,Depth (D): The depth of the penguin's dive, measured in meters.Dive duration (DD) : The…
Q: The table below gives the number of hours spent unsupervised each day as well as the overall grade…
A: Here the given information is The table below gives the number of hours spent unsupervised each day…
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Q: The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the…
A: Given: The least-squares regression equation: y=620.6x+16,624 where, y is the median income x is…
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Q: A study of king penguins looked for a relationship between how deep the penguins dive to seek food…
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Q: If a correlation is r= 0.00, then SP = 0 and the regression equation is O Ý =X +0 O Ý =X + X O Ý =0…
A: Given: correlation is r = 0.00, then SP = 0
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A: Calculate the following values. x y (x - mean_x)2 (y - mean_y)2 (x - mean)*(y - mean_y) 39 355…
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- The table shows the numbers of new-vehicle sales (in thousands) in the United States for Company A and Company B for 10 years. The equation of the regression line is y = 0.991x + 1,222.81. Complete parts (a) and (b) below. D New-vehicle sales (Company A), x New-vehicle sales (Company B), y 4,149 3,923 3,566 3,400 3,266 3,076 2,868 2,485 1,952 2,066 4,912 4,871 4,827 4,721 4,672 4,474 4,684 3,822 2,956 2,754 (a) Find the coefficient of determination and interpret the result. 12²=0 (Round to three decimal places as needed.) 1The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for five randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 2 3 4 5 6 Overall Grades 94 86 79 71 62 Table Step 1 of 6 : Find the estimated slope, y intercept and correlation coefficient Round your answer to three decimal places. Answerwhen a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?
- The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0 1 1.5 2.5 4 5.5 6 Overall Grades 98 86 85 83 80 78 67 Table Step 1 of 6: Find the estimated slope, y intercept, correlation cofficient Round your answers to three decimal places.Explain how to find seplease use this situation: A small theater company has a linear regression model to estimate y = the concession stand sales in dollars, based on knowing x = the number of people in attendance. The regression equation is: = 6.72x + 11.50 and the correlation coefficient was r = 0.781. The data set saw the number of people in attendance ranging from a minimum of 18 people to a maximum of 170 people. 1) How reliable would it be to make a prediction for the concession sales amount if there were 500 people in attendance? Explain.
- The table shows the average weekly wages (in dollars) for state government employees and federal government employees for 8 years. The equation of the regression line is y = 1.493x - 83.403. Complete parts (a) and (b) below. A Average Weekly Wages (state), x Average Weekly Wages (federal), y 764 1003 766 1048 791 1119 (a) Find the coefficient of determination and interpret the result. r² = 0 (Round to three decimal places as needed.) 800 1152 843 1201 887 1250 924 1277 939 1306Data were collected that included information on the weight of the trash (in pounds) on the street one week and the number of people who live in the house. The figure shows a scatterplot with the regression line. Complete parts (a) through (d) below.The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.972. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y= - 0.0070x + 44.4405. Complete parts (a) and (b) below. Click the icon to view the data table. ..... (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in is by the linear model. Data Table (Round to one decimal p Full data set gas mileage Miles per Weight (pounds), x Weight (pounds), x Miles per Gallon, y Car Car Gallon, y…
- Jackson finds that the heavier a person is, the higher his pulse rate tends to be. A linear regression model he built suggests that 20-kilogram differences in weight correspond to differences in pulse rate of 5 beats per minute. Which of the following must be true and explain your answer. (I) The correlation coefficient between body weight and pulse rate is 1/4. (II) Your pulse rate slows down 3 beats per minute if your weight decreases by 12 kilograms.A study of king penguins looked for a relationship between how deep the penguins dive to seek food and how long they stay underwater. For all but the shallowest dives, there is a linear relationship that is different for different penguins. The study report gives a scatterplot for one penguin titled " The relation of dive duration (DD) to depth (D)." Duration DD is measured in minutes and depth D is in meters. The report then says, " The regression equation for this bird is: DD = 2.33 + 0.001 D. (a) What is the slope of the regression line?. ANSWER ? minutes per meter. (b) According to the regression line, how long does a typical dive to a depth of 100 meters last? ANSWER ? minutes.In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?