Compute the least-squares regression line for the given data set. 4 6. 7. 8.5 8.4 10 11.3 11.9 13.1 Oy = 5.0648x + 0.9943 Oy = 0.9400x + 8.5 %D Oy = 0.9943x + 5.0648 Oy= 0.9400x + 5.0648
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- In a study, nine tires of a particular brand were driven on a track under identical conditions. Each tire was driven a particular controlled distance (measured in thousands of miles) and the tread depth was measured after the drive. Tread depth is measured in "mils." Here, 1 mil is 0.001 inch. The equation of the least-squares regression line is: y-hat 360.64 - 11.39x Also, r = 0.9762. For every 1,000 miles driven, the decrease in tread depth (in mils) can be estimated as: 246.74 mils. 11.39 mils. 275.6 mils. O 360.64 mils.Find the required linear model using least-squares regression. The following table shows the number of operating federal credit unions in a certain country for several years. 2011 2012 2013 2014 4497 4323 4156 3976 Year Number of federal credit unions (a) The linear model for these data is y = (Round to the nearest tenth as needed.) (a) Find a linear model for these data with x = 11 corresponding to the year 2011. (b) Assuming the trend continues, estimate the number of federal credit unions in the year 2018. X + 2015 (b) The estimated number of credit unions for the year 2018 is (Round to the nearest integer as needed.) 3807A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data. Complete parts (a) through (g) below. Click the icon to view the children's data. Data table (a) Find the least-squares regression line treating height y =x+ (O (Round the slope to three decimal places and round the d Height (inches), x Head Circumference (inches), y O. 28 17.6 24.5 17.1 25.75 17.1 25.75 17.5 24.25 17.0 27.75 17.7 26.5 17.3 27.25 17.6 26.5 17.3 26.5 17.5 27.75 17.6 Print Done Help me solve this View an example Get more help - Media - Clear all Check answer
- The y-interept bo of a least-squares regression line has a useful interpretation only if the x-values are either all positive or all negative. Determine if the statement is true or false. Why? If the statement is false, rewrite as a true statement.A financial analyst is examining the relationship between stock prices and earnings per share. She chooses fifteen publicly traded companies at random and records for each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with x denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least- squares regression line to be y = -0.137 +0.043x. This line, along with a scatter plot of her data, is shown below. Earnings per share, x (in dollars) Current stock price, y (in dollars) 59.10 2.35 30.89 1.34 40.95 1.76 29.50 1.02 48.19 1.63 37.29 1.68 21.65 0.63 15.95 0.63 1- 26.45 1.02 0.5 32.26 1.53 17.72 0.78 42.86 1.50 Earnings per share (in dollars) 36.45 1.17 57.56 2.84 40.32 1.06 Current stock price (in dollars)A financial analyst is examining the relationship between stock prices and earnings per share. She chooses sixteen publicly traded companies at random and records for each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with X denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least- squares regression line to be ŷ=-0.231+0.045x. This line, along with a scatter plot of her data, is shown below. Earnings per share, x Current stock price, y (in dollars) (in dollars) 58.47 2.17 30.64 1.44 41.03 1.45 41.26 1.05 26.04 0.80 42.80 1.84 18.10 0.77 21.73 0.62 29.68 0.74 32.97 1.64 37.74 1.56 57.98 2.70 38.05 1.16 49.40 1.59 15.49 0.59 51.67 2.65 Send data to calculator ✓ Send data to Excel Current stock price (in dollars) Based on the analyst's data and regression line, complete the following. 05 0 10 30 x x x **…
- A manager at the Camden Walmart is interested in learning more about the relationship between the number of customers in a checkout line and the total time it takes to check out. She selects a random sample of 11 customers and measured the number of customers who were in front of the selected customer in line and the time until that customer had finished checking out. An analysis of the data is provided below. Identify and interpret the y-intercept of the least squares regression line in context. Identify the coefficient of determination, r2. Interpret it in context. One of the data points appears to be an outlier. Describe this point and explain why it is considered an outlier.a. Develop the least squares estimated regression equation that relates labor hours to house square footage and type of flooring. b. Use the regression equation developed in part (a) to predict labor hours when the house size is 3350 square feet and the type of flooring is wood.In a study of 1991 model cars, a researcher computed the least-squares regression line of price (in dollars) on horsepower. He obtained the following equation for this line. Price = – 6677 + 175× Horsepower Based on the least-squares regression line, what would we predict the cost to be of a 1991 model car with horsepower equal to 200? If the actual cost of a 1991 car with 200 horsepower is $27500, what is the residual? Is the predictionan underestimate or an overestimate? What does the slope of 175 and y intercept of (0,-6677) mean in the context of the problem? The coefficient of determination is ?2=84%. Interpret in the context of the problem. Find the correlation and interpret.
- A prospective MBA student would like to examine the factors that impact starting salary upon graduation and decides to develop a model that uses program per-year tuition as a predictor of starting salary. Data were collected for 37 full-time MBA programs offered at private universities. The least squares equation was found Y; = -13258.594 + 2.422X;, where X; is the program per-year tuition and Y; is the predicted mean starting salary. To perform a residual analysis for these data, the following results are obtained. of regression have been seriously violated. Residual index plot QQ Plot of Residuals Residuals Residuals 20000- 20000 0. -20000 -20000 a) To evaluate whether the assumption of linearity has been violated, which of the following graph shou be examined? A. Predicted Values vs. Residuals B. Residual index plot C. QQ plot of residuals D. Residuals vs. Progrm Per-Year Tuition ($) b) To evaluate whether the assumption of normality has been violated, which of the following graph…A financial analyst is examining the relationship between stock prices and earnings per share. She chooses sixteen publicly traded companies at random and records for each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with x denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least- squares regression line to be y =-0.303 +0.048x. This line, along with a scatter plot of her data, is shown below. Earnings per share, x (in dollars) Current stock price, y (in dollars) 36.37 1.61 52.57 2.80 57.34 2.81 36.26 1.29 57.82 2.24 41.36 1.66 51.02 1.71 Xx X 31.83 0.78 27.38 0.82 14.63 0.51 it 0.5. 33.70 + + 1.64 39.97 1.06 42.39 1.91 Earnings per share (in dollars) 18.83 0.68 21.10 0.83 29.79 1.33 Send data to calculator Send data to Excel Based on the analyst's data and regression line, answer the following. (a)…An engineer is testing a new car model to determine how its fuel efficiency, measured in L/(100 km), is related to its speed, which is measured in km/hour. The engineer calculates the average speed for 30 trials. The average speed is an example of a (statistic or parameter) The engineer would like to find the least squares regression line predicting fuel used (y) from speed (x) for the 30 cars he observed. He collected the data below. Speed 62 65 80 82 85 87 90 96 98 100 Fuel 12 13 14 13 14 14 15 15 16 15 Speed 100 102 104 107 112 114 114 117 121 122 Fuel 16 17 16 17 18 17 18 17 18 19 Speed 124 127 127 130 132 137 138 142 144 150 Fuel 18 19 20 19 21 23 22 23 24 26 The regression line equation is Round each number to four decimal places.
