For a sample of 8 employees, a personnel director has collected the following data on ownership of company stock, y, versus years with the firm, x. 12 14 13 15 Y 300 408 560 252 288 650 630 522 (a) Determine the least squares regression line and interpret its slope (b) For an employee who has been with the firm 10 years, what is the predicted number of shares owned? (c) Is there a statistical significance between years of service and ownership of company stocks? (use a 0.05)

For a sample of 8 employees, a personnel director has collected the following data on ownership of company stock, y, versus years with the firm, x. 12 14 13 15 Y 300 408 560 252 288 650 630 522 (a) Determine the least squares regression line and interpret its slope (b) For an employee who has been with the firm 10 years, what is the predicted number of shares owned? (c) Is there a statistical significance between years of service and ownership of company stocks? (use a 0.05)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

19. You might think that increasing the resources available would elevate the number of plant spe- cies that an area could support, but the evidence suggests otherwise. The data in the accompany- ing table are from the Park Grass Experiment at Rothamsted Experimental Station in the U.K., where grassland field plots have been fertilized annually for the past 150 years (collated by Harpole and Tilman 2007). The number of plant species recorded in 10 plots is given in response to the number of different nutrient types added Plot 1 2 3 4 5 6 7 8 9 10 Number of nutrients added 0 0 0 3144 E2 3 Number of plant species 36 36 32 34 33 30 20 23 21 16
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 -20000 -20000 -40000 40000 TO 20 Index Normal Quantile Residuals vs. Program Per-Year Tuition ($) Residuals Predicted Values vs. Residuals Predicted Values 20000 140000- 120000- 100000 80000- -20000 60000 -40000 40000- 30000 50000 4000 Program Per-Year Tuition ($) 20000 60000 70000 -20000 20000 Residuals ..... a) To evaluate whether the assumption of linearity…
The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) to (c) below. Click the icon to view the data table. C... (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for female Find the least-squares regression line for males. ŷ=0x+0 (Round the slope to three decimal places and round the constant to the nearest integer as needed.) Data for licensed drivers by age and gender. 21-24 25-34 35-44 45-54 55-64 65-74 > 74 Number of Male Fatal Licensed Age Drivers (000s) < 16 12 16-20 6,424 6,914 18,068 20,406 Number of Number of Female Fatal Crashes Licensed (Males) Drivers (000s) 227 12 6,139 Crashes (Females) 77 2,113 1,534 5,180 5,016 6,816 8,567 17,664 2,780 7,990 20,047 2,742 19,984 14,441 8,386 5,375 19,898 14,328 8,194…
Add to MyRegistry 9 Mail - sgorham@a.. Like father, like son: In 1906, the statistician Karl Pearson measured the heights of 1078 pairs of fathers and sons. The following table presents a sample of 6 pairs, with height measured in inches, simulated from the distribution specified by Pearson. Compute the least-squares regression line for predicting son's height (v) from father's height (x). Round the slope and y-intercept values to at least four decimal places. Father's Son's height height 73.6 74.9 66.7 68.8 70.1 73.3 72.3 71.9 73.6 76.5 69.3 71.4 Send data to Excel Regression line equation: Save For Later Submit Assignment Check Answer © 2021 McGraw Hill LLC. AlI Rights Reserved. Terms of Use Privacy Center
A 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 following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts (a) through (f). Click here to view the data. Click here to view the critical values of the corelation coefficient (a) Find the least-squares regression line treating speed at which the ball was hit as the explanatory variable and distance the ball traveled as the response variable. y (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Begin by interpreting the slope. Data table O A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit. O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was…
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. Thecorrelation coefficient is 0.944 and the least squares regression line is y = 0.199x + 11.982. Complete parts (a) and (b)below.Height, x27.5 25.5 26.25 25.25 27.5 26.25 26 27.25 27.25 27 27.25 ФHead Circumference, # 17.5 17.0 17.2 17.0 17.5 17.3 17.2 17.4 17.3 17.3 17.4(a) Compute the coefficient of determination, R?R?.% (Round to one decimal place as needed.)(b) Interpret the coefficient of determination and comment on the adequacy of the linear model.Approximately % of the variation inis explained by the least-squares regression model.According to the residual plot, the linear model appears to be (Round to one decimal place as needed.)
A researcher records data on 7 adult pairs' heights (in inches) to compare the physical characteristics of brothers and sisters. Brother Sister 71 69 68 64 6 65 67 63 70 65 71 62 66 62 Mean 68.4285 64.2857 SD 2.2253 2.4299 r=0.4050 What would the least-squares regression equation be for predicting the brother's height from the sister's? A. brother's height = 0.037+44.58 * sister's height B. brother's height = 44.58 + 0.371* sister's height C. brother's height = 20.71 + 0.029 * sister's height D. brother's height = 3.28- 40.68 * sister's height If the sister's height is the same as the mean (64.2857 inches), what would the brother's predicted height be A. 68.4285 (the same as the mean as well) B. 62.1234 C. 70.8990 D. None of the above. Which of the following would be correct? A. The pair of means, (68.4285, 64.2857), lies on the linear regression line. B. The effectiveness of the linear regression model is about 16%, C. The effectiveness of the linear regression model is 10096. D. Both…
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Number of Accidents Jan 25 Feb 40 Mar 60 Apr 90 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): ŷ-0-0x Using least-squares regression, the forecast for the number of accidents that will occur in the month of May = accidents (enter your response as a whole number).
An article on the cost of housing in California that appeared in the San Luis Obispo Tribunet included the following statement: "In Northern California, people from the San Francisco Bay area pushed into the Central Valley, benefiting from home prices that dropped on average $4000 for every mile traveled east of the Bay area." If this statement is correct, what is the slope of the least-squares regression line, ý = a + bx, where y = house price (in dollars) and x = distance east of the Bay (in miles)? Your answer cannot be understood or graded. More Information
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.858 and the least squares regression line is y = 0.228x +11.187. Complete parts (a) and (b) below. Height, x 27.5 25.75 26.5 25.5 27.25 26.25 25.75 27.25 27 27.25 27 Head Circumference, y 17.4 17.2 17.2 16.9 17.6 17.1 17.1 17.4 17.4 17.3 17.3 (a) Compute the coefficient of determination, R². R² =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination and comment on the adequacy of the linear model. Approximately % of the variation in (Round to one decimal place as needed.) is explained by the least-squares regression model. According to the residual plot, the linear model appears to be
Draw a scatterplot of the right foot temperature (y) versus the left foot temperature (x). Then draw the least-squares regression line on the graph.