A departmental store has the following sales for a period of last one year of 8 salesmen, who have different years of education.Years of Education81010121416Annual Sales (Thousand Tk.)100120801008010070130a. Determine the correlation coefficient r between the two variables.Answer rounded to at least 4 decimal places.b. Determine the slope coefficient B1 for the regression of annual sales on years of education.Answer rounded to at least 4 decimal places.c. Determine the y-intercept Bo for the regression of annual sales on years of education.Answer rounded to at least 4 decimal places.d. What will be the annual sales when the years of education is 11 (years)?Answer rounded to at least 4 decimal places.Thousand Tk.

Correlation coefficient is the degree of correlation between two variables. And regression is a…

Answered: A departmental store has the following…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Suppose that for a typical FedEx package delivery, the cost of the shipment is a function of the weight of the package measured in ounces. You want to try to predict the cost of a typical shipment given package dimensions. If 10 packages in a city are sampled and the regression output is given below, report the regression equation. 1) (cost of delivery) = 1.468*(weight) - 23.015 2) (weight) = -23.015*(cost of delivery) + 1.468 3) (cost of delivery) = -23.015*(weight) + 1.468 4) (cost of delivery) = 1.468*(weight) 5) (weight) = 1.468*(cost of delivery) - 23.015
The table below gives the age and bone density for 5 women. Use the equation of the regression line, y= b0 + b1x, for predicting a women's bone density based on her age. The correlation coefficient may or may not be statically significant for the data given. Remember it wouldn't be appropiate to use regression line to make a prediction if the correlation coefficient isn;t statically significant. (y has a "hat" on the top) age 39 51 54 56 67 bone density 355 349 347 315 313 Find the estimated slope. Rund your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine the value of the dependent variable y at x+ 0 (y has a "hat" onthe top) Find the estimated value of y when x = 51. Round your answer to three decimal places. Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the valueof the…
A particular professor has noticed that the number of people, P, who complain about his attitude is dependent on the number of cups of coffee, n, he drinks. From eight days of tracking he compiled the following data: People (P) 10 10 10 9 6 7 6 4 Cups of coffee (n) 1 2 2 3 4 4 5 5 Find the correlation coefficientr = Interpret the meaning of the slope of your formula in the context of the problem Use your model to predict the number of people that will complain about his attitude if he drinks 9 cups of coffee. How many cups of coffee should he drink so that no one will complain about his attitude? It is ok to round to one decimal place.
The following data represent the number of flash drives sold per day at a localcomputer shop and their prices.Price Units Sold34 336 432 635 530 938 240 1a. Develop the estimated regression equation that could be used to predict thequantity sold given the price. Interpret the slope.b. Did the estimated regression equation provide a good fit? Explain.c. Compute the sample correlation coefficient between the price and the number offlash drives sold. Use a= 0.01 to test the relationship between price and units sold.d. How many units can be sold per day if the price of flash drive is set to $28.
Use the given data to find the scatter plot, equation of the regression line, and prediction. 4) Managers rate employees according to job performance and attitude. The results for several randomly selected employees are given below. Performance 59 63 65 69 58 77 76 | 69 70 64 Attitude 72 67 78 82 75 87 92 83 87 78 What function do you use to see the scatterplot on the calculator. Scatterplot from the calculator:
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
For major league baseball teams, is there a relationship between player payrolls and gate money? Here are data for each of the American League teams for the year 2000. The variable x denotes the 2000 player payroll (in millions of dollars), and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the Figure 1 scatter plot. Also given is the product of the player payroll and the mean attendance for each of the fourteen teams. (These products, written in the column labelled "xy", may aid in calculations.) Anaheim Baltimore Boston Chicago White Sox Cleveland Detroit Kansas City Minnesota Mean Player payroll, x attendance, y (in $1,000,000s) thousands) New York Yankees Oakland Seattle Tampa Bay Texas Toronto Send data to calculator 59.2 80.5 97.0 42.3 90.5 68.6 31.8 23.5 114.3 43.0 69.9 65.2 72.7 66.8 25.56 40.74 31.98 24.07 42.72 31.23 20.74 13.09 39.88 21.36 38.89 18.27 34.57 22.47 xy x 1513.152 3279.57 3102.06…
Select the most appropriate response. If the correlation between a person’s age and annual income is 0.60, then the coefficient of determination tells us that: 36% of the variation in a person’s annual income can be explained by the predictor variable age. 36% of a person’s annual income can be explained by their age 60% of the variation in a person’s annual income can be explained by the predictor variable age 60% of a person’s annual income can be explained by their age
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For 39 nations, a correlation of 0.887 was found between y = Internet use (%) and x = gross domestic product (GDP, in thousands of dollars per capita). The regression equation is y = -3.68 + 1.73x. Complete parts (a) through (c). a. Based on the correlation value, the slope had to be positive. Why? A. The slope and correlation are positive because gross domestic product could not be negative. B. The correlation and the slope are positive because the y-intercept is negative. C. That is a very unusual fact, because the slope and correlation usually have different signs. D. Although slope and correlation usually have different values, they always have the same sign.

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