please answer quickly 23An accountant monitors the number of items produced per month by a company, togetherwith the total production cost (RM). Table Q3 shows the data which is used to develop asimple regression model.Table Q3: Number of items and production costNumber of items (000)Production costs (000')2140393848672445727589961535375362838110212355675a)Sketch a scatter plot for the data.b)(i)Find the estimated regression line by using the least square method.(ii)Interpret the result in Q3(b)(i).(c)Predict the production cost when the number of item is 74000.

Solution-: Let, X=Number of items (000') and Y=Production costs (000') We have following table:…

Answered: 23 An accountant monitors the number of…

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter5: Inner Product Spaces

Section5.CR: Review Exercises

Problem 62CR

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
A 10-year study conducted by the American Heart Association provided data on how age, blood pressure, and smoking related to the risk of strokes. The data file “Stroke.xslx” includes a portion of the data from the study. The variable “Risk of Stroke” is measured as the percentage of risk (proportion times 100) that a person will have a stroke over the next 10-year period. Regression Analysis As Image: 1) Based on the simple regression analysis output, write the estimated regression equation. 2) What is the correlation coefficient between Risk of Stroke and Age? How do you find i
Interpret the least squares regression line of this data set. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The correct least squares regression line for the data set is: y = 8.116x + 273.273 Use it to complete the following sentence: The least squares regression line predicts an additional annual rainfall if the average temperature of coastal waters increases by one degree millimetres of Celsius.
when 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?
Show the best fitted line on scatter diagram and Find the predicted value for each y using the exposure time and the equation obtained in part b (b. Find the equation of regression line between radiation doses on exposure time .usingleast square method)
The age and height (in cm) of 400 adult women from Bolivia were measured. A researcher wants to know if age has any effect on height. A linear regression is carried out in Minitab and the following output obtained. Coefficients Term Constant Age (a) Write down the regression model. (b) Interpret the regression coefficient for the fitted model. (c) Use the output from Minitab to explain if the age of a participant affects their height. Percent (d) The normal probability plot of the residuals from this regression model is given below. Do the assumptions of the regression model seem reasonable? Justify your answer. 99.9 8 28 22299229 88 Coef SE Coef 152.94 7.69 0.022 0.231 01 -100 T-Value P-Value VIF 19.90 0.000 0.10 0.924 1.00 -50 Normal Probability Plot (response is Height) 0 Residual 50 ***** 100 150
Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetres
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last seven years. Construc and interpret a 90% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,542 thousand barrels per day. The equation of the regression line is y = - 1.248x + 16,547.218. Oil produced, x Oil imported, y 5,759 9,307 5,724 9,146 5.642 9,611 5,482 10,011 5,199 10,185 5,092 10,116 5,052 10,088 2 Construct and interpret a 90% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,542 thousand barrels per day. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to two decimal places as needed.) O A. There is a 90% chance that the predicted amount of oil imported is between and thousand barrels…
(15 Pt) 10. Jamie made the plot for her car showing the miles she drove on nine trips and the gas she uses. Gas Usage Distance Driven (Miles) 1 500 450 400 350 300 250 200 150 100 50 0 0 2 4 6 Gas Used (Gallons) 8 y = 35.475x + 32.893 10 ^ The equation for the regression line is y=32.893+35.475x. Jamie's car has 2 gallons of gas left. Estimate how far she can drive before running out of gas. Good Luck! 12
It is one question on two page. Please do it
he scatter plot below shows the average cost of a patient staying one day in a hospital (with no surgery) in a sample of years between 2000 and 2015. The least squares regression line modeling this data is given by yˆ=−4815+3.765x. A scatterplot has a horizontal axis labeled Year from 2005 to 2015 in increments of 5 and a vertical axis labeled Price ($) from 2660 to 2780 in increments of 20. The following points are plotted: (2003, 2736); (2004, 2715); (2007, 2675); (2009, 2719); (2013, 270). All coordinates are approximate. Interpret the slope of the least squares regression line. Select the correct answer below: The average cost of a day in a hospital decreased by $3.765 each year between 2000 and 2015. The average cost of a day in a hospital increased by $3.765 each year between 2000 and 2015. The average cost of a day in a hospital decreased by $4815 each year between 2000 and 2015. The average cost of a day in a hospital increased by…
Compute the least-square regression line for predicting the president's age from the first lady's age. Round the slope and -intercept values to at least four decimal places.

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