A company wants to assess the impact of R & D expenditure onits annual profit. The following table presents the information for the last eightyears.Years19911992 | 199319941995199619971998R & D expenditure- . "000) :9.751043Annual profit"000):4542416030342520Estimate the regression and predict the annual profit for 2002 for an allocatedsum of Rs. 100,000 as R & D expenditure.

Answered: A company wants to assess the impact of…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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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?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
For a linear regression for a sample of n=20 pairs of X and Y values. What is the value of the degrees of freedom for the predicted portion of the Y-score variance, MSregression?
A regression of average weekly earnings (AWE, measured in dollars) on age (measured in age) using a random sample of college-educated full-time workers aged 25-65 yields the following
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 1. Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is 8.6 cars. 2. Compute the coefficient of determination and interpret its meaning. The coefficient of determination is 0.234. 3. Do the sample data provide evidence that the model is useful for predicting average monthly sales for employees based on their sales experience using α=0.05? The test statistic is (Type an integer or decimal rounded to two decimal places as…
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on the employee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 Predict the sales next month for an employee with 2.5 years of experience. The predicted sales is _________ cars. (Type an integer or decimal rounded to one decimal place as needed.)
A random sample of 8 office staffs hired within the previous year was selected from a large corporation. For each selected office staff, his or her experience (in months) at the time of hire and starting salary were recorded. The data is given in the table below. Experience (in months) 13 6 8 10 20 7 9 15 Starting Salary (in thousand pesos) 20 14 16 19 21 12 13 21 Determine the following: regression equation sum of squares for error standard error coefficient of determination
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)
A research department of an American automobile company wants to develop a model topredict gasoline mileage (measured in MPG) of the company’s vehicles by using theirhorsepower and weights (measured in pounds). To do this, it took a random sample of 50vehicles to perform a regression analysis as follows: SUMMARYOUTPUTRegression StatisticsMultiple R 0.865689R Square 0.749417Adjusted RSquare 0.738754Standard Error 4.176602Observations 50ANOVAdf SS MS FRegression a 2451.973702 1225.987 dResidual b 819.8680976 cTotal 49 3271.8418CoefficientsStandardError t StatIntercept 58.15708 2.658248208 21.87797Horsepower -0.11753 0.032643428 -3.60028Weight -0.00687 0.001401173 -4.90349(a) State the multiple regression equation. Interpret the meanings of the coefficients forhorsepower and weight.(b) Test the validity of this multiple regression equation at the significance level of 1%. Showyour reasoning.(c) The research department claims that the weight of the vehicle is negatively linearly related…
It was proposed that a study be conducted on the number of start- ups and annual number of business bankruptcies. Data collected was six year period. Develop regression equation and forecast the bankruptcy numbers given the start ups for the forthcoming year to be 60. Interpret the significance of predictor through R Square Business Bankruptcies Start ups (in 1000) 34.3 58.1 35 55.4 38.5 57 40.1 58.5 35.5 57.4 37.9 58
The Update to the Task Force Report on Blood Pressure Control in Children [12] reported the observed 90th per-centile of SBP in single years of age from age 1 to 17 based on prior studies. The data for boys of average height are given in Table 11.18. Suppose we seek a more efficient way to display the data and choose linear regression to accomplish this task. age sbp 1 99 2 102 3 105 4 107 5 108 6 110 7 111 8 112 9 114 10 115 11 117 12 120 13 122 14 125 15 127 16 130 17 132 Do you think the linear regression provides a good fit to the data? Why or why not? Use residual analysis to justify your answer. Am I supposed to run a residual plot and QQ-plot for this question?
Find the correlation coefficient (round to four decimal places) between the year of construction and the price sold at auction and then interpret the direction and strength of the correlation. Find the Least-Squares Regression Line (round to four decimal places throughout.)

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