Modern Business Statistics with Microsoft Excel (MindTap Course List)
5th Edition
ISBN: 9781285433301
Author: David R. Anderson, Dennis J. Sweeney, Thomas A. Williams
Publisher: Cengage Learning
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Chapter 16.6, Problem 26E
The following data show the daily closing prices (in dollars per share) for a stock.
- a. Define the independent variable Period, where Period = 1 corresponds to the data for November 3, Period = 2 corresponds to the data for November 4, and so on. Develop the estimated regression equation that can be used to predict the closing price given the value of Period.
- b. At the .05 level of significance, test for any positive autocorrelation in the data.
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Consider the following regression equation representing the linear relationship between the Canada Child Benefit provided for a married couple with 3 children under the age of 6, based on their annual family net income:
ŷ =121.09−0.57246xR2=0.894
where y = annual Canada Child Benefit paid (in $100s) x = net annual family income (in $1000s)
Source: Canada Revenue Agency
a. As the net annual family income increases, does the Canada Child Benefit paid increase or decrease? Based on this, is the correlation between the two variables positive or negative?The Canada Child Benefit paid
.The correlation between the two variables is
.b. Calculate the correlation coefficient and determine if the relationship between the two variables is strong, moderate or weak.r=
, the relationship is
.
Round to 3 decimal places
c. Interpret the value of the slope as it relates to this relationship.
For every $1 increase in annual family net income, there is a $0.57246 decrease in…
Consider the following regression equation representing the linear relationship between the Canada Child Benefit provided for a married couple with 3 children under the age of 6, based on their annual family net income:
ŷ =121.09−0.57246xR2=0.894
where y= annual Canada Child Benefit paid (in $100s) x = net annual family income (in $1000s)
Source: Canada Revenue Agency
a. As the net annual family income increases, does the Canada Child Benefit paid increase or decrease? Based on this, is the correlation between the two variables positive or negative?The Canada Child Benefit paid ?
.The correlation between the two variables is ?
.b. Calculate the correlation coefficient and determine if the relationship between the two variables is strong, moderate or weak.r=
, the relationship is ?
.
Round to 3 decimal places
c. Interpret the value of the slope as it relates to this relationship.
For every $1 increase in annual family net income, there is a $0.57246 decrease in…
Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married:
1=married,
0=otherwise),
a dummy variable for head of household (Head:
1=yes,
0=no)
and a dummy variable for management position (Manager:
1=yes,
0=no).
We shall call this Model 1. The coefficient of partial determination
(R2Yj.(All variables except j))
of each of the six predictors are, respectively, 0.2807, 0.0386, 0.0317, 0.0141, 0.0958, and 0.1201. Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager. The results of the regression analysis are given. Refer to model 1. Which of the following is the correct null hypothesis to test…
Chapter 16 Solutions
Modern Business Statistics with Microsoft Excel (MindTap Course List)
Ch. 16.1 - Consider the following data for two variables, x...Ch. 16.1 - Consider the following data for two variables, x...Ch. 16.1 - Prob. 3ECh. 16.1 - A highway department is studying the relationship...Ch. 16.1 - In working further with the problem of exercise 4,...Ch. 16.1 - A study of emergency service facilities...Ch. 16.1 - Prob. 7ECh. 16.1 - Prob. 8ECh. 16.1 - Prob. 9ECh. 16.2 - In a regression analysis involving 27...
Ch. 16.2 - Prob. 11ECh. 16.2 - The Professional Golfers’ Association of America...Ch. 16.2 - Refer to exercise 12.
Develop an estimated...Ch. 16.2 - A 10-year study conducted by the American Heart...Ch. 16.2 - Prob. 15ECh. 16.5 - Prob. 19ECh. 16.5 - Prob. 20ECh. 16.5 - Prob. 21ECh. 16.5 - Prob. 22ECh. 16.5 - Prob. 23ECh. 16.5 - Prob. 24ECh. 16.5 - Prob. 25ECh. 16.6 - The following data show the daily closing prices...Ch. 16.6 - Refer to the Cravens data set in Table 16.5. In...Ch. 16 - A sample containing years to maturity and yield...Ch. 16 - Consumer Reports tested 19 different brands and...Ch. 16 - A study investigated the relationship between...Ch. 16 - Refer to the data in exercise 28. Consider a model...Ch. 16 - Refer to the data in exercise 28.
Develop an...Ch. 16 - Prob. 33SECh. 16 - The Ladies Professional Golf Association (LPGA)...Ch. 16 - Wine Spectator magazine contains articles and...
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- For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracyarrow_forwardFor the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.arrow_forwardFor the following exercises, consider the data in Table 5, which shows the percent of unemployed ina city of people 25 years or older who are college graduates is given below, by year. 40. Based on the set of data given in Table 6, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient to three decimal places.arrow_forward
- 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?arrow_forwardFor the following exercises, consider this scenario: The profit of a company decreased steadily overa ten-year spam.The following ordered pairs shows dollars and the number of units sold in hundreds and the profit in thousands ofover the ten-year span, (number of units sold, profit) for specific recorded years: (46,600),(48,550),(50,505),(52,540),(54,495). Use linear regression to determine a function Pwhere the profit in thousands of dollars depends onthe number of units sold in hundreds.arrow_forward2) The following results are an autoregression for US Exports to Mexico where the dependent variable is the lagged value of US Exports. Based on these regression results, what is your forecast of US Exports to Mexico for March 2005?arrow_forward
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