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Question: Company donations to charity. The amount a company donates to a charitable organization is often restricted by financial inflexibility at the firm. One measure of financial inflexibility is the ratio of restricted assets to total firm assets. A study published in the Journal of Management Accounting Research (Vol. 27, 2015) investigated the link between donation amount and this ratio. Data were collected on donations to 115,333 charities over a recent 10-year period, resulting in a sample of 419,225 firm-years. The researchers fit the quadratic model,E(y)=β0+β1x+β2x2, where y = natural logarithm of total donations to charity by a firm in a year and x = ratio of restricted assets to the firm’s total assets in the previous year. [Note: This model is a simplified version of the actual model fit by the researchers.]

  1. The researchers’ theory is that as a firm’s restricted assets increase, donations will initially increase. However, there is a point at which donations will not only diminish, but also decline as restricted assets increase. How should the researchers use the model to test this theory?
  2. The results of the multiple regression are shown in the table below. Use this information to test the researchers’ theory at. What do you conclude?

Short Answer

Expert verified

Answer:

  1. The researchers’ theory that as a firm’s restricted assets increase, donations will initially increase, and after a point, donations will decline as restricted assets increase. This relationship can be tested by drawing a scatterplot for the data. If the researchers’ theory is right, then the curve will slope upwards initially and then after a point, it will start moving downwards and the slope will become negative.
  2. At 95% confidence level, β2=0. This means that the parabola doesn’t have a curvature and it essentially is a straight line.

Step by step solution

01

Testing the curvilinear relationship

The researchers’ theory is that as a firm’s restricted assets increase, donations will initially increase and after a point donation will decline as restricted assets increase. This relationship can be tested by drawing a scatterplot for the data. If the researchers’ theory is right, then the curve will slope upwards initially and then after a point, it will start moving downwards and the slope will become negative.

02

Significance of β2

To test the curvilinear relation amongst the variables, the significance ofβ2is tested.

Here,H0:β2=0,whilelocalid="1649839803799" Ha:β20

Here, t-test statistic

=β2^sβ2=-0.2790.039=-7.1538

Value of t0.025,419224is 1.96

H0is rejected if t statistic >t0.035,385. For α=0.05, since t<t0.05,199

Not sufficient evidence to reject at 95% confidence interval.

Therefore,β2=0

This means that the parabola doesn’t have a curvature and it essentially is a straight line.

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Most popular questions from this chapter

Question: Bordeaux wine sold at auction. The uncertainty of the weather during the growing season, the phenomenon that wine tastes better with age, and the fact that some vineyards produce better wines than others encourage speculation concerning the value of a case of wine produced by a certain vineyard during a certain year (or vintage). The publishers of a newsletter titled Liquid Assets: The International Guide to Fine Wine discussed a multiple regression approach to predicting the London auction price of red Bordeaux wine. The natural logarithm of the price y (in dollars) of a case containing a dozen bottles of red wine was modelled as a function of weather during growing season and age of vintage. Consider the multiple regression results for hypothetical data collected for 30 vintages (years) shown below.

  1. Conduct a t-test (atα=0.05 ) for each of the βparameters in the model. Interpret the results.
  2. When the natural log of y is used as a dependent variable, the antilogarithm of a b coefficient minus 1—that is ebi - 1—represents the percentage change in y for every 1-unit increase in the associated x-value. Use this information to interpret each of the b estimates.
  3. Interpret the values of R2and s. Do you recommend using the model for predicting Bordeaux wine prices? Explain

Write a model relating E(y) to one qualitative independent variable that is at four levels. Define all the terms in your model.

The first-order model E(y)=β0+β1x1was fit to n = 19 data points. A residual plot for the model is provided below. Is the need for a quadratic term in the model evident from the residual plot? Explain.


Question: Refer to Exercise 12.82.

a. Write a complete second-order model that relates E(y) to the quantitative variable.

b. Add the main effect terms for the qualitative variable (at three levels) to the model of part a.

c. Add terms to the model of part b to allow for interaction between the quantitative and qualitative independent variables.

d. Under what circumstances will the response curves of the model have the same shape but different y-intercepts?

e. Under what circumstances will the response curves of the model be parallel lines?

f. Under what circumstances will the response curves of the model be identical?

Assertiveness and leadership. Management professors at Columbia University examined the relationship between assertiveness and leadership (Journal of Personality and Social Psychology, February 2007). The sample represented 388 people enrolled in a full-time MBA program. Based on answers to a questionnaire, the researchers measured two variables for each subject: assertiveness score (x) and leadership ability score (y). A quadratic regression model was fit to the data, with the following results:

a. Conduct a test of overall model utility. Useα=0.05 .

b. The researchers hypothesized that leadership ability increases at a decreasing rate with assertiveness. Set up the null and alternative hypotheses to test this theory.

  1. Use the reported results to conduct the test, part b. Give your conclusion(atα=0.05 )in the words of the problem.
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