netrashetty
Netra Shetty
DineEquity (NYSE: DIN) is a United States company that both franchises and operates restaurants. Headquartered in Glendale, California, the company was founded originally as IHOP (International House Of Pancakes) and changed its corporate identity after it acquired Applebee's.[1] On July 16, 2007, IHOP Corp. announced its intent to acquire the bar-and-grill chain Applebee's International, Inc. in an all-cash transaction, valued at approximately US$2.1 billion. Under the deal, IHOP paid $25.50 per share for Applebee's. IHOP stated it would franchise most of Applebee's 500 company-owned stores. Applebee's has 1,943 restaurants overall worldwide, including those operated by franchisees.[2]
Julia Stewart, who originally worked as a waitress at IHOP and worked her way up through the restaurant industry, became Chief Executive Officer of IHOP Corp. She had previously been President of Applebee’s, but left after being overlooked for that company's CEO position. She became CEO of IHOP in 2001, and returned to manage her old company due to the acquisition. [3]
With a larger than 70% vote, Applebee's stockholders approved the takeover, which closed on November 29, 2007. The deal beat 26 other offers to purchase the economically slumping Applebee's. A number of executives from Applebee's voted against the offer, the chain's largest individual shareholder, Applebee's director Burton "Skip" Sack plans to take IHOP to court to demand a higher amount of money to be paid to him as the purchasing price that IHOP is giving is unfair to the shareholders of Applebee's. As part of the purchase, a brand re-marketing scheme and revitalization of the Applebee's image was undertaken
arious characteristics of the sample population of interest, including sample size and distribution. The test statistic can assume many numerical values. Since the value of the test statistic has a significant effect on the decision, one must use the appropriate statistic in order to obtain meaningful results. Most test statistics follow this general pattern:
For example, the appropriate statistic to use when testing a hypothesis about a population means is:
In this formula Z = test statistic, Χ̅ = mean of the sample, μ = mean of the population, σ = standard deviation of the sample, and η = number in the sample.
SPECIFYING THE STATISTICAL SIGNIFICANCE SEVEL.
As previously noted, one can reject a null hypothesis or fail to reject a null hypothesis. A null hypothesis that is rejected may, in reality, be true or false. Additionally, a null hypothesis that fails to be rejected may, in reality, be true or false. The outcome that a researcher desires is to reject a false null hypothesis or to fail to reject a true null hypothesis. However, there always is the possibility of rejecting a true hypothesis or failing to reject a false hypothesis.
Rejecting a null hypothesis that is true is called a Type I error and failing to reject a false null hypothesis is called a Type II error. The probability of committing a Type I error is termed α and the probability of committing a Type II error is termed β. As the value of α increases, the probability of committing a Type I error increases. As the value of β increases, the probability of committing a Type II error increases. While one would like to decrease the probability of committing of both types of errors, the reduction of α results in the increase of β and vice versa. The best way to reduce the probability of decreasing both types of error is to increase sample size.
The probability of committing a Type I error, α, is called the level of significance. Before data is collected one must specify a level of significance, or the probability of committing a Type I error (rejecting a true null hypothesis). There is an inverse relationship between a researcher's desire to avoid making a Type I error and the selected value of α; if not making the error is particularly important, a low probability of making the error is sought. The greater the desire is to not reject a true null hypothesis, the lower the selected value of α. In theory, the value of α can be any value between 0 and 1. However, the most common values used in social science research are .05, .01, and .001, which respectively correspond to the levels of 95 percent, 99 percent, and 99.9 percent likelihood that a Type I error is not being made. The tradeoff for choosing a higher level of certainty (significance) is that it will take much stronger statistical evidence to ever reject the null hypothesis.
DETERMINING THE DECISION RULE.
Before data are collected and analyzed it is necessary to determine under what circumstances the null hypothesis will be rejected or fail to be rejected. The decision rule can be stated in terms of the computed test statistic, or in probabilistic terms. The same decision will be reached regardless of which method is chosen.
COLLECTING THE DATA AND PERFORMING THE CALCULATIONS.
The method of data collection is determined early in the research process. Once a research question is determined, one must make decisions regarding what type of data is needed and how the data will be collected. This decision establishes the bases for how the data will be analyzed. One should use only approved research methods for collecting and analyzing data.
DECIDING WHETHER TO REJECT THE NULL HYPOTHESIS.
This step involves the application of the decision rule. The decision rule allows one to reject or fail to reject the null hypothesis. If one rejects the null hypothesis, the alternative hypothesis can be accepted. However, as discussed earlier, if one fails to reject the null he or she can only suggest that the null may be true.
EXAMPLE.
XYZ Corporation is a company that is focused on a stable workforce that has very little turnover. XYZ has been in business for 50 years and has more than 10,000 employees. The company has always promoted the idea that its employees stay with them for a very long time, and it has used the following line in its recruitment brochures: "The average tenure of our employees is 20 years." Since XYZ isn't quite sure if that statement is still true, a random sample of 100 employees is taken and the average age turns out to be 19 years with a standard deviation of 2 years. Can XYZ continue to make its claim, or does it need to make a change?
State the hypotheses.
H 0 = 20 years
H 1 ≠ 20 years
Determine the test statistic. Since we are testing a population mean that is normally distributed, the appropriate test statistic is:
Specify the significance level. Since the firm would like to keep its present message to new recruits, it selects a fairly weak significance level (α = .05). Since this is a two-tailed test, half of the alpha will be assigned to each tail of the distribution. In this situation the critical values of Z = +1.96 and −1.96.
State the decision rule. If the computed value of Z is greater than or equal to +1.96 or less than or equal to −1.96, the null hypothesis is rejected.
Calculations.
Reject or fail to reject the null. Since 2.5 is greater than 1.96, the null is rejected. The mean tenure is not 20 years, therefore XYZ needs to change its statement.
Julia Stewart, who originally worked as a waitress at IHOP and worked her way up through the restaurant industry, became Chief Executive Officer of IHOP Corp. She had previously been President of Applebee’s, but left after being overlooked for that company's CEO position. She became CEO of IHOP in 2001, and returned to manage her old company due to the acquisition. [3]
With a larger than 70% vote, Applebee's stockholders approved the takeover, which closed on November 29, 2007. The deal beat 26 other offers to purchase the economically slumping Applebee's. A number of executives from Applebee's voted against the offer, the chain's largest individual shareholder, Applebee's director Burton "Skip" Sack plans to take IHOP to court to demand a higher amount of money to be paid to him as the purchasing price that IHOP is giving is unfair to the shareholders of Applebee's. As part of the purchase, a brand re-marketing scheme and revitalization of the Applebee's image was undertaken
arious characteristics of the sample population of interest, including sample size and distribution. The test statistic can assume many numerical values. Since the value of the test statistic has a significant effect on the decision, one must use the appropriate statistic in order to obtain meaningful results. Most test statistics follow this general pattern:
For example, the appropriate statistic to use when testing a hypothesis about a population means is:
In this formula Z = test statistic, Χ̅ = mean of the sample, μ = mean of the population, σ = standard deviation of the sample, and η = number in the sample.
SPECIFYING THE STATISTICAL SIGNIFICANCE SEVEL.
As previously noted, one can reject a null hypothesis or fail to reject a null hypothesis. A null hypothesis that is rejected may, in reality, be true or false. Additionally, a null hypothesis that fails to be rejected may, in reality, be true or false. The outcome that a researcher desires is to reject a false null hypothesis or to fail to reject a true null hypothesis. However, there always is the possibility of rejecting a true hypothesis or failing to reject a false hypothesis.
Rejecting a null hypothesis that is true is called a Type I error and failing to reject a false null hypothesis is called a Type II error. The probability of committing a Type I error is termed α and the probability of committing a Type II error is termed β. As the value of α increases, the probability of committing a Type I error increases. As the value of β increases, the probability of committing a Type II error increases. While one would like to decrease the probability of committing of both types of errors, the reduction of α results in the increase of β and vice versa. The best way to reduce the probability of decreasing both types of error is to increase sample size.
The probability of committing a Type I error, α, is called the level of significance. Before data is collected one must specify a level of significance, or the probability of committing a Type I error (rejecting a true null hypothesis). There is an inverse relationship between a researcher's desire to avoid making a Type I error and the selected value of α; if not making the error is particularly important, a low probability of making the error is sought. The greater the desire is to not reject a true null hypothesis, the lower the selected value of α. In theory, the value of α can be any value between 0 and 1. However, the most common values used in social science research are .05, .01, and .001, which respectively correspond to the levels of 95 percent, 99 percent, and 99.9 percent likelihood that a Type I error is not being made. The tradeoff for choosing a higher level of certainty (significance) is that it will take much stronger statistical evidence to ever reject the null hypothesis.
DETERMINING THE DECISION RULE.
Before data are collected and analyzed it is necessary to determine under what circumstances the null hypothesis will be rejected or fail to be rejected. The decision rule can be stated in terms of the computed test statistic, or in probabilistic terms. The same decision will be reached regardless of which method is chosen.
COLLECTING THE DATA AND PERFORMING THE CALCULATIONS.
The method of data collection is determined early in the research process. Once a research question is determined, one must make decisions regarding what type of data is needed and how the data will be collected. This decision establishes the bases for how the data will be analyzed. One should use only approved research methods for collecting and analyzing data.
DECIDING WHETHER TO REJECT THE NULL HYPOTHESIS.
This step involves the application of the decision rule. The decision rule allows one to reject or fail to reject the null hypothesis. If one rejects the null hypothesis, the alternative hypothesis can be accepted. However, as discussed earlier, if one fails to reject the null he or she can only suggest that the null may be true.
EXAMPLE.
XYZ Corporation is a company that is focused on a stable workforce that has very little turnover. XYZ has been in business for 50 years and has more than 10,000 employees. The company has always promoted the idea that its employees stay with them for a very long time, and it has used the following line in its recruitment brochures: "The average tenure of our employees is 20 years." Since XYZ isn't quite sure if that statement is still true, a random sample of 100 employees is taken and the average age turns out to be 19 years with a standard deviation of 2 years. Can XYZ continue to make its claim, or does it need to make a change?
State the hypotheses.
H 0 = 20 years
H 1 ≠ 20 years
Determine the test statistic. Since we are testing a population mean that is normally distributed, the appropriate test statistic is:
Specify the significance level. Since the firm would like to keep its present message to new recruits, it selects a fairly weak significance level (α = .05). Since this is a two-tailed test, half of the alpha will be assigned to each tail of the distribution. In this situation the critical values of Z = +1.96 and −1.96.
State the decision rule. If the computed value of Z is greater than or equal to +1.96 or less than or equal to −1.96, the null hypothesis is rejected.
Calculations.
Reject or fail to reject the null. Since 2.5 is greater than 1.96, the null is rejected. The mean tenure is not 20 years, therefore XYZ needs to change its statement.