Acceptance Or Rejection Of The Null Hypothesis Economics Essay

Acceptance Or Rejection Of The Null Hypothesis Economics Essay The appropriate value of t is 2.100. Since we are concerned whether b (the slope of original regression line) is significantly different fro B (the hypothesized slope of population regression), this is a two tailed test, and the critical values are  ±2.100. The standardized regression coefficient is 0.063, which is inside the acceptance region for our hypothesis test. Therefore, we accept null hypothesis that B is equal to 0.01. Step 6: Interpretation of the Result There is not enough difference between b and 0.01 for us to conclude that that B has changed from its historical value. Because of this, we feel that a one hundred percent increase in inflation would increase the poverty headcount by around 0.01%, as it has in the past. 2. Inflation and Ginni Coefficient The slope for the regression line that shows a relationship between inflation and gini coefficient is 0.5956. This means that a 100% increase in inflation would result in 0.5956% increase in gini coefficient. Now we wou ld perform the same hypothesis testing procedure to determine the authenticity of slope and whether the slope justifies the relationship between inflation and gini coefficient. Step 1: State the Null and the Alternative Hypothesis Let B denotes the hypothesized slope of actual regression line, the value of the actual slope of regression line is b = 0.5956. The first step is to find some value for B to compare with b= 0.5956. Suppose that over an extended past period of time, the slope of the relationship between inflation and gini coefficient was 0.5. To test whether this is still the case, we could define the hypothesis as: H0: B= 0.50 (Null hypothesis) H1: B à ¢Ã¢â‚¬ °Ã‚   0.50 (Alternative hypothesis) Step 2: Decide on Significance Level and Degree of Freedom Significance level ÃŽÂ ± = 0.05 and Degree of freedom (df) = n-2 = 19 – 2 = 17 Step 3: Find out Standard Error of b Where Sb = standard error of the regression coefficient Se = standard error of estimate Xi = valu es of the independent variable X-Bar = mean of the values of the independent variable n = number of the data points Year X Y X – X-Bar (X-X-Bar)2 Y2 XY 1963-64 4.19 38.6 -2.607368 6.79837008 1489.96 161.734 1966-67 8.58 35.5 1.7826316 3.17777535 1260.25 304.59 1968-69 1.58 33.6 -5.217368 27.2209332 1128.96 53.088 1969-70 4.12 33.6 -2.677368 7.16830166 1128.96 138.432 1970-71 5.71 33 -1.087368 1.18237008 1089 188.43 1971-72 4.69 34.5 -2.107368 4.44100166 1190.25 161.805 1979-80 8.33 37.3 1.5326316 2.34895956 1391.29 310.709 1984-85 5.67 36.9 -1.127368 1.27095956 1361.61 209.223 1985-86 4.35 35.5 -2.447368 5.98961219 1260.25 154.425 1986-87 3.6 34.6 -3.197368 10.2231648 1197.16 124.56 1987-88 6.29 34.8 -0.507368 0.25742271 1211.04 218.892 1990-91 12.66 40.7 5.8626316 34.370449 1656.49 515.262 1992-93 9.83 41 3.0326316 9.19685429 1681 403.03 1993-94 11.27 40 4.4726316 20.0044332 1600 450.8 1996-97 11.8 40 5.0026316 25.0263227 1600 472 1998-99 5.74 41 -1.057368 1.11802798 1681 23 5.34 2001-02 3.54 27.52 -3.257368 10.610449 757.3504 97.4208 2004-05 9.28 29.76 2.4826316 6.16345956 885.6576 276.1728 2005-06 7.92 30.18 1.1226316 1.26030166 910.8324 239.0256 Summation 129.15 678.06 0 177.829168 24481.06 4714.9392 X-Bar = 6.79 Y-Bar = 35.68 Se = 3.59 By putting Se and Summation (X-X-Bar) 2 in Sb, we have Sb = 0.269 Step 4: Find the Standardized Value of b t = b – BH0/Sb Where b = slope of fitted regression BH0 = actual hypothesized slope Sb = standard error of the regression coefficient By putting the values of the above in t, we have t = 0.355 Step 5: Conclusion on Acceptance or Rejection of the Null Hypothesis The appropriate value of t is 2.10. Since we are concerned whether b (the slope of original regression line) is significantly different from B (the hypothesized slope of population regression), this is a two tailed test, and the critical values are  ±2.10. The standardized regression coefficient is 0.355, which is inside the acceptance region for our hypothesis test. Therefore, we accept null hypothesis that B is equal to 0.5 Step 6: Interpretation of the Result There is not enough difference between b and 0.50 for us to conclude that that B has changed from its historical value. Because of this, we feel that a one hundred percent increase in inflation would result in an increase of 0.50% in gini coefficient, as it has in the past.

Trends in Terrorism Essay Example | Topics and Well Written Essays - 750 words

Trends in Terrorism - Essay Example Tsunamis, hurricanes, and typhoons are just a few natural disasters that mankind faces from time to time (Kapur & Smith, 2010). It is up to different individuals to set the stage for some of them to be tackled. This paper will examine one such disaster, and some of the communication problems agencies might have faced when it came down to handling the situation. Hurricane Katrina, to some, may have been the worst hurricane to ever hit the Atlantic. It is estimated to have killed over 1, 800 people, and the damage assessed to over 80 billion U.S dollars. Communication failure might have worked to increase the damage in terms of lives lost and the property destroyed. The Federal Emergency Management Agency (FEMA) director at the time was forced to resign because of the actions or lack of actions, to warn of the failure of the levee system to drain the flood water. The New Orleans Police Department also had to let go of the Superintendent after investigations were conducted into the fail ure of the levee system in the area (Milakovich & Gordon, 2011). Accurate weather tracking were provided by the U.S Coast Guard and the National Hurricane Center. However, none of these agencies were provided with sufficient knowledge about the levee system in the areas affected, and there was no way the citizens in the area could have automatically known of their fate. During the impact of Katrina, a lot of areas needed health information. Unfortunately, as the devastation of the hurricane continued, it was crucial for the agencies present to change their strategies to those of drown prevention and protection against electrical threats (Izard & Perkins, 2011). The fire departments in some of the states affected were destroyed completely, making the rescue efforts harder for the local agencies and some of the personnel to reach the affected areas. Communication became a problem due to the loss of information centers in these regions. There were reports of department personnel from s ome of the agencies abandoning their posts during the storm. There would have been no definite channel of communication between the different departments and agencies that were present during the evacuation and rescue operations. The destroyed communication infrastructure disabled rescue attempts as there could no longer be any coordination of response teams. There could be no access to the police and fire dispatch centers present in the affected areas, and no public safety radio system was able to operate adequately. In one incident, a senior state official was reported as saying there was no channel of communication and people were writing messages on paper, putting them in bottles, and throwing them in the water for people on the ground (Milakovich & Gordon, 2011). The inadequacy and inefficiency of response teams had consequences on the lives and property of the individuals in the affected areas. The economic, social, and even political scene was changing as people were quick to point fingers in every direction. In terms of repairs for some of the damages, the administration at the time sought for over $100 billion to start some of the repairs. Land was destroyed in the aftermath of Katrina, for example in Mississippi; acres of forest land were destroyed. The redistribution of people changed the social scene drastically

Abacus Business Solution Case Study Example | Topics and Well Written Essays - 1750 words

Abacus Business Solution - Case Study Example Industry Definition Point of Sale is the checkout place where a transaction is completed. In other words, it is the point where customers make payments for the goods or services they have purchased. POS systems are usually used by retail industry and restaurant industry. However, in this context, the study will focus only on the restaurant industry. According to David Gilbert, COO of the National Restaurant Association, POS systems are one of the most essential tools for a restaurant as it helps to make their business operation secure, fast and reliable. POS systems are simply the synthesis of hardware and software and business model of the companies belonging to POS industry is also simple. In general, the supply chain model of the POS industry includes POS manufacturers and developers, POS distributors, POS VARs and business owners. Figure 1 –POS Supply Chain Model Some of the major players of POS industry are Micros Systems, Inc., Restaurant Data Concepts and NCR among others. The industry is characterised by variety in product features, services and prices. According to the reports published by IBIS World, the industry reached a value of $1.2 billion in the year 2012. The industry growth rate is 1.8 % annually, but it is expected that, the growth rate will be 2% for the next five years. The major customers of this industry include hospitality sector, retail sectors, and foodservices sector. The share of revenues of POS industry is presented below. Figure 2 – Share of Revenue in POS Industry