Hook Law** ABSTRACT In this paper‚ we examine the relationship between components of government expenditure and economic growth in ASEAN-5 using the autoregressive distributed lag (ARDL) approach developed by Pesaran‚ Shin‚ and Smith. Bound testing approaches to analysis of level relationship‚ and this test suggested that the all variables in functional form framework are bound together in long run. The results also show that there are possible long-run coefficient effects between the variables
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decision analysis (MCDA) technique with inherent families of models (WSM‚ WPM and AHP) that have similar data structure. In this paper‚ we present our online tool and discuss the underlying conceptual framework. Proof of concept is made through hypothesis test using frequency distribution (qualitative)‚ intra-class correlation and Spearman rank correlation approximated to normal distribution. The results on the tests indicate a promising future for the tool. Keywords: Multi-Criteria Decision Analysis;
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another student’s exam? Yes _________ No _________ During your time at Bayview‚ did you ever collaborate with other students on projects that were supposed to be completed individually? Yes _________ No _________ According to the statistical analysis‚ given Figure 1‚ it shows that 53% of the sample have answered “Yes” to at least one of the questions and were considered as cheaters and in which more than half are males. It seems that most of the male cheaters copy their work off the
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people with cholesterol levels over 200. In order to determine if this is a valid claim‚ they hire an independent testing agency‚ which then selects 25 people with a cholesterol level over 200 to eat their cereal for breakfast daily for a month. The agency should be testing the null hypothesis H0: μ = 10 and the alternative hypothesis B 6. Ha: μ >10. Suppose we are testing the null hypothesis H0: μ = 20 and the alternative Ha: μ 20‚ for a normal population with σ = 5. A random sample of 25 observations
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619 is significant as it is P=0.005‚ it can be said that the intervention group participants face a significant reduction in mobility difficulty. 2. State the null hypothesis for the Baird and Sands (2004) study that focuses on the effect of the GI with PMR treatment on patients’ mobility level. Should the null hypothesis be rejected for the difference between the two groups in change in mobility scores over 12 weeks? Provide a rationale for your answer. Ho 1: Guided imagery (GI) with
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Problem N°1 1.Formulate the null and alternative hypotheses. Null Hypothesis: The average (mean) annual income was greater than or equal to $50‚000 H_0: μ≥50000 Alternate Hypothesis: The average (mean) annual income was less than $50‚000. H_a: μ 30 we will use the z-test. As Ha:μ0.40 the‚ test is a right tailed z-test. The critical value for significance level‚ α=0.05 for a right tailed z-test is given in the table as: 1.645. Decision Rule: Reject H_0‚if z>1.645 3. Calculate the test
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satisfaction ratings to provide evidence supporting the claim that the mean composite satisfaction rating for the XYZ-Box exceeds 42. a. Letting represent the mean composite satisfaction rating for the XYZ-Box‚ set up the null hypothesis and the alternative hypothesis needed if we wish to attempt to provide evidence supporting the claim that exceeds 42. b. The random sample of 65 satisfaction ratings yields a sample mean of x = 42.954. Assuming that equals to 2.65‚ use critical values
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collected and the way in which the data are collected affect the possibility of a Type I or Type II error? According to Neutens‚ J. J.‚ & Rubinson‚ L. (2010) the key to most significance testing is to establish the extent to which the null hypothesis is believed to be true. The null hypothesis refers to any hypothesis to be nullified and normally presumes chance results only‚ no difference in averages or no correlation between variables. For example‚ if we undertook a study of the effects of consuming
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| | | | | | | 3 | Testing | | | | | Test of Hypothesis | 7.1 | | | | Formulating hypothesis‚ rejection region | 7.2 | 7.1; 7.2; 7.3; 7.4; 7.8; 7.11 | | | Observed significance levels‚ p- value | 7.3 | 7.19; 7.20; 7.21; 7.23; 7.26 | | | Test hypothesis population mean | 7.4 | 7.28; 7.30; 7.31; 7.32 | | | | | | | 4 | Test of Hypothesis population mean‚ t-statistic | 7.5 | 7.48; 7.49; 7.50; 7.51 | | | Test hypothesis population proportion | 7.6
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below and more question in future weeks. Practice Problems: 1) Use your data from above. This week assume that historically the average person takes 3 vitamins on a daily basis. Conduct a hypothesis test analysis to determine if 3 is still the correct average number. Write your hypotheses in correct statistical notation. Finally use the important numbers from your output to explain your results. Use alpha = 0.05. Post only the relevant numbers‚ not all of the output; then explain your results
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