...Nonparametric Hypothesis Test:. Research can be focused in a number of ways, to help refine our topic to a point where we have clear hypotheses statements. If the housing industry was determined to be doing better than the rest of the economy, a hypothesis test might be in order, with mean prices greater than other housing industries. The test to determine the difference is the one sample run (Wald- Wolfowitz test to determine the mean prices to be equal to each home cost (Doane & Seward, 2007). However, March home sales were higher than expected. Our Presidents recent trip aboard has secured enough raw material and additional energy products to secure the USA zone of growth for 100 years. We believe America remains at the top of the economic world and by far the most secure in the housing industry recovery (Dohrmann B, 2011). More often than not, homeowners has maximize their time and search for the best sales for homes by searching information about homes sales and choices(Rosales L. 2011). The housing industry can also find the best buyer’s options that are available to him or her in a similar way that real estate agencies can seek the best sales on homes. However, the homes of the homeowners in the housing industry are set by the regional, state, and local expectations of the buyers and not so much by differences in qualifications of the homeowners. There are still a bright light in our housing futures across the nation (Rosales L, 2011) When the housing industry...
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...empec, Vol. 13, 1988, page 223-249 Nonparametric Estimation and Hypothesis Testing in Econometric Models By A. Ullah ~ Abstract: In this paper we systematically review and develop nonparametric estimation and testing techniques in the context of econometric models. The results are discussed under the settings of regression model and kernel estimation, although as indicated in the paper these results can go through for other econometric models and for the nearest neighbor estimation. A nontechnical survey of the asymptotic properties of kernel regression estimation is also presented. The technique described in the paper are useful for the empirical analysis of the economic relations whose true functional forms are usually unknown. 1 Introduction Consider an economic model y =R(x)+u where y is a dependent variable, x is a vector o f regressors, u is the disturbance and R(x) = E ( y l x ) . Often, in practice, the estimation o f the derivatives o f R(x)are o f interest. For example, the first derivative indicates the response coefficient (regression coefficient) o f y with respect to x, and the second derivauve indicates the curvature o f R(x). In the parametric econometrics the estimation o f these derivatives and testing 1 Aman Ullah, Department of Economics, University of Western Ontario, London, Ontario, N6A 5C2, Canada. I thank L Ahmad, A. Bera, A. Pagan, C. Robinson, A. Zellner, and the participants of the workshops at the Universities of Chicago...
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...Last week, we found that average performance ratings do not differ between males and females in the population. Now we need to see if they differ among the grades. Is the average performace rating the same for all grades? (Assume variances are equal across the grades for this ANOVA.) You can use these columns to place grade Perf Ratings if desired. A B C D E F Null Hypothesis: Alt. Hypothesis: Place B17 in Outcome range box. Interpretation: What is the p-value: Is P-value < 0.05? Do we REJ or Not reject the null? If the null hypothesis was rejected, what is the effect size value (eta squared): Meaning of effect size measure: What does that decision mean in terms of our equal pay question: 2 While it appears that average salaries per each grade differ, we need to test this assumption. Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.) Use the input table to the right to list salaries under each grade level. Null Hypothesis: If desired, place salaries per grade in these columns Alt. Hypothesis: A B C D E F Place B55 in Outcome range box. What is the p-value: Is...
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...Last week, we found that average performance ratings do not differ between males and females in the population. Now we need to see if they differ among the grades. Is the average performace rating the same for all grades? (Assume variances are equal across the grades for this ANOVA.) You can use these columns to place grade Perf Ratings if desired. A B C D E F Null Hypothesis: Alt. Hypothesis: Place B17 in Outcome range box. Interpretation: What is the p-value: Is P-value < 0.05? Do we REJ or Not reject the null? If the null hypothesis was rejected, what is the effect size value (eta squared): Meaning of effect size measure: What does that decision mean in terms of our equal pay question: 2 While it appears that average salaries per each grade differ, we need to test this assumption. Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.) Use the input table to the right to list salaries under each grade level. Null Hypothesis: If desired, place salaries per grade in these columns Alt. Hypothesis: A B C D E F Place B55 in Outcome range box. What is the p-value: Is...
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...|[pic] |Syllabus | | |College of Social Sciences | | |PSY/315 Version 2 | | |Statistical Reasoning in Psychology | Copyright © 2010, 2009, 2006 by University of Phoenix. All rights reserved. Course Description This is an introductory course in applied statistics, with particular emphasis in psychology. Both descriptive and inferential statistics are included. In addition, this course provides the basic statistical background and understanding needed. Policies Faculty and students/learners will be held responsible for understanding and adhering to all policies contained within the following two documents: • University policies: You must be logged into the student website to view this document. • Instructor policies: This document is posted in the Course Materials forum. University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently...
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...the mean salary of professors at Metro University is higher than the national average for university professors Incorrect d. Test of whether a change occurred in the likelihood of heart disease among people who switched to a diet high in fish e. Test of differences in ad recall among three experimental groups (each of which saw a different advertisement) and a control group Feedback The correct answer is: Test of the average incomes of magazine subscribers of Southern Living verses Better Homes and Gardens Question 3 Correct Mark 1 out of 1 Not flaggedFlag question Question text Under which of the following conditions must the null hypothesis be rejected? Select one: a. p value < equation Correct b. p value > equation c. p value = equation d. p value cannot be determined e. p value is unrelated to hypothesis testing Feedback The correct answer is: p...
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...to perform the standard exploratory data analysis before commencing any hypothesis testing involving t-tests. It is often useful to check through summary statistics (like the minimum and maximum of the data), as well as a quick plot of the data (box-plots), to check for any problematic data or outliers. The use of a t-test requires the assumption that the data is distributed like a normal distribution – essentially a bell-shaped curve for the histogram. Therefore it is extremely informative to look at the histogram of the data before commencing on testing, as this will indicate whether the use of the t-test is justified. Before commencing any testing, evaluate what are your hypotheses that you are interested in. If you are testing the mean for a single sample, are you testing the mean to be 0, or are you testing the mean against some non-zero value. If so, do remember to change the input in SPSS correspondingly. Similarly if you are testing the means for two samples, are you testing for the difference to be zero, or against a non-zero difference. Essentially, think through your hypotheses before doing any testings! When comparing between two groups, it is absolutely essential to note whether the groups are dependent (and therefore possibly requiring a paired-t test), or independent (and therefore allowing the use of the independent samples t-test). It is often useful to present the p-value of the hypothesis test, as well as the...
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...|[pic] |Course Syllabus | | |College of Natural Sciences | | |MTH/233 Version 2 | | |Statistics | Copyright © 2010, 2006 by University of Phoenix. All rights reserved. Course Description This course surveys descriptive and inferential statistics with an emphasis on practical applications of statistical analysis. The principles of collecting, analyzing, and interpreting data are covered. It examines the role of statistical analysis, statistical terminology, the appropriate use of statistical techniques and interpretation of statistical findings through applications and functions of statistical methods. Policies Faculty and students/learners will be held responsible for understanding and adhering to all policies contained within the following two documents: • University policies: You must be logged into the student website to view this document. • Instructor policies: This document is posted in the Course Materials forum. University policies are subject to...
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...HYPOTHESIS TESTING Using SPSS for Windows 1. INTRODUCTION In order to pass this aspect of your course, you need to understand the "mechanics" of the statistical processes as taught in your Introductory Statistics course, and be able to apply them to the practical example at hand. This means that you have to "think" about what your data means. You have to "think" about suitable analyses for the level of measurement of your variable (nominal, ordinal, interval, or ratio). You then have to be able to interpret the statistical results in the context of the given problem. When requesting SPSS procedures: a. Consider the results that you are expecting, for example, what will be the point of the results attained via a Regression Analysis, or a Means Analysis? Ask if this procedure is the best way of solving your informational need. b. ALWAYS consider the level of measurement of your variable (nominal, ordinal, interval or ratio). There are varying opportunities for statistical analysis, and varying limitations, according to the level of measurement. c. Always do a "reasonableness" check on your results, for example, if you ask SPSS to do a DESCRIPTIVES analysis on the variable, GENDER (Male = 1 Female = 2) it will happily provide you with a Mean of say, 1.586 and a Standard Deviation of say, 0.694. However unless you are dealing with a population of transvestites - this result is silly!!! 2. GENERAL PRINCIPLES OF HYPOTHESIS TESTING The following...
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...The terms "statistical analysis" and "data analysis" can be said to mean the same thing -- the study of how we describe, combine, and make inferences based on numbers. A lot of people are scared of numbers (quantiphobia), but data analysis with statistics has got less to do with numbers, and more to do with rules for arranging them. It even lets you create some of those rules yourself, so instead of looking at it like a lot of memorization, it's best to see it as an extension of the research mentality, something researchers do anyway (i.e., play with or crunch numbers). Once you realize that YOU have complete and total power over how you want to arrange numbers, your fear of them will disappear. It helps, of course, if you know some basic algebra and arithmetic, at a level where you might be comfortable solving the following equation There are three (3) general areas that make up the field of statistics: descriptive statistics, relational statistics, and inferential statistics. 1. Descriptive statistics fall into one of two categories: measures of central tendency (mean, median, and mode) or measures of dispersion (standard deviation and variance). Their purpose is to explore hunches that may have come up during the course of the research process, but most people compute them to look at the normality of their numbers. Examples include descriptive analysis of sex, age, race, social class, and so forth. 2. Relationalstatistics fall into one of three categories: univariate,...
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...here QNT 565 Final Exam (Latest) 3. A population consists of the following data: 7, 11, 12, 18, 20, 22, 25. The population variance is • 36.82 • 22.86 • 5.16 • 6.07 4. If the hypothesis presented is tested using a .05 alpha level and the resulting p value is .01, which of the following interpretations of the results is most appropriate? • As income increases, food expenditures increase. • There is a relationship between income and food expenditures. • As income decreases, the percentage of income spent on food increases. • There is no relationship between income and food expenditures. 5. Paired comparison scales result in _____ data. • ratio • interval • ordinal • nominal To download the Complete Assignment of QNT/565 Class check QNT 565 Entire Course 6. Five homes were recently sold in Oxnard Acres. Four of the homes sold for $400,000 while the fifth home sold for $2.5 million. Which measure of central tendency best represents a typical home price in Oxnard Acres? • The mean or mode. • The midrange or mean. • The median or mode. • The mean or median. 7. An increase in hours of television viewing leads to increases in the sales of snack foods. This is an example of a • causal hypothesis • correlational hypothesis • research question • descriptive hypothesis Want to download the Complete Final Exam Assignment..?? Click...
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...= = = = = = Research and Evaluation I - 341 Syllabus –Rev 6 Note: Please note each week’s individual assignments from this syllabus, find the corresponding chapter (s) and read them along with chapters assigned for reading in the e-text. As far as the assignments to be submitted are concerned, you are only, and only, responsible for the requirements which appear in my syllabus as presented below. |Values for the Course Assignments |Percent | |Individual | |Participation (All Weeks - 2% each) |10 | |DQ’s (All Weeks – 2% each) |10 | |Weekly Summaries (All Weeks - 1% each) |5 | |Business Research Paper (week One) |4 | |Survey (Week Two) |6 | |Assignments from the Text - – Section Exercises (Week Two) ...
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...Hypothes9 9.1 Fundamentals of Hypothesis Testing: One-Sample Tests A Connection Between Confidence Interval Estimation and Hypothesis Testing Can You Ever Know the Population Standard Deviation? USING STATISTICS @ Oxford Cereals, Part II Fundamentals of Hypothesis-Testing Methodology The Null and Alternative Hypotheses The Critical Value of the Test Statistic Regions of Rejection and Nonrejection Risks in Decision Making Using Hypothesis Testing Hypothesis Testing Using the Critical Value Approach Hypothesis Testing Using the p-Value Approach 9.4 Z Test of Hypothesis for the Proportion The Critical Value Approach The p-Value Approach Potential HypothesisTesting Pitfalls and Ethical Issues 9.5 9.2 t Test of Hypothesis for the Mean (S Unknown) The Critical Value Approach The p-Value Approach Checking the Normality Assumption One-Tail Tests The Critical Value Approach The p-Value Approach 9.6 Online Topic: The Power of a Test USING STATISTICS @ Oxford Cereals, Part II Revisited CHAPTER 9 EXCEL GUIDE CHAPTER 9 MINITAB GUIDE 9.3 Learning Objectives In this chapter, you learn: • The basic principles of hypothesis testing • How to use hypothesis testing to test a mean or proportion • The assumptions of each hypothesis-testing procedure, how to evaluate them, and the consequences if they are seriously violated • How to avoid the pitfalls involved in hypothesis testing • Ethical issues involved in hypothesis testing U S I N G S TAT I S T I C S ...
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...University Islamabad E-mail: zulfiqar.shah@gmail.com Muhammad Husnain Ph.D Scholar (Finance) Mohammad Ali Jinnah University Islamabad Email: Husnain_ctn@yahoo.com Abstract Financial economists have continuously questioned the efficient market hypothesis especially in last decade. Major part of discussion is whether the equity markets are efficient and if not then up to what extent one can forecast the meaningful future movement of equity prices. On one side there are believers of random walk and contrary there are followers of chartist theories. Those who negate the random walk suggested that there exist anomalies in the equity markets and hence are not perfectly efficient. The major objective of this study is to check the weak form of efficiency and presence of calendar anomalies in equity markets of developing and developed countries. On the basis of most recent and relatively longer horizon (14 Year) data on daily basis and a range of powerful econometrics this study suggested that in broader sense both of developed and developing equity markets are weak form inefficient. Hence there is no remarkable difference in term of market efficiency in equity markets of developed and developing countries. Hence one can reject the random walk hypothesis and therefore presence of markets efficiency is again a matter of theory not as much practical. Key Word: Weak form efficiency, Random Walk, Calendar Anomalies, EGARCH JEL classification: G12, G14, G15 1 Introduction The role of capital...
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...Journal of Economic Literature Vol. XXXIV (March 1996), pp. 97-114 The Standard Error of Regressions By D E I R D R E N . M C C L O S K E Y and STEPHEN T. ZILIAK University of Iowa Suggestions by two anonymous and patient referees greatly improved the paper. Our thanks also to seminars at Clark, Iowa State, Harvard, Houston, Indiana, and Kansas State universities, at Williatns College, and at the universities of Virginia and Iowa. A colleague at Iowa, Calvin Siehert, was materially helpful. T cant for science or policy and yet be insignificant statistically, ignored by the less thoughtful researchers. In the 1930s Jerzy Neyman and Egon S. Pearson, and then more explicitly Abraham Wald, argued that actual investigations should depend on substantive not merely statistical significance. In 1933 Neyman and Pearson wrote of type I and type II errors: HE IDEA OF Statistical significance is old, as old as Cicero writing on forecasts (Cicero, De Divinatione, 1. xiii. 23). In 1773 Laplace used it to test whether comets came from outside the solar system (Elizabeth Scott 1953, p. 20). The first use of the very word "significance" in a statistical context seems to be John Venn's, in 1888, speaking of differences expressed in units of probable error; Is it more serious to convict an innocent man or to acquit a guilty? That will depend on the consequences of the error; is the punishment death or fine; what is the danger to the community of released...
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