...the null hypothesis is that intercept differences between family groups are allowed but no difference in slope coefficients, versus an alternative hypothesis where also slopes are allowed to differ between family groups. What do you conclude?Perform a Chow test where the null hypothesis is that intercept differences between family groups are allowed but no difference in slope coefficients, versus an alternative hypothesis where also slopes are allowed to differ between family groups. What do you conclude?Perform a Chow test where the null hypothesis is that intercept differences between family groups are allowed but no difference in slope coefficients, versus an alternative hypothesis where also slopes are allowed to differ between family groups. What do you conclude?Perform a Chow test where the null hypothesis is that intercept differences between family groups are allowed but no difference in slope coefficients, versus an alternative hypothesis where also slopes are allowed to differ between family groups. What do you conclude?Perform a Chow test where the null hypothesis is that intercept differences between family groups are allowed but no difference in slope coefficients, versus an alternative hypothesis where also slopes are allowed to differ between family groups. What do you conclude?Perform a Chow test where the null hypothesis is that intercept differences between family groups are allowed but no difference in slope coefficients, versus an alternative hypothesis where...
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...decide whether to accept or eject a hypothesis after evaluating the sample. Statistical hypothesis Hypothesis is a theory, claim or assertion about a particular parameter of a population. It needs to be proven true. Once proven true, it is accepted; otherwise, it is rejected. Types of hypothesis: 1. Null hypothesis (Ho) is always one of status quo or no difference. Example: Ho : The mean fill per box of cereal is 368 grams. (μ = 368) 2. Alternative hypothesis (H1) is the opposite of the null hypothesis (Ho). it is the statement of difference Example: H1: The mean fill per box is not 368 grams. (μ ≠ 368) A summary of the null and alternative hypothesis is presented below: The following two key points summarize the null and alternative hypothesis: 1. The null hypothesis Ho is the hypothesis that is always tested. 2. The alternative hypothesis h1 is set up as the opposite of the null hypothesis and represents the conclusion supported if the null hypothesis is rejected. In what is known as classical hypothesis-testing methodology, we have the following three key points: 1. The null hypothesis Ho always refers to a specified value of the population parameter (such as μ), not a sample statistic (such as X) 2. The statement of the null hypothesis always contains an equal sign regarding the specified value of the population parameter (i.e. H0 = 368 grams) 3. The statement of the alternative hypothesis never contains an equal sign regarding...
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...not simply say something like “x1
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...terms related to hypothesis testing that you are already familiar with? What is the difference between a null and an alternative hypothesis statement? Why do a null and alternative hypothesis have to be mutually exclusive? (Due Wednesday) Terms that is related to hypothesis testing that I am familiar with is hypothesis, which means a prediction which is usually based on an informal observation, research and it is tested in a study. Theory is another term I know. Theory is what one has or a set of principles that tries to explain one or more facts. I did not know there was something called null hypothesis. According to our textbook, “null hypothesis” is because it “states the situation in which there is no difference ( the difference is null) between the populations” (Aron, Aron, & Coups, 2009). Null hypothesis will show that there is no observed effect for a experiment. A null hypothesis is what one tries to overturn his or her hypothesis test. Taylor (2013) states, “We hope to obtain a small enough p-value that we are justified in rejecting the null hypothesis” (para. 3). A alternative hypothesis is also known as an experimental hypothesis. It shows that there is a observed effect for an experiment. It is what one tries to show in an indirect way by using the hypothesis testing. If the null hypothesis is rejected then one will see that the alternative hypothesis is effective. Now if the finding are not null then one will not accept the alternative of the hypothesis. Reference ...
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...6: Introduction to Hypothesis Testing Significance testing is used to help make a judgment about a claim by addressing the question, Can the observed difference be attributed to chance? We break up significance testing into three (or four) steps: Step A: Null and alternative hypotheses The first step of hypothesis testing is to convert the research question into null and alterative hypotheses. We start with the null hypothesis (H0). The null hypothesis is a claim of “no difference.” The opposing hypothesis is the alternative hypothesis (H1). The alternative hypothesis is a claim of “a difference in the population,” and is the hypothesis the researcher often hopes to bolster. It is important to keep in mind that the null and alternative hypotheses reference population values, and not observed statistics. Step B: Test statistic We calculate a test statistic from the data. There are different types of test statistics. This chapter introduces the one-sample z-statistics. The z statistic will compare the observed sample mean to an expected population mean μ0. Large test statistics indicate data are far from expected, providing evidence against the null hypothesis and in favor of the alternative hypothesis. Step C: p Value and conclusion The test statistic is converted to a conditional probability called a P-value. The P- value answers the question “If the null hypothesis were true, what is the probability of observing the current data or data that is more extreme?” Small p values...
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...Introduction to Hypothesis Testing 8.1 8.2 8.3 8.4 8.5 CHAPTER 8 Inferential Statistics and Hypothesis Testing Four Steps to Hypothesis Testing Hypothesis Testing and Sampling Distributions Making a Decision: Types of Error Testing a Research Hypothesis: Examples Using the z Test Research in Focus: Directional Versus Nondirectional Tests Measuring the Size of an Effect: Cohen’s d Effect Size, Power, and Sample Size Additional Factors That Increase Power LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 2 Identify the four steps of hypothesis testing. Define null hypothesis, alternative hypothesis, level of significance, test statistic, p value, and statistical significance. Define Type I error and Type II error, and identify the type of error that researchers control. Calculate the one-independent sample z test and interpret the results. Distinguish between a one-tailed and two-tailed test, and explain why a Type III error is possible only with one-tailed tests. Explain what effect size measures and compute a Cohen’s d for the one-independent sample z test. Define power and identify six factors that influence power. Summarize the results of a one-independent sample z test in American Psychological Association (APA) format. 8.6 3 4 5 8.7 8.8 8.9 8.10 SPSS in Focus: A Preview for Chapters 9 to 18 8.11 APA in Focus: Reporting the Test Statistic and Effect Size 6 7 8 2 PART III: PROBABILITY AND THE FOUNDATIONS OF INFERENTIAL STATISTICS 8.1 INFERENTIAL...
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...Hypothesis Testing Hypothesis testing is basically a process that uses statistical inference to test claims about population parameters. In hypothesis testing we begin by making a tentative assumption about a population parameter. This assumption is called the null hypothesis and is denoted by Ho. We then define another hypothesis, called the alternative hypothesis, which is the opposite of what is stated is the null hypothesis. The alternative hypothesis is denoted by Ha. The hypothesis testing procedure involves using data from a sample to test the competing claims indicated by the null and alternate hypotheses. Testing the hypothesis is similar to a criminal trail. Ho: The defendant is innocent Ha: The defendant is guilty Just as the defendant in a criminal trial is assumed to be innocent, the null hypothesis is assumed to be true and given the benefit of the doubt. The testimony and evidence obtained during the trial provide the sample information. If the sample information is consistent with the assumption of innocence, the null hypothesis that the defendant is innocent cannot be rejected. On the other hand, if the sample information is inconsistent with the assumption of innocence, the null hypothesis is rejected and action is taken based on the alternative hypothesis that the defendant is guilty. Developing Null and Alternative Hypotheses In some applications it may not be obvious how the null and alternative hypotheses should be formulated...
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...Hypothesis Testing Question for self-study: 1. Mary Arnold wants to use the results of a random sample market survey to seek strong evidence that her brand of breakfast cereal has a least 20% of the total market. Formulate the null and alternative hypothesis, using P as the population proportion. 2. The Federal Reserve Board is meeting to decide if it should reduce interest rates in order to stimulate economic growths. State the null and alternative hypothesis regarding economic growth that the board would formulate to guide its decision. 3. John Stull, senior vice president of manufacturing, is seeking strong evidence to support his hope that new operating procedures have reduced the percentage of under filled cereal packages from the Ames production line. State his null and alternative hypothesis and indicate the results that would provide strong evidence. 4. During 2000 and 2001 many people in Europe objected to purchasing food that was genetically modified, produced by farmers in the United States. The U.S. farmers argued that there was no scientific evidence to conclude that these products were not healthy. The Europeans argued products were not healthy. The Europeans argued that there still might be a problem with these foods. a. State the null and alternative hypotheses from the perspective of the Europeans. b. State the null and alternative hypotheses from the perspective of the U.S. farmers. 5. The 200- presidential election in...
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...Assignment 8a PSYC340 Research Methods I 1. ______ statistics are used in the process of hypothesis testing. a. Descriptive b. Null c. Alternative d. inferential 2. Inferential statistics allow us to: a. infer something about the sample based on the population. b. infer something about the population based on the sample c. infer that the sample is representative d. do all the above 3. No effect is to _____ hypothesis as effect is to _____ hypothesis. a. null; alternative b. alternative; null c. one-tailed; two-tailed d. two-tailed; one tailed 4. Ho is to Ha as ______ hypothesis is to ______ hypothesis. a. null; alternative b. alternative; null c. one-tailed; two-tailed d. two-tailed; one tailed 5. One and two-tailed hypotheses are both types of ______ hypotheses. a. null b. alternative c. directional d. non-directional 6. When using a ____ hypothesis, the researcher predicts the direction of the expected difference between the groups. a. null b. non-directional c. one-tailed d. two-tailed 7. A false alarm is to ____ as a miss is to _____. a. Type I error; Type II error b. Type II error; Type I error c. null hypothesis; alternative hypothesis d. alternative hypothesis; null hypothesis 8. Failing to reject Ho when we should have rejected it is a ____ error. a. Type I b. Type II c. null d. one-tailed 9. If researchers report that the results from their study were significant, p < .05, this...
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...Business Statistics WISE-International Master Hypothesis Testing A hypothesis is a claim (conjecture/assumption) about a population parameter: population mean population proportion It is always about a population parameter, not a sample statistic A Common Theme Check the merits of this hypothesis based on sample information sample A hypothesis is formed about some population parameter infer Hypothesis testing provides a general framework for approaching such inference problems ˆ The Null Hypothesis Suppose that some hypothesis has been formed about the population parameter and that this hypothesis will be believed unless sufficient contrary evidence is produced. This hypothesis can be thought of as a maintained hypothesis. In the language of statistics, this hypothesis is called a null hypothesis, and is denoted as H0. In hypothesis testing, the null hypothesis plays a role similar to that of a defendant on trial in many judicial systems. Just as a defendant is presumed to be innocent until proven guilty, the null hypothesis is presumed to be true until the data strongly suggest otherwise. The Alternative Hypothesis, H1 Having a null hypothesis requires having an alternative hypothesis that challenges the null hypothesis. In a Court of Law H 0 : innocent H1 : guilty The defendant is deemed innocent until the prosecution presents sufficiently strong contrary evidence...
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...Solutions to Questions on Hypothesis Testing and Regression 1. A mileage test is conducted for a new car model, the “Pizzazz.” Thirty (n=30) random selected Pizzazzes are driven for a month and the mileage is carefully measured in each. The mean mileage for the sample is 28.6 miles per gallon (mpg) and the sample standard deviation is 2.2 mpg. Estimate a 95% confidence interval for the mean mpg in the entire population of Pizzazzes (you might need to round your answer a little bit to agree with mine). (a) (b) (c) (d) (e) (23.42, 33.84) (27.81, 29.39) (26.82, 30.47) (27.23, 30.03) None of the above 2. Determine the test statistic for testing the null hypothesis that the population mean is 27 mpg ( H0 : µ = 27 Ha : µ ≠ 27 ) (a) (b) (c) (d) (e) (f) t = 3.98 t = -3.98 t = 4.6 t = -4.6 t = 1.96 None of the above 3. A Type II error is made when a. b. c. d. e. the null hypothesis is accepted when it is false. the null hypothesis is rejected when it is true. the alternate hypothesis is accepted when it is false. the null hypothesis is accepted when it is true. the alternate hypothesis is accepted when it is true. 4. A recent USA TODAY/CNN/Gallup Poll showed that most American people support Bush’s efforts in the Middle East peace process. The poll of 2000 adults was conducted and 1243 people said they supported Bush’s efforts. a) Find a 95% confidence interval for p, the fraction of Americans who support Bush’s efforts phat =...
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...Hypothesis Testing Statistical Method Karl Phillip R. Alcarde MBA University of Negros Occidental-Recoletos DEFINITION DEFINITION Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true. Hypothesis testing or significance testing is a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. In this method, we test some hypothesis by determining the likelihood that a sample statistic could have been selected, if the hypothesis regarding the population parameter were true. The method of hypothesis testing can be summarized in four steps. 1. To begin, we identify a hypothesis or claim that we feel should be tested. For example, we might want to test the claim that the mean number of hours that children in the United States watch TV is 3 hours. 2. We select a criterion upon which we decide that the claim being tested is true or not. For example, the claim is that children watch 3 hours of TV per week. Most samples we select should have a mean close to or equal to 3 hours if the claim we are testing is true. So at what point do we decide that the discrepancy between the sample mean and 3 is so big that the claim we...
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...Chapter 11: Testing a Claim Objectives: Students will: Explain the logic of significance testing. List and explain the differences between a null hypothesis and an alternative hypothesis. Discuss the meaning of statistical significance. Use the Inference Toolbox to conduct a large sample test for a population mean. Compare two-sided significance tests and confidence intervals when doing inference. Differentiate between statistical and practical “significance.” Explain, and distinguish between, two types of errors in hypothesis testing. Define and discuss the power of a test. AP Outline Fit: IV. Statistical Inference: Estimating population parameters and testing hypotheses (30%–40%) B. Tests of significance 1. Logic of significance testing, null and alternative hypotheses; P-values; one- and two-sided tests; concepts of Type I and Type II errors; concept of power 4. Test for a mean (large sample -- ( known) What you will learn: A. Significance Tests for µ (( known) 1. State the null and alternative hypotheses in a testing situation when the parameter in question is a population mean µ. 2. Explain in nontechnical language the meaning of the P-value when you are given the numerical value of P for a test. 3. Calculate the one-sample z-statistic and the P-value for both one-sided and two-sided tests about the mean µ of a Normal population. 4. Assess statistical significance at standard levels α by comparing...
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...Hypothesis Testing Paper Hypothesis testing in statistics goes beyond the traditional acceptances of trying to prove a hypothesis correct. Hypothesis testing is matter of accepting what has already been proven until another hypothesis is verified to be true. In the essay, an overview of the hypothesis testing process is described using a fictional example and numerical values to detail the process. The idea formed by the hypothesis, “online students have more stress than traditional on campus students”, is to reject the hypothesis that online college students experience less stress than on campus college students. This is in no way true findings; it is a fictional example to help walk the reader through the steps of hypothesis testing. Hypothesis testing is a king of research used to say how a certain topic of interest will end or how researchers think it will end in the environment. The testing will show that just because one forms an answer it does not prove that the answer is correct for there are many factors that could change the outcome, which is why researchers use probability rates of five or one percent. The null hypothesis endeavors to show that there is no variation between variables, or a single variable is no different from zero. It is surmised to be true until statistical evidence nullifies it for an alternative hypothesis. Null hypothesis is a hypothesis that the researcher endeavors to confute, reject, or nullify. The research hypothesis is the categorical...
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...Hypothesis Testing and EViews p-values: Suppose that we want to test a null hypothesis about a single parameter using its estimated value (for example a mean or a regression coefficient). We can do so using a t-test. To begin, suppose that the parameter to be estimated is β. We must first specify a null hypothesis and an alternative hypothesis. 2 tail test: For a two tailed test, we want to test whether β is a particular value or not. We first set the value of β that we want to test. We’ll call this β0 to indicate that this will be the value of β under the null hypothesis. In a two tail test, the null and alternative hypotheses are: H0 : β = β 0 HA : β = β0 ˆ We proceed by estimating β. We denote the estimated value as β. This could for example be a sample mean estimate of the population mean, a least squared estimate of a regression coefficient, or a maximum likelihood estimate of a model coefficient, ˆ depending on the context. The estimate β is usually accompanied by a standard error ˆ to indicate how precisely it is estimated. We denote this standard error as se(β). This ˆ is a random variable with a sampling distribution. It will have reflects the fact the β different values in different samples. We can then form the following test statistic by computing the standardised statistic ˆ whereby we subtract the hypothesisized value β0 from the estimate β and divide by its standard error: t-stat = ˆ β − β0 ˆ se(β) ˆ Again, this test statistic is a random variable since it depends...
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