Hypothesis Testing Project - Inferential Statistics
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I created this presentation as a final project for the subject of Inferential Statistics. It defines the different hypothesis, inferential statistics, regression analysis, the use of dependent and independent variables. It also consists an example of these theories at the end. Let me know in the comments if you want me to upload a video of myself presenting this presentation.
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Hypothesis testing
Hypothesis testing is a methodology of econometrics that involves making and testing hypotheses about population parameters. A hypothesis is a statement or assumption that is tested. There are two types of hypotheses: simple hypotheses that specify all parameters and composite hypotheses that specify some parameters. The steps in hypothesis testing are: 1) stating the null hypothesis, 2) specifying the mathematical and econometric models, 3) collecting data, 4) testing the hypothesis, 5) estimating the model, 6) forecasting and prediction, and 7) using the model for policy recommendations.
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The Mann Witney U Test in statistics is related to a testing without considering any assumption as to the parameters of frequently distributed of a valueless hypothesis. It is similar to the value selected randomly from one sample, can be higher than or lesser than a value selected randomly from a second sample. Copy the link given below and paste it in new browser window to get more information on Mann Whitney U Test:- http://www.transtutors.com/homework-help/statistics/mann-whitney-u-test.aspx
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Hypothesis testing is a methodology of econometrics that involves making and testing hypotheses about population parameters. A hypothesis is a statement or assumption that is tested. There are two types of hypotheses: simple hypotheses that specify all parameters and composite hypotheses that specify some parameters. The steps in hypothesis testing are: 1) stating the null hypothesis, 2) specifying the mathematical and econometric models, 3) collecting data, 4) testing the hypothesis, 5) estimating the model, 6) forecasting and prediction, and 7) using the model for policy recommendations.
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1. The document provides an overview of statistical analysis including the scientific method, common statistical terminology, hypotheses testing, choosing an appropriate statistical method, the normal distribution, and significance and confidence limits.
2. It explains key concepts like the null hypothesis, which is the opposite of the research hypothesis and is disproven through statistical testing.
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Inferential statistics use samples to make generalizations about populations. It allows researchers to test theories designed to apply to entire populations even though samples are used. The goal is to determine if sample characteristics differ enough from the null hypothesis, which states there is no difference or relationship, to justify rejecting the null in favor of the research hypothesis. All inferential tests examine the size of differences or relationships in a sample compared to variability and sample size to evaluate how deviant the results are from what would be expected by chance alone.
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3) Inferential statistics involve estimation, such as estimating a population parameter and constructing a confidence interval, and hypothesis testing, where a null hypothesis is proposed and data is used to determine if it should be rejected.
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- A confidence interval gives the probability that the interval calculated from the sample data contains the true population parameter. Common confidence intervals are 95% confidence intervals.
- Formulas for confidence intervals depend on whether the population standard deviation is known or unknown, and the sample size.
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- 1. COURSE PROJECT SUBMITTED TO: DR.SAJID SUBMITTED BY: SYED ALI ROSHAN MA EPM (3rd) NATIONAL UNIVERSITY OF MODERN LANGUAGRS ISLAMABD
- 2. HYPOTHESIS TESTING A Hypothesis is an assumption regarding a data set or a population. It may or may not be true however, carrying out inferential statistics can help us identify if its correct and then how correct it is. If the relationship is strong enough to be considered or not.
- 3. TYPES OF HYPOTHESIS There are two types of hypothesis testing, • Null Hypothesis • Alternative Hypothesis
- 4. NULL, ALTERNATIVE HYPOTHESIS A Null hypothesis is considered as the status quo and is followed by statisticians. However, if a statistician decides to test the Null Hypothesis using new or innovative data, which disregards the Null Hypothesis and brings a new hypothesis, it is known as Alternative Hypothesis.
- 5. INFERENTIAL STATISTICS In Education, inferential statistics can be used to understand and estimate several applications. One of these applications can be the effect on the marks of students if an innovative teaching strategy is introduced by the teachers. Statisticians may be able to gather data of students marks before the teaching strategy is implemented and the marks of students after implementing the strategy.
- 6. CONT..... This could help identify if the teaching strategy was helpful or not. If it was helpful to the students, it would mean that students will receive more marks and vice versa. This would allow the institution to either continue using the strategy or to discard it completely, or to modify it according to student’s needs.
- 7. PURPOSE OF REGRESSION ANALYSIS Regression is a tool that statisticians use to predict some event or to create a new model. In regression, the effect of variable on another variable is studied. It summarizes the best relationship between a dependent variable and independent variable.
- 8. CONT..... Regression equation: • Y= a + bX An example, could be to find the relationship between test score of a student in each subject against the number of hours he prepared for the test of each subject. This will help determine how many number of hours are required for the student to earn an A grade.
- 9. DEPENDANT,INDEPENDENT VARIABLES A dependant variable is the one which changes due to a change in the independent variable. However, an independent variable has no effect on it if the dependent variable is changed.
- 10. CONT..... • Independent variable is usually denoted by x and represented on the x axis of the graph and the dependent variable is usually denoted by y and is represented on the y axis of the graph.
- 11. X Y X - x̅ Y - Ȳ ( X - x̅ ) 2 ( Y - Ȳ ) 2 ( X - x̅ )2 ( Y - Ȳ ) 2 7 8 1.4 1.8 1.96 3.24 2.52 4 5 -1.6 -1.2 2.56 1.44 1.92 4 7 -1.6 0.8 2.56 0.64 -1.28 7 6 1.4 -0.2 1.96 0.04 -0.28 6 5 0.4 -1.2 0.16 1.44 -0.48 The relationship between attendance and test marks is positive however very weak.