...Hypothesis Reflection Learning Team A: Kala Chavez, Jessica Gonani, Stacie Knauss, Michael Orcutt, and Troy Stodard QNT 351/Statistics for Business and Economics June 17, 2014 Don Silva Hypothesis Reflection This week Learning Team A is tasked with discussing three parts to hypothesis research. These objectives included evaluating the steps to test a research hypothesis, compare the means of two or more groups, and calculating the correlation between the two variables. By discussing these three objectives, team A will gain a better understanding of how the objectives are associated with statistical analysis. Research hypothesis Testing a theory or hypothesis sounds like an easy task but it is not. There are many steps to testing a research hypothesis. The five steps of hypothesis testing are (The University of North Carolina at Chapel Hill, 2012): 1. Stating the research question or questions 2. Specifying the null and alternative hypothesis involved 3. Calculating the test statistics 4. Computing the probability of test statistics or the rejection region 5. Stating the conclusions The first step, stating the research question, will clearly define and identify the research question. The population of interest, hypothesized values, and the parameters of the variables are clearly stated. Next is to state the hypothesis of at least two possible outcomes. These are the null and alternative hypothesis. The null hypothesis states that nothing will...
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...2011 Exercise: SPSS 5. Hypothesis test The MBA programme leader is interested to know if there is any significant average age difference between males and females and if there is which is the older group. a. Suggest a null hypothesis and an alternative hypothesis for testing the mean age for male and female students. μ0: The average ages of males and females are the same. μ1: The average ages of males and females are not the same. b. Carry out an appropriate test to compare the mean age for the two sexes, and interpret your results. Since the goal is to compare two means and that the data is of ratio scale, One-Way ANOVA is the appropriate test. Here we have gender as the factor and age as the dependent variable, and we choose the common 0.05 level of significance. Figure 5.1 is the resulting ANOVA table. | | | | | | |3.131a |2 |.209 | | |3.433 |2 |.180 | | |.543 |1 |.461 | | |40 | | | Figure 6.1 Cross table of satisfaction and sex at α=0.05 The p-value, which is 0.209, is very obviously greater than our chosen level of significance, 0.05. The null hypothesis is accordingly not rejected...
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...Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test Upload test...
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...RAJANI BHASHKAR E-mail:rajanibaranwal2000@gmail.com Mobile: +91 7507578738 Objective Seeking challenging opportunity as a software test engineer with strong academic background, excellent communication, knowledge of software testing principals and automation tools like Quick Test Professional (QTP) and Selenium having certification from SEED Infotech. Summary Highly motivated and enthusiastic aspiring software tester with good knowledge in C and Java technologies. Excellent communication skills. Energetic self-starter with excellent analytical and creative skills. Selective Accomplishments * B.Tech. (Computer Science) from Karnataka University with first class. * Secured grade A+ in ‘Diploma in Software Testing’ through SEED infotech. Technical Skills Programming Languages | C, Java, SQL | Technologies | Web applications | Tools | Bugzilla, QuickTestPro (QTP)9.0, Selenium, Test Link, Quality Center | Technical Experience Internship Project Project Title | Orange HRM(New Level of HR Management) | Client | Seed Infotech | Duration | 20th April,2015 to 27th April,2015 | Technologies | Visual Basic, C, SQL Server | Roles and Responsibilities | * Writing test cases for time module * Test execution of test cases of leave module * Defect reporting | Technologies | Java | Project Title | Orange HRM and Flights | | * Prerequisites of automation * Easy record and playback * Use of various...
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...September 20, 2011 TO WHOM SOEVER IT MAY CONCERN Re: Verification of End user of H-1B Beneficiary's Services. Beneficiary: ___________________ This letter confirms that ______________is and end user of Beneficiary's services as a subject matter expert in the position of SAP Consultant. We, __________ (Vendor Name) contracted for the beneficiary's services with her employer, _________________________ to provide services to the end user, _____________________. Responsibilities in this position are sufficiently complex so as to require the services of H-1B specialty worker. The minimum educational requirement for this position is a Bachelor's degree or its Equivalent in the related field of study. As a SAP Consultant, her job duties include but not limited to: • Work with Customer Solutions lead and business personnel to understand various human resource related business processes, such as Organizational Management, Personnel Administration, Payroll, Time Evaluation/CATS, Compensation and interfaces, and provide the technology to support these processes. • Based on requirements submitted by the business, develop detailed functional and technical specifications for changes to the SAP ECC system. • Primary responsibility for incident management and resolution for area of responsibility. • Understand and follow all relevant standard Client's IT processes and procedures. Perform configuration for the HR SAP modules of Payroll, Time Evaluation...
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...ABSTRACT To reduce the effort, testing cycle time & % of human errors that can easily creep in while comparing the results of Regression Test Suite, a thought process was put into designing & implementing an Automation Framework for the purpose. A lot of work and research has already being done for the Execution phase of Regression Testing wherein two parallel sides – Test & Prod are setup & Test Cases executed by firing the same one after the another & results stored. A large number of Regression Automation Tools are available in market like, QTP, Selenium, WATIR etc, to cover this up. Contrary to this very less work is available & very less has been thought about the Comparison phase wherein Test Results thus generated have to be compared to produce a summary report for QA Testers to analyze which they can further categorize into Expected & Unexpected Breaks & then reach out to Development for investigation & thus complete the end-to-end life cycle of Regression Testing. With advent of IT and shift of focus toward Financial Banks & Institutions, a need is felt to have some faster & feasible way to compare records with high volume. That is the starting point for this paper under which an Automation Framework for Comparison Phase of Regression Testing is built in Perl, that could easily cover records of any volume. Use of Industry Compliant Methodology, named Best Match, made the framework even more flexible for scenarios having duplicate records on either of the two parallel...
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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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...Regression of Renata Pharmaceuticals Ltd. Dependent and Independent variable data for regression are given below: Year | Market Share Price | ROE | DPS | P/E | NP | 2000.0 | 431.5 | 11.34 | 30.0 | 5.34 | 37.56 | 2001.0 | 615.25 | 17.43 | 40.0 | 4.12 | 67.23 | 2002.0 | 650.0 | 16.25 | 50.0 | 4.16 | 72.56 | 2003.0 | 1261.0 | 22.57 | 70.0 | 5.55 | 105.56 | 2004.0 | 3200.0 | 25.0 | 70.0 | 12.27 | 145.59 | 2005.0 | 3000.0 | 26.0 | 70.0 | 10.43 | 192.57 | 2006.0 | 3099.25 | 24.65 | 70.0 | 23.14 | 242.13 | 2007.0 | 7491.25 | 26.29 | 70.0 | 40.31 | 358.02 | 2008.0 | 7789.25 | 26.06 | 75.0 | 32.5 | 438.67 | 2009.0 | 12051.5 | 27.34 | 85.0 | 36.09 | 594.48 | Where market price is dependent variable and ROE, DPS, P/E, and NP are independent variable. Model Summaryb | Model | R | R Square | Adjusted R Square | Std. Error of the Estimate | Change Statistics | Durbin-Watson | | | | | | R Square Change | F Change | df1 | df2 | Sig. F Change | | 1 | .987a | .974 | .954 | 840.25403 | .974 | 47.163 | 4 | 5 | .000 | 2.660 | a. Predictors: (Constant), NP, ROE, PE, DPS | | | | | | | b. Dependent Variable: Market | | | | | | | | INTERPRETATION OF THE COEFFICIENT OF MULTIPLE DETERMINATIONS (R2) The model summary shows some important indicators of the explaining power of the model. The R-square value shows the percent of variation in the dependent variable is explained by the set of independent variables. In this case, r-square value...
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...Background 3 Methods of Data Collection 4 Results and Interpretations Descriptive Statistics 5 A. Stem and Leaf Plot 7 B. Box Plot 8 C. Histogram 9 D. Probability 11 E. 95% Confidence Interval for Mean 13 F. Scatter plot 14 G. Simple Linear Regression 15 H. Multiple Linear regression 16 I. Residual plot 17 J. Mean T-test 18 Summary and Conclusions 19 Introduction and Background In medical device manufacturing there are a lot of proteins that are produced such as antibodies, which require concentration determination. These protein concentration determinations necessary for downstream manufacturing processes to make sure the correct protein concentrations are used to meet product specifications and product performance. In the testing areas there are several test methods used by the laboratory to determine protein concentration. One method is the Amino Acid Analysis method (AAA). This method takes the protein and “unzips” the DNA strand of the protein and analyzes the protein for the different Amino Acids, then calculates a theoretical protein concentration based on the testing. Another method employed is the UV absorbance method (A280), where UV light (wavelength 280) is passed through the sample and hits the detector on the other side of the sample, at which point a measurement is made. The instrument will then use the light reading to determine a protein concentration of the material...
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...Table of Contents Introduction……………………………………………………………………………………………………………………2 Descriptive Analysis and Frequency Distribution…………………………………………………………….2 Hypothesis Testing………………………………………………………………………………………………………….7 Hypothesis 1…….……………………………………………………………………..……………………......7 Hypothesis 2……………………………………………………………………………………………………...8 Hypothesis 3……………………………………………………………………………………………………...9 Multiple Regression Analysis…………………………………………………..…………………………………….11 Summary ……….…………………………………………………………………………..………………………………..16 Reference……………………………………………………………….…………………………………………………….19 Appendices……………………………………………………………………….…………………………………………..20 Introduction For my statistical data analysis project, I chose to analyze the National Basketball Association (NBA) 2013 regular season teams. The analysis looks at the total team and reviewed the information such as games played, field goals attempt and percentage, free throw attempts and percentage, blocks and steals. The data was obtained from the NBA website. For the 2013 NBA stats there were 30 teams that played on the average of 82 games. Based on statistical analysis, the most important keys for team success in basketball and their relative weights, in parentheses, are field goal percentage, turnovers, offensive rebounds, free throw attempts and percentage, blocks and steals. Coaches are always looking for a better understanding of what makes up a winning team. This knowledge would help them improve the team statistics in the areas listed...
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...However, Tom has little to no experience on the business end of owning a car dealership. It has been reported that Tom’s asking prices for his vehicles are not aligned with the average market price. Ultimately, Tom does not base his prices on the used car market price; instead he bases it on assumptions. For example, Tom thinks, red cars give the impression of being “sportier and can sell at a premium,” he also believes older models normally sell for less. After much dismay and inaccurate rates in sells, Tom tries to systematically price his Mustangs, but due to the multitude of variables, Tom found this task challenging. (Bowerman, 2003, p. 3) This report will provide information obtained through hypothesis confidence intervals and hypothesis testing, multiple and simple regression models, descriptive statistics, and historical background information pertaining to Tom’s Ford Mustangs. This report will pay close attention to the possible relationship between the dependent...
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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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...Learning Team Assignment MTH 233 Week 4 Individual Assignment: Individual Assignment MTH 233 Week 5 Individual Assignment: Individual Assignment MTH 233 Week 5 Learning Team Assignment: Hypothesis Testing and Regression Analysis Paper only MTH 233 Learning Team Assignment: Hypothesis Testing and Regression Analysis Presentation ----------------------------------------------- MTH 233 Learning Team Assignment Hypothesis Testing and Regression Analysis Presentation For more classes visit www.snaptutorial.com Resources: University Library and the Internet Select a research issue, problem, or opportunity facing a Learning Team member’s organization to examine using hypothesis testing and a regression analysis on the collected data. Write a 1,050- to 1,750-word paper describing a new hypothesis test using a different statistic (e.g., large sample size, small sample size, means and/or proportions, one- and two-tailed tests) to perform on that data. Formulate a new hypothesis statement and perform the five-step hypothesis test on the data. Describe the results of the tests. Interpret the results of the regression analysis, state the limitations of the analysis, and describe the significance of the results to the organization. Be sure to attach the results of the regression analysis created in Microsoft® Excel to your paper. Present the results to the class in a 10-minute PowerPoint® presentation ----------------------------------------------- MTH 233...
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...Meghan Corvington February 29. 2016 EC492 Forecasting Prof. Orlowski PROBLEM SET #1 (Due date: February 29, 2016) Use the quarterly data base from the Federal Reserve Bank of St. Louis FRED provided to you in the EViews program to answer the following questions: 1. Choose the real US export of goods and services (REXPGS), real import of goods and services (RIMPGS) and disposable personal income (DPI) variables. View their descriptive statistics. Analyze skewness, kurtosis and volatility (measured by the coefficient of variation) of each of them. Discuss possible economic factors underlying the data asymmetry, kurtosis and relative volatility. In this example, all three variables are left skewed, while kurtosis is between 1.99 and 2.29 for each of the three variables. I measured volatility based on the standard deviation of the three variables. DPI is extremely volatile, while RIMPGS and REXPGS are not as volatile as DPI. It seems as if there is a strong correlation between the amounts of standard deviations compared to the kurtosis. There is a lack of asymmetry between the three variables, as there is no equivalence in various measurements. | |REXPGS |RIMPGS |DPI | | Mean | 747.1115 | 962.0551 | 4682.076 | | Median | 447.5000 | 636.0000 | 3400.400...
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