...Linear Regression I would like to know if people who enjoy thrill seeking have tattoos. I believe thrill seeking and tattoos go hand in hand. Most people I know are adventurous, risk takers, and daredevils and all of them have tattoos. I have a strong feeling that the correlation between the two will have a strong positive relationship. X= Tattoos Y= Thrill Seeking The scatter plot shows an extremely rough linear pattern but there is an upward sloping. Line of best fit: y = 0.9148x +25.505 Analysis: 1. r = .14 little or no correlation 2. R^2 = 2% 2% of the variance in thrill seeking is accounted by tattoos. 3. Slope = 0.0196(m) For every 1 tattoo people have there is an increase we expected of 0.9148 in thrill seeking. Conclusion: Between these two variables, there are no correlations between the two. It was shocking to see there is no relationship between the two. I truly believed people who are thrill seekers have tattoo. T-Test Independent 2 Sample My gym teacher believes that males are stronger than females and that is why males have more tattoos. The scale is determine by the number of tattoos both males and females have. Eighty-four males and one hundred and eleven females responded. The males average 39 (s.d. 1.42) while the females average 38 (s.d. 0.98). At the .10 significance level, test to see if there is a difference between males having more tattoos than females? Ho: Null Hypothesis Males equal Females Ha: Null Hypothesis...
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...In multiple linear regression analysis, R2 is a measure of the ________. A) homoskedasticity of the predictors B) misclassification rate C) percentage of the variance of the dependent variable that is explained by the set of independent (predictor) variables D) precision of the resulting model when applied to the validation data 2. Categorical variables can be used in a multiple linear regression model _________. A) by partitioning of the dataset B) when no multicollinearity among the independent variables is present C) when the sample size is at least 10 times that of the number of variables D) through the use of dummy variables 3. In multiple linear regression analysis “multicollinearity” refers to _________. A) two or more predictors sharing the same linear relationship with the outcome variable B) a high degree of correlation between the dependent variables C) the equality of the variance of the dependent throughout its range of values D) None of the above. 4. In multiple regression analysis, which of the following is an example of a subset selection algorithm? A) Forward selection B) Backwards elimination C) Stepwise regression D) All of the above 5. _________ is an important property of a good model. A) Complexity B) Independence C) Parsimony D) None of the bove 6. An assumption that applies to the linear multiple regression method is that the distribution of the error term values should be ________. A) standardized ...
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...Introduction Simple linear regression is a model with a single regressor x that has a relationship with a response y that is a straight line. This simple linear regression model is y = β0 + β1x + ε where the intercept β0 and the slope β1 are unknown constants and ε is a random error component. Testing Significance of Regression: H0: β1 = 0, H1 : β1 ≠ 0 The hypotheses relate to the significance of regression. Failing to reject H0: β1 = 0 implies that there is no linear relationship between x and y. On the other hand, if H0: β1 = 0 is rejected, it implies that x is of value in explaining the variability in y. The following equation is the Fundamental analysis-of-variance identity for a regression model. SST = SSR + SSRes Analysis of variance (ANOVA) is a collection of statistical models used in order to analyze the differences between group means and their associated procedures (such as "variation" among and between groups), developed by R. A. Fisher. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. P value or calculated probability is the estimated probability of rejecting the null hypothesis (H0) of a study question when that hypothesis is true. VIF (the variance inflation factor) for each term in the model measures the combined effect of the dependences among the regressors on the variance of the term. Practical experience indicates that if any of...
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...Chapter 4 Multiple Linear Regression Section 4.1 The Model and Assumptions Objectives Participants will: understand the elements of the model understand the major assumptions of doing a regression analysis learn how to verify the assumptions understand a median split 3 The Model y o 1x1 ... p x p or in Matrix Notation Dependent Variable nx1 Unknown Parameters (p+1) x 1 Y X e Independent Variables – n x(p+1) Error – nx1 4 Questions How many unknown parameters are there? Can you name them? How many populations will be sampled? What are conceptual populations? 5 Major Requirements for Doing a Regression Analysis The errors are normally distributed (not Y). Constant variance – What is the null hypothesis? Linear in the parameters Errors are independent. Some people call these assumptions. EY () X 6 Example We have observed y = response (change in blood pressure) and x = dosage level of a drug. We assume a linear relationship between E(y) and x. The two graphs are the same, but they have been rotated to give additional views. 7 continued... Example 8 continued... Example Sketch E(y). Based on the graphs, make comments about the assumptions. Do they appear to be satisfied or violated? How many populations are represented by the graphs? List all of the parameters. Write the model down. 9 Checking Assumptions Testing the residuals for normality PROC CAPABILITY ...
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...Linear Regression Forecast Nicolas Scott Gomez Park University Introduction………………………………………………………………………………………..3 Subjects and Methods...…………………..…………………………….…………………………3 Results…………………..…………………………………………………………………………4 References…...…………………………………………………………………………………….7 Introduction There is a growing awareness of obesity in more modern nations which has added importance to efforts in understanding causes and natural history of obesity. In order to understand it, you must determine what a normal body fat content is and how it changes with age. Most recently, there are four component models of body composition that don’t rely on major assumptions about constant compositions that have been developed. The models offer the opportunity to determine the relation between age and body composition components such as fat in a more accurate way. In 1999, a study was done showing several studies showing body composition variables like fat mass and how they vary significantly among ethnic groups. Subjects and Methods Fat mass was determined once in a large sample of healthy volunteers by using a 4 component model requiring measurement of body volume, total body water, total body bone mineral mass and the body weight. The relation between age and body fat was explored by using several different statistical methods and the 1324 volunteers ages 20-94 were recruited for his study through various means of advertising. These studies were performed between 1986 and 1997 and each potential subject needed...
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...Forecasting Gold Prices Using Multiple Linear Regression Method Z. Ismail, 2A. Yahya and 1A. Shabri Department of Mathematics, Faculty of Science 2 Department of Basic Education, Faculty of Education University Technology Malaysia, 81310 Skudai, Johor Malaysia 1 1 Abstract: Problem statement: Forecasting is a function in management to assist decision making. It is also described as the process of estimation in unknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago, but is now once again accepted as a potential currency. The demand for this commodity is on the rise. Approach: Objective of this study was to develop a forecasting model for predicting gold prices based on economic factors such as inflation, currency price movements and others. Following the melt-down of US dollars, investors are putting their money into gold because gold plays an important role as a stabilizing influence for investment portfolios. Due to the increase in demand for gold in Malaysian and other parts of the world, it is necessary to develop a model that reflects the structure and pattern of gold market and forecast movement of gold price. The most appropriate approach to the understanding of gold prices is the Multiple Linear Regression (MLR) model. MLR is a study on the relationship between a single dependent...
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...PSYCH 625 Week 5 Learning Team Assignment Linear regression To Buy this Class Copy & paste below link in your Brower http://www.homeworkregency.com/downloads/psych-625-week-5-learning-team-assignment-linear-regression/ Or Visit Our Website Visit : http://www.homeworkregency.com Email Us : homeworkregency@gmail.com PSYCH 625 Week 5 Learning Team Assignment Linear regression PSYCH 625 Week 5 Learning Team Assignment Linear regression To Buy this Class Copy & paste below link in your Brower http://www.homeworkregency.com/downloads/psych-625-week-5-learning-team-assignment-linear-regression/ Or Visit Our Website Visit : http://www.homeworkregency.com Email Us : homeworkregency@gmail.com PSYCH 625 Week 5 Learning Team Assignment Linear regression PSYCH 625 Week 5 Learning Team Assignment Linear regression To Buy this Class Copy & paste below link in your Brower http://www.homeworkregency.com/downloads/psych-625-week-5-learning-team-assignment-linear-regression/ Or Visit Our Website Visit : http://www.homeworkregency.com Email Us : homeworkregency@gmail.com PSYCH 625 Week 5 Learning Team Assignment Linear regression PSYCH 625 Week 5 Learning Team Assignment Linear regression To Buy this Class Copy & paste below link in your Brower http://www.homeworkregency.com/downloads/psych-625-week-5-learning-team-assignment-linear-regression/ Or Visit Our Website Visit : http://www.homeworkregency.com Email Us : homeworkregency@gmail.com PSYCH...
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...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, i.e. the level of satisfaction is independent of the sexes. 7. Correlation and regression The MBA programme leader has become so enthralled with the benefits of using statistical analysis as a performance indicator toolkit, then decides that further analyses on student performance, expected and anticipated salaries ought to be carried out. a. Produce a...
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...Does Exchange Rate Exposure Matter? By Craig Doidge, John Griffin, and Rohan Williamson* Draft: May 8, 2002. Comments Welcome. _________________ Doidge is at the Ohio State University, Fisher College of Business, Columbus, OH 43210, email: doidge.4@osu.edu. Griffin is at Arizona State University, College of Business, Tempe, AZ 85287, email: john.griffin@asu.edu, and Williamson is at Georgetown University, McDonough School of Business, Washington, DC 20057, email: williarg@georgetown.edu. This paper replaces an earlier draft entitled, “An International Comparison of Exchange Rate Exposure.” We thank Yiorgos Allayannis, James Linck, Patrick Kelly, Spencer Martin, Felix Meschke, Clifford Smith, René Stulz, and participants at the International Finance Conference at the Georgia Institute of Technology, Cornell University, Georgetown University, and the Ohio State University for helpful comments and suggestions. We also thank Selim Topaloglu for research assistance. Williamson acknowledges research support from the Capital Markets Research center at Georgetown University. All errors are the responsibility of the authors. * Does Exchange Rate Exposure Matter? Abstract Previous literature finds mixed empirical support for a relation between exchange rate exposure and its theoretical determinants and that exposure is of negligible economic importance. To re-examine the nature and the economic significance of the exchange rate to firm value relation, we construct an international...
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...Correlation Correlation is a statistical technique that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people. The relationship isn't perfect. People of the same height vary in weight, and you can easily think of two people you know where the shorter one is heavier than the taller one. Nonetheless, the average weight of people 5'5'' is less than the average weight of people 5'6'', and their average weight is less than that of people 5'7'', etc. Correlation can tell you just how much of the variation in peoples' weights is related to their heights. Although this correlation is fairly obvious your data may contain unsuspected correlations. You may also suspect there are correlations, but don't know which are the strongest. An intelligent correlation analysis can lead to a greater understanding of your data. Techniques in Determining Correlation There are several different correlation techniques. The Survey System's optional Statistics Module includes the most common type, called the Pearson or product-moment correlation. The module also includes a variation on this type called partial correlation. The latter is useful when you want to look at the relationship between two variables while removing the effect of one or two other variables. Like all statistical techniques, correlation is only appropriate for certain kinds of data. Correlation works for quantifiable data...
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...FITNESS LEVEL AT IFMR [Document subtitle] Contents PROBLEM STATEMENT 2 Research Objective 2 INTRODUCTION 2 Variable View in SPSS 3 Data View in SPSS 3 DESCRIPTIVE STATISTICS 4 Gender Statistics 4 BMI Health Statistics 4 Factors Influencing Exercise Participation 7 Factors Influencing Exercise Non- Participation 8 CONFIDENCE INTERVAL 9 CROSS-TABULATIONS WITH CHI-SQUARE ANALYSIS: 10 T-Test Analysis: 14 One-Sample T-Test: 14 Independent Sample T-Test: 16 Paired Sample T-Test: 17 FACTOR ANALYSIS 20 KMO measure of sampling adequacy 21 Bartlett’s test of sphericity 21 LINEAR MULTIPLE REGRESSION 21 Multiple Linear Regression Equation: 25 ONE-WAY ANOVA TEST 26 INTERPRETATION OF ATTITUDE SCALE 29 CONCLUSION 31 PROBLEM STATEMENT In what ways can IFMR improve the fitness level among its population by better analyzing factors influencing exercise participation & exercise non-participation. Research Objective The research attempts to analyze the fitness level of students in the IFMR, as well as ways to improve the fitness level and what exercise they do on regular basis to maintain their physical health. Moreover, a research objective is to collect statistical information of the students and the factors influencing exercise participation & non-participation. The data that was received from the questionnaires and the responses were further analyzed through statistical analysis with the help of SPSS. INTRODUCTION The report details the analysis made...
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...Multicultural buyer/supplier relationship: the impact of importance of trust and ease of adaptation on continuity. Multicultural buyer/supplier relationship: the impact of importance of trust and ease of adaptation on continuity. Summary Abstract: 2 Introduction: 2 I) Theory: 3 Research question 3 II) Literature review: 3 Buyer-supplier relationship 3 Importance of trust 3 Adaptation 3 Continuity 4 Culture and buyer-supplier relationship 4 III) Hypothesis and conceptual framework 5 IV) Methodology 7 Research Design 7 Data Collection 7 Reliability and Validity analysis 8 Model significance and assumptions 9 V) Results 13 Hypothesis validation 15 VI) Limitations and further research: 16 Conclusions and managerial implications 18 Appendix 19 Survey: 19 References: 20 Multicultural buyer/supplier relationship: the impact of importance of trust and ease of adaptation on continuity. Abstract In today’s globalized world, more and more companies are dealing with international partners or tends to integrate international territories in order to expand their business. Those companies have, thus, a strong need to understand the impact of cultural differences on the working relationships between key dyads in the business process. The purpose of this study is to investigate the effects of cultural differences on specific factors (Importance of trust, Ease of adaptation and continuity) that determine the efficiency...
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...1. Report the sample you selected and the question that was explored in the study. * There is a correlation between x and y. The scatter chart shows that the more hours a student studies the higher their grade percentage will be. 2. Report the r2 linear correlation coefficient and the linear regression equation produced in the Excel spreadsheet. * The linear correlation coefficient is positive. * The r^2 linear correlation coefficient is 0.785 * The linear regression equation is y=1.5608x+55.767 3. What would be the value of Pearson’s r (simply the square root of r2)? * R^2 is 0.785, which means that 78.5% of the total variation in y can be explained by the relationship between x and y. The other 21.5% remains unexplained. 4. Would Pearson’s r be positive or negative? What does this imply about the relationship between the factors in this study? * Pearson’s r is positive and equal to 1.0. Since Pearson r is positive this implies that there is a strong correlation between x and y. 5. What is the implication of any correlation found between the variables in the study you picked? * X & Y have a strong positive correlation. It seems the more hours put into studying the better the grade percentages will be. 6. Does this correlation imply a causal relationship? Explain. * There is a casual relationship, because on the graph it shows positive correlation, because the slopes are increasing. That implies if you spend more...
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...Journal of Mechanical Engineering and Automation 2015, 5(1): 20-28 DOI: 10.5923/j.jmea.20150501.03 Effect of Evaporator Heater Power Input and Refrigerant Flow Rate on the Performance of a Refrigerator – Developing Empirical Models Ali Naif*, Abdulkareem Shafiq Mahdi Al-Obaidi, Mohammad H. Nassir School of Engineering, Taylor’s University, Malaysia Abstract Refrigerators normally are systems that are used to preserve perishable goods in a house hold by reducing the temperature of the food compartments. However, refrigerators are also infamous for their high electricity consumptions. This paper presents first a comparison between theoretical and experimental determination of the tons of refrigeration (TR) of a vapor compression refrigeration cycle (VCRC). Then, it was looked into the derivation of empirical models, grounded on experimental results, which would provide refrigerator designers a reliable mean to check on the impact of each examined parameter on the TR during the preliminary stage of refrigerator design. It was noted that both evaporator heater power input (EHPI) and refrigerant flow rate (RFR) positively affected the tons of refrigeration, while the effects of condenser water flow rate (CWFR) was negligible. A total of three models generated. As the accuracy of the data for all models were about 99.7%, the minute difference had to be looked in to. Hence, Model 1, considering both evaporator heater power input (EHPI) and refrigerant flow rate (RFR)...
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...1. Report the sample you selected and the question that was explored in the study. * There is a correlation between x and y. The scatter chart shows that the more hours a student studies the higher their grade percentage will be. 2. Report the r2 linear correlation coefficient and the linear regression equation produced in the Excel spreadsheet. * The linear correlation coefficient is positive. * The r^2 linear correlation coefficient is 0.785 * The linear regression equation is y=1.5608x+55.767 3. What would be the value of Pearson’s r (simply the square root of r2)? * R^2 is 0.785, which means that 78.5% of the total variation in y can be explained by the relationship between x and y. The other 21.5% remains unexplained. 4. Would Pearson’s r be positive or negative? What does this imply about the relationship between the factors in this study? * Pearson’s r is positive and equal to 1.0. Since Pearson r is positive this implies that there is a strong correlation between x and y. 5. What is the implication of any correlation found between the variables in the study you picked? * X & Y have a strong positive correlation. It seems the more hours put into studying the better the grade percentages will be. 6. Does this correlation imply a causal relationship? Explain. * There is a casual relationship, because on the graph it shows positive correlation, because the slopes are increasing. That implies if you spend more time studying your...
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