...Virtual Lab: Dependent and Independent Variables Worksheet 1. ECB refers to: a. A genetically engineered plant that is resistant to insect pests b. Edible corn byproducts c. An insect pest that reduces corn yield d. European corn borer e. c and d 2. How many days are required for a corn seed to become a mature plant with maximum weight kernels ready to be harvested? e. about 23 f. about 65 c. about 140 d. about 180 3. “BT Corn” refers to corn that: a. Has been infested with insect pests b. Has been infected with bacteria c. Is resistant to ECB d. Is not affected by pesticides 4. BT is: a. A stomach poison produced by bacteria b. A genetically engineered corn product c. A bacterium carried by the European corn borer d. A bacterium that has a gene for producing Cry proteins 5. Creation of BT corn requires genetic material from all of the following except: a. European corn borer b. Bacillus thuringiensis c. a corn plant d. all of the above contribute genetic material to the production of BT corn Table 1: Average Yield for each seed variety at no, low, and high infestation levels Seed Variety | Level of ECB Infestation | Pot 1 Yield | Pot 2Yield | Pot 3Yield | Average Yield | BT 123 | None | 160.1 | 164.8 | 164.2 | 163 | | Low | 164.0 | 162.6 | 168.3 | 165 | ...
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...whose action is specified on variables. Take x and y to be two variables. A function f may map x to some expression in x. Assigning gives a relation between y and x. If there is some relation specifying y in terms of x, then y is known as a dependent variable (and x is an independent variable). Statistics In a statistics experiment, the dependent variable is the event studied and expected to change whenever the independent variable is altered.[1] Modelling In mathematical modelling, the dependent variable is studied to see if and how much it varies as the independent variables vary. In the simple stochastic linear model the term is the i th value of the dependent variable and is i th value of the independent variable. The term is known as the "error" and contains the variability of the dependent variable not explained by the independent variable. With multiple independent variables, the expression is: , where n is the number of independent variables. Simulation In simulation, the dependent variable is changed in response to changes in the independent variables. Statistics Synonyms Independent variable An independent variable is also known as a "predictor variable", "regressor", "controlled variable", "manipulated variable", "explanatory variable", "exposure variable" (see reliability theory), "risk factor" (see medical statistics), "feature" (in machine learning and pattern recognition) or an "input variable."[2][3] "Explanatory variable" is preferred by some authors...
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...Credits 781 individuals contributed to OpenOffice.org (and whose contributions were imported into LibreOffice) or LibreOffice until 2013-06-21 09:07:34. * marks developers whose first contributions happened after 2010-09-28. Developers committing code since 2010-09-28 Ruediger TimmCommits: 83578Joined: 2000-10-10 | Kurt ZenkerCommits: 31935Joined: 2000-09-25 | Oliver BolteCommits: 31190Joined: 2000-09-18 | Jens-Heiner Rechtien [hr]Commits: 28866Joined: 2000-09-18 | Vladimir GlazunovCommits: 26286Joined: 2000-12-04 | Ivo HinkelmannCommits: 9512Joined: 2002-09-09 | Caolán McNamaraCommits: 9082Joined: 2000-10-10 | Frank Schoenheit [fs]Commits: 5025Joined: 2000-09-19 | Tor LillqvistCommits: 4488Joined: 2010-03-23 | Stephan BergmannCommits: 4113Joined: 2000-10-04 | Kohei YoshidaCommits: 3505Joined: 2009-06-19 | Hans-Joachim LankenauCommits: 3007Joined: 2000-09-18 | Ocke Janssen [oj]Commits: 2852Joined: 2000-09-20 | Mathias BauerCommits: 2580Joined: 2000-09-20 | Michael StahlCommits: 2535Joined: 2008-06-16 | Oliver SpechtCommits: 2458Joined: 2000-09-21 | David TardonCommits: 2435Joined: 2009-11-12 | Philipp Lohmann [pl]Commits: 2096Joined: 2000-09-21 | Miklos VajnaCommits: 1869Joined: 2010-07-29 | Christian LippkaCommits: 1822Joined: 2000-09-25 | *Markus MohrhardCommits: 1745Joined: 2011-03-17 | Eike RathkeCommits: 1549Joined: 2000-10-11 | *Norbert ThiebaudCommits: 1478Joined: 2010-09-29...
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...Experimental design: Pop 100 kernels from 3 different brands of popcorn one brand at a time. Count the number of popped kernels for each brand. Repeat this process for each brand 3 times. Average the number of kernels popped for each brand to determine which brand pops the best. Variables: Dependent - amount of kernels popped. Independent – the product brand/manufacturer will vary with each test group. Controlled – cooking method, time and temperature will be regulated for each test group. Threats to internal validity: Freshness of popcorn kernels could impact ability to pop. In order to reduce this threat, the product dates should be similar. Each brand should have a manufacturing date within 3 days of each other. Hypothesis: All popcorn brands will pop the same, leaving an equal number of un-popped kernels. A1. Introduction Popcorn has been around for hundreds of years. The corn (maize) was introduced to the English settlers by the native Indians in 16th century (“Popcorn”, 2012). When heated, popcorn expends from a hard kernel into a puffed soft substance. Because popcorn is relatively cheap to purchase and make, popcorn is often seen in many areas such as at fairs, carnivals and is practically synonymous with at the movies. There is over one billion pounds of popcorn consumed in America each year (“What is the,” 2011). The love affair with popcorn has been well documented an experiments with popcorn can be traced back to the 1940’s. One such experiment...
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...type of variable is a celebrity endorsement? An independent variable is one that can be manipulated. Independent variables are observed to see if it affects any other variables. A celebrity endorsement can be manipulated by making the celebrity appear to love a product. That is just one example of manipulation. Therefore because the variable can be manipulated, a celebrity endorsement is an independent variable. * In an experiment, this variable is expected to be affected by the manipulation. When in an experiment an independent variable can be manipulated to affect the dependent variable. Most of the time the researcher has control over the independent variable which has a direct effect on the dependent variable. Usually the dependent variable is a response to the independent variable. In fact a dependent variable is called a dependent variable because it depends on another variable usually an independent variable. For example, a researcher wants to show the effects of alcohol on the liver the independent variable would be the alcohol and the dependent variable would be the functions of the liver after the alcohol. * Criterion variable is synonymous with this term. Criterion variable is synonymous with the term dependent variable. When doing research, researchers are looking for the cause and effect between independent variable and the criterion variable. The researchers observe the way a criterion variable responds to the change in the independent variable...
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...Assignment 1: Making Decisions Based on Demand and Forecasting Regression analysis is the description about the relationship between two variables where one is dependent and the other is independent. Regression analysis (in statistics), generally, is about any techniques that facilitate modeling and analysis of several variables. It focuses on the relationship between a dependent variable and one or more independent variables (Sykes, 2000). To be specific, regression analysis allows understanding of the typicality of value of the dependent variable changes, while any one of the independent variables is varied. At the same time, the other independent variables must be fixed. Usually, regression analysis estimates the expectation of conditions connected to the dependent variable given the independent variables (Sykes, 2000). Thus, the average value of the dependent variable is calculated using condition that the independent variables are held fixed. Not that often, regression analysis focuses on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. Nevertheless, the regression function is the estimation target, which is a function of the independent variables. In regression analysis, it is also necessary to characterize the variation of the dependent variable around the regression function. This can be described by a probability distribution (Sykes, 2000). Regression analysis is usually and...
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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 of .974...
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...Quantitative Analysis Instructor Name April 6, 2013 Abstract This paper will refer to regression models and the benefits that variables provide when developing and examining such models. Also, it will discuss the reason why scatter diagrams are used and will describe the simple linear regression model and will refer to multiple regression analysis as well as the potential uses for this type of model. Regression Models Regression models are a statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables). Regression models provide the scientist with a powerful tool, allowing predictions about past, present, or future events to be made with information about past or present events. Inference based on such models is known as regression analysis. The main purpose of regression analysis is to predict the value of a dependent or response variable based on values of the independent or explanatory variables. According to Render, Stair, and Hanna (2011) they are two reasons for which regression analyses are used: one is to understand the relation between various variables and the second is to predict the variable's value based on the value of the other. Variables provide many advantages when creating models. One of the advantages is that the model can be shaped in various ways which would offer the...
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...several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are held fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However...
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...HYPOTHESIS As mentioned previously, a hypothesis is a tool of quantitative studies. It is a tentative and formal prediction about the relationship between two or more variables in the population being studied, and the hypothesis translates the research question into a prediction of expected outcomes. So…a hypothesis is a statement about the relationship between two or more variables that we set out to prove or disprove in our research. study. To be complete the hypothesis must include three components: The variables. 'qualities, properties, and or characteristics of persons, things, or situations that change or vary, and that can be manipulated, measured, or controlled in a research study.' (Burns & Groves 2005:755) There are different types of variables, namely: dependent variables; independent variables. A dependent variable is the response, the behaviour, or the outcome that is predicted and measured in research. Changes in the dependent variable are presumed to be caused by the independent variables. An independent variable is the treatment, the intervention, or the experimental activity that is manipulated or varied by the researcher during the research study in order to create an effect (i.e. change) on the dependent variable. The population. A population is what we call the entire group of individuals or elements who meet the sampling criteria. A sample is representative of that population. So if we were interested in...
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...numerical measures to express the relationship between two variables. Relationships between variables can either be strong or weak or even direct or inverse. A few examples may be the amount McDonald’s spends on advertising per month and the amount of total sales in a month. Additionally the amount of study time one puts toward this statistics in comparison to the grades they receive may be analyzed using the regression method. The formal definition of Regression Analysis is the equation that allows one to estimate the value of one variable based on the value of another. Key objectives in performing a regression analysis include estimating the dependent variable Y based on a selected value of the independent variable X. To explain, Nike could possibly measurer how much they spend on celebrity endorsements and the affect it has on sales in a month. When measuring, the amount spent celebrity endorsements would be the independent X variable. Without the X variable, Y would be impossible to estimate. The general from of the regression equation is Y hat "=a + bX" where Y hat is the estimated value of the estimated value of the Y variable for a selected X value. a represents the Y-Intercept, therefore, it is the estimated value of Y when X=0. Furthermore, b is the slope of the line or the average change in Y hat for each change of one unit in the independent variable X. Finally, X is any value of the independent variable that is selected. Regression is a subject goes in depth when...
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...EXPERIMENTAL DESIGNS FOR RESEARCH Causality Experimental Designs Control Group Pre-test/Post-test Design Threats to Internal Validity Threats to External Validity Post-Test only Control Group Design CAUSALITY To establish whether two variables are causally related, that is, whether a change in the independent variable X results in a change in the dependent variable Y, you must establish: 1) time order--The cause must have occurred before the effect; 2) co-variation (statistical association)-- Changes in the value of the independent variable must be accompanied by changes in the value of the dependent variable; 3) rationale-- There must be a logical and compelling explanation for why these two variables are related; 4) non-spuriousness-- It must be established that the independent variable X, and only X, was the cause of changes in the dependent variable Y; rival explanations must be ruled out. To establish causality, one must use an experimental or quasi-experimental design. Note that it is never possible to prove causality, but only to show to what degree it is probable. EXPERIMENTAL DESIGNS True experimental designs include: Pre-test/Post-test control group design Solomon Four-Group design Post-test only control group design Pre-test/Post-test control group design This is also called the classic controlled experimental design, and the randomized pre-test/post-test design because it: 1) Controls the assignment of subjects to...
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...gained through previous research, or on a combination of both. Just because a question can be answered doesn’t mean that it can be answered scientifically. Step 2: Defining the Dependent and Independent Variable Dependent Variable (DV) | Variable the experimenter measures, after making changes to the IV that are assumed to affect the DV. | Independent Variable (IV) | Variable the experimenter manipulates (i.e. changes) – assumed to have a direct effect on the dependent variable. | Note: In many cases, the investigator does not manipulate the independent variable directly. He/She collects data and uses the data to evaluate the hypothesis, rather than doing a direct experiment. For example, the hypothesis that more crimes are committed during a full moon can be tested scientifically. The number of crimes committed is the dependent variable and can be measured from police reports. The phase of the moon is the independent variable. The investigator cannot deliberately change the phase of the moon, but can collect data during any phase he/she chooses. Why is the scientist limited to one independent variable per experiment? As the aim of an experiment is to see what happens when one thing is changed and how it affects something else. If you change two variables, then you don't know which variable is responsible for the change e.g. You are measuring the time it takes for something to fall. If you change both the height and the shape of the object, the time is lengthened. You...
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...explain the terms dependent and independent variable Calculate and interpret the correlation coefficient , the coefficient of determination, and the standard error of estimate Calculate the least squares regression line Construct and interpret confidence and prediction intervals for the dependent variable ©2006 Prentice Hall • In this chapter, we will develop numerical measures to express the relationship between two variables. Is the relationship strong or weak, is it direct or inverse? In addition, we will develop an equation to express the relationship between variables. Then, we will estimate one variable on the basis of another. • - Examples: Is there a relationship between the number of hours that student studies for an exam and the score earned? - Is there a relationship between years of employee experience and the quantity of production? - Is there a relationship between the product price and the purchasing amount of that product? - Is there a relationship between the amount of money spend per month on advertising and the monthly sales? - Is there a relationship between age and blood pressure ? Can we estimate, based on the amount of money spent on advertising in January, what is the sales in February will be? • - We begin this chapter by: Examining the meaning and purpose of Correlation Analysis. Then, We continue our study by developing a mathematical equation that help us to estimate the value of one variable based on the value...
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...Types of Variables, Conceptual and Operational Definition A variable is a concept – noun which stands for variation within a class or objects (Ariola, 2006; Catane, 2000). Variable refers to characteristic [condition or attributes] that has two or more equally exclusive values or properties (Sevilla and Others, 1988 as cited in Ardales, 1992). Ariola (2006) states that variables can be manipulated, selected, controlled and observed by the researcher or experimenter (p. 121). Therefore variables can “vary”; it is a tool in measuring the values and determination of quantity of datum for the conducted research. According to Calmorin et al. (2007) a variable is defined as a quantity susceptible of fluctuation or change in value or in magnitude under different conditions (p. 14). The term variable has the correspondent term variation, which means difference and relationship per se. In mathematics, variables are use for graphing, problem solving, statistical computations, algebraic expression, and various kinds of equations, different functions, relations and measurement to analyze and interpret certain data. It is the same with social research but those attributes apply and take different form, thus they can belong to different levels of measurement ("Variable and attribute," 2011). Variables operated or utilized as a tool of measurement is for throughput which leads to conclusion of the study. Based on Ariola (2006) the variable classified into two such as: The quantitative...
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