what is univariate and multivariate analysis and what is log regression curve?
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Univariate problems depend on one variable only. multivariate pepend on several variables. A log regression curve when you have two varaibles y and x, and the relation between  x and y is of the form y = k log x
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There are two types of variables on a graph - independent and dependent.\n\nindependent variables are on the "X" axis.  They are the variables that you change during the experiment.\n\ndependent variables are on the "Y" axis.  They are the ones that change due to your manipulation of the independent variables.
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Your question isn't very clear.  Try again.\n\nYou might be thinking of the different approaches that can be taken in descriptive studies and experimental studies.  Descriptive studies look for qualitative and quantitative differences between groups, and can be analyzed using very basic chi-square, analysis of variance, and regression techniques.  Such tests can establish associations and correlations among set of variables, but they cannot be used to evaluate causation, i.e. we could say that A is strongly correlated with B in a certain group, but we cannot say that A causes B or vice-versa.\n\nIn experimental studies, observations are made in such a way that statements about causality can be made, whether the outcome is a disease or a treament for that disease.  Experimental studies should always have at least two arms, one for the intervention or exposed group, and one for the control group.  The results can be analyzed in many different ways, including analysis of variance, multiple regression, logistic regression, proportional hazards models, and life table analysis.  The results permit definite statements about whether or not exposure A is likely to be a cause of disease B, or whether treatment A is more effective, less effective, or no different than treatment B or a placebo.\n\nI hope this helps.
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Regression analysis is very complicated mathematical/statistic analysis that is very difficult to describe easily.  R-squared is a measure of how closely an investment tracks the S&P 500 or other index.  While I am not a great fan of investopedia.com, they give some answers in this case.\n(These comments refer to investment analysis, regression analysis is also used in other fields.)
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Teaching graduate students the proper ways to analyze data statistically is one of the most difficult things- It isnt interesting to them, it's hard to grasp, and there are rules for variance, independent and dependent variables, sample populations and distribution, even and uneven sampling comparisons..on and on.
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If you give only obscure details (B1, T/F) but tell nothing more about the problem, you are not likely to get an answer....I do a lot of statistical correlations, but this really doesn't mean much.\n\nNow, my guess on what you mean.\nYou have a series of data points (x_n, y_n). If you make a scatterplot and the points are rougly on a line you have perfect correlation, correlation coefficient is +1 or -1.\n\nIf on the other hand Y doesn't depend on X, i.e. correlation coefficient 0, then when you do a linear regression the slope of the regression line will be close to zero, as the data points (x_n, y_n) are scattered randomly.
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Econometrics literally means 'economic measurement'. It is a combination of mathematical economics, statistics, economic statistics and economic theory.\n\nThe two main purposes of econometrics are to give empirical content to economic theory and also to empirically verify economic theory. For example, econometrics could empirically verify if indeed a given demand curve slopes downward as economic theory would suggest. Empirical content is also given in that a numerical value would be given to this slope, while economic theory alone is usually mute on actual specific values.\n\nArguably the most important tool of econometrics is regression analysis (for an overview of a linear implementation of this framework, see linear regression).\n\nEconometric analysis can often be divided into time-series analysis and cross-sectional analysis. Time-series analysis examines variables over time, such as the effect of interest rates on national expenditure. Cross-sectional analysis studies relationship between different variables at a point in time. For instance, the relationship between income, locality, and personal expenditure. When time-series analysis and cross-sectional analysis are conducted simultaneously on the same sample, it is called panel analysis. If the sample is different each time, it is called pooled cross section data. Multi-dimensional panel data analysis is conducted on data sets that have more than two dimensions. For example, some forecast data sets provide forecasts for multiple target periods, conducted by multiple forecasters, and made at multiple horizons. The three dimensions provide more information than can be gleaned from two dimensional panel data sets.\n\nEconometric analysis may also be classified on the basis of the number of relationships modelled. Single equation methods rely on the assumption of a causal relationship between the variable of interest (the [dependent variable]]) and the explanatory or exogenous variables. If this assumption is not satisfied, the results may be subject to simultaneous equations bias. A variety of simultaneous equation methods have been developed to take account of the fact that economic variables such as prices and quantities are, in general, jointly determined in market equilibrium.\n\nMuch larger econometric models are used in an attempt to explain or predict the behavior of national economies.\n\nA simple example of a relationship in econometrics is:\n\nPersonal Expenditure = Propensity to Spend * Income + random error \nThis statement asserts that the amount a person spends is dependent on his or her income and his or her willingness to spend money. If we can observe personal expenditure and income, techniques such as regression analysis can then be applied to find the value of the coefficients, here just the propensity to spend. The estimated coefficient can then be compared across samples (such as different countries or income brackets) and conclusions made.\n\nThe above example can also be used to illustrate the many difficulties facing the applied econometrician. For instance, do we really know that the above relationship is correct? Perhaps the true relationship between personal expenditure and income is non-linear (that is, curved). Even if we know the correct theory, it is not certain we can measure personal expenditure and income correctly. For instance, the value of work by homemakers is not recorded although it contributes to income. There are also a variety of statistical pitfalls that potentially lead to incorrect conclusions. Econometrics has dealt extensively with such issues. Often it turns out to be difficult to fully implement the resulting methods in practice.\n\nPeople\nNobel Memorial Prize in Economics recipients in the field of econometrics:\n\nJan Tinbergen and Ragnar Frisch were awarded in 1969 (the first Nobel Prize for Economic Sciences) for having developed and applied dynamic models for the analysis of economic processes \nLawrence Kl
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The scatter graph lets you plot data points based on two independent variables.
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log y = d log (cx)\n\nlogy = d log c + d logx\n\nlet logx = X\nlog y = Y\n\nand d logc = C\n\nthe equation becomes\n\nY=dX+C\n\nthis is of the form y=mx+c which is a straight line
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Well, the y-intercept would be acceleration when the force on the object is zero.  You'd better hope it's zero!  If it's not, you might want to fix the point in your analysis (this can be done in Excel) or redo the experiment.\n\nEdit: If you're plotting acceleration vs. force, the intercept really should be zero.  Those values appear small enough to be within the error of the experiment.  They could be indicative of the effect of a systematic error, but are more likely the result of random noise.  Try fixing the intercepts at zero in Exel (right-click on the regression line and open up the options for it; there should be a checkbox for "fix intercept").  An intercept value that is not equal to zero indicates that there was acceleration when there was no force!
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If you have a quantitative dependent variable and a nominal independent variable, then the one-way ANOVA test is appropriate.\n\nIf you have nominal dependent and independent variables, then a chi-square test for independence is appropriate.
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A Natural log. It is expressed as: ln()\nFor example: ln(e^2) = 2
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First load the add-in:\nTools > Add-Ins > Analysis Tool Pack\nthen find it:\nTools > Data Analysis & Choose Regression\nIf you need any more help mail me.
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Forecasting is neither pure art nor pure science. Its a mix.\n\nFor oil consumption, I think it depends on variables like inflation, oil price, population, tax. Seasonal effect might have to be considered as well (you might need to do 'deseasonalization'.\n\nExcel and Minitab can do simple statistical methods like weighted average, exponential smoothing, and regression. Complex analyses require advanced software like SPSS.\n\nBy the way, I recommend that you ask an expert for forcasting. Because selection of method and setting parameters (you need to know statistical concepts) are far more difficult than choosing software.
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To use Excel to create a regression analysis: \n-Import supplied data into your own Excel spreadsheet. \n-Use the graph Wizard to create a line graph representing that data.\n-Modify data to demonstrate adaptability of Chart Wizard \n-Upload spreadsheet to your website\n\nSee the second link for a better understanding.  Have fun.
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Multiple regression is often used to test a model with a few free parameters. There exist several methods to find the optimal values. \n\nTrial and error *is* one of these methods but not a very efficient one. It is often used as starting method to get an idea of *where* in the parameter space the optimal solutions are located.
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time usually\n\nThe horizonatal axis is usually the independent (or input) variable and the vertical axis is the dependent (or output) variable whose value depends on the value chosen for the input\n\neg v = f(t)\n\nt is the input and v (= f(t)) is the output\n\nHowever if you had the equation in the form t = g(v) then v would be the input and so the horizontal axis and t would be the output