what is a dummy variable in regression?
116997
That is correct what that other guy said!
393026
You use regression when you want to fit a model to predict some value or outcome. Multinomial regression is where you have more than one input to predict one output. \n\nHere is an example of regression:\n\nSuppose you had the following data:\n\nHeight Weight Age for 100 people. \n\nYou might want to see if you can predict someone's age if you are given their height and weight. You call age the dependent variable because it depends on height and weight, and you can height and weight the independent variables. \n\nWhen you fit your model, you get something like this:\n\nAge = Intercept + A * Height * B * Weight\n\nThe model gives you the values of A and B and Intercept, so you can predict the Age of someone if you know their Height and Weight.\n\nThis is multinomial regression where the dependent variable is continuous, that is, it can be any value within reason and it varies.\n\nLogistic regression is used when you want a binary outcome, for example 1 meaning success and 0 failure.\n\nUsing the same example, you might want to predict someone's sex based on their height, weight and age. In this model, you would have three independent variables making it multinomial and your outcome would be 1 = male, 0 = not male, that is female. \n\nThe logistic regression model gives you the probability that, given someone's age, weight and height, that they are male (in this example, the higher the probability, the greater the chance that they person is male).\n\nAgain, you get a number of coefficients, but this time you use them to calculate a LOGIT:\n\nLOGIT = Intercept + A * Height + B * Weight * C * Age\n\nThe LOGIT is also written as:\n\nLOGIT(P) = Log [( P )/ ( 1 - P )]\n\nSo if you fit your model and get your values for A, B, C and the INTERCEPT, you can calculate the LOGIT value from some data and with this, calculate the probability P, that they are male. This probability P is given by:\n\nP = 1 / ( 1 + e**-(LOGIT))\n\nwhere ** is to the power of.\n\nThis is a much better way to model categories or classications, which are often used for understanding risk in banks and marketing, where you want to know the likelihood that someone will buy a product or default on a loan payment.
527923
Draw a scatter-plot. In short draw a graph of y against x.\nIf the graph show linear relationship, u may continue. If the graph is not linear(or curvilinear) ignore the questions as regression model is  only for linear model.\n\nSince u didn't include datas, i can only gives u equations\n\n\nQ1....\nthe mean for the number of years a teacher has taught \n    =  (∑ x)/n\n\n \nthe mean for the students' average course grade \n    =  (∑ y)/n\n\nQ2....\nthe variance for the number of years a teacher has taught \n     s²=[ ∑ (xi – mean for x value) ² ]/(n-1)\n\nthe variance for the students' average course grade\n     s²=[ ∑ (yi – mean for y value) ² ]/(n-1)\n\n\nQ3....\nthe standard deviation(s)\ns= √variance for the number of years a teacher has taught\n\ns= √variance for the students' average course grade\n\nQ4....\nthe z-score for the number of years a teacher has taught\n= (observed value of x-mean for x value)/ the standard deviation of x value\n\nthe z-score for the students' average course grade\n= (observed value of y-mean for y value)/ the standard deviation of y value.\n\nobserved value means u must calculate for each value one by one.\n\nQ5.....\nThe correlation coefficent(r)\n=1/(n-1) X  ∑(xi-mean value for x / s for x value)(yi- mean value for y)\n\ns stands for standard deviation and X is times and n is sample size\n\nThe equation looks quite tough isn't it? Normally we calculated r².\n\nr²= (SSTO-SSE)/SSTO\n\nwhere SSE = sum of squared errors\n          = ∑(yi-predicted value of y)²\n\nthe predicted value is base on the least square line( u have to draw a graph that has the sum of ( yi-predicted value of y) is smaller than any other line.) \n\n  \n     SSTO = sum of squares total\n          = ∑(yi-the mean for y value)²\n\nAfter u find r², find r by √r²\n      \nThe value of r always between -1 and +1\n#If u get value for r outside that range, means u have made some mistakes in calculation.\n#If u get r=1 or -1, it means that the relationship is perfectly linear and all data points fall on the same straight line.\n#r=+something means that when two varibles tend to increase together.(x increase, y increase)\n#r=0 means the best straight line is exactly horizontal\n\nQ6....\ni dont know about the coefficient of determination. Never heard such thing. Sorry.\n\nQ7....\nthe regression equation\n\ny-hat=b-nought  -  b-one   x\n\n  where    \nb-one = [∑(xi-the mean for x value)(yi-the mean for y value)]/  ∑(xi-the mean for x value)²\n\nb-nought = the mean for y value  -  b-one(the mean of x value)\n\nQ8....\n From y-hat=(b-nought)  -  (b-one) x\nJust simply substitude x=x1, therefore\n   predicted y = b-nought)  -  (b-one)x1\n\nQ9....\nI'm not sure about standard error of the estimate for predicting Y from X. But if if i'm not mistaken, the formula should be\n\nstandard error(s.e)= s/ [∑(yi-the mean for y value)²][b-one)\n\nwhere b-one=r(the standard deviation for x value)/(the standard deviation for y value)\n\nand s= √[SSE/(n-2)]\n\n\n\n\nGood luck
129909
You might find the following informative:\nhttp://www2.mtroyal.ab.ca/~igilborn/admn1382/regression/theory/part_b.htm
846805
Typically a passing variable is a variable that is passed from one subroutine (or function) to another.\n\nFor example.\n\nsub ImaTest()\ndim a = 2\ndim b = 3\ndim result as integer\nresult = addme(a,b)\nend sub\n\nfunction addme(byval a, byval b) as integer\naddme = a + b\nend function\n\nThe above example passes two variables (a and b) to the function addme, which returns the result.  'a' and 'b' are both passed variables.\n\n- Rich\nhttp://www.IMAgolfer.com
1135408
OH NOES THIS IS LIKE A MATH PROBLEM!!!! i can't handle any more variables :( sry.\n\n\nhahahaha.
573482
Literal coefficient: -ab\nNumerical coefficient: -1
682508
Logistic regression is a statistical regression model for binary dependent variables. It can be considered as a generalized linear model that utilizes the logit as its link function, and has binomially distributed errors.\n\nThe model takes the form\n\n    &#92;operatorname{logit}(p)=&#92;log&#92;left(&#92;frac{p}{1-p}&#92;right) = &#92;alpha + &#92;beta_1 x_{1,i} + &#92;cdots + &#92;beta_k x_{k,i}\n\ni, = 1, ..., n, where\n\n    p = &#92;Pr(Y_i = 1).&#92;,\n\nThe logarithm of the odds (probability divided by one minus the probability) of the outcome is modelled as a linear function of the explanatory variables, X1 to Xk. This can be written equivalently as\n\n    p = &#92;Pr(Y_i = 1|X) = &#92;frac{&#92;exp(&#92;alpha + &#92;beta_1 x_{1,i} + &#92;cdots + &#92;beta_k x_{k,i})}{1+&#92;exp(&#92;alpha + &#92;beta_1 x_{1,i} + &#92;cdots + &#92;beta_k x_{k,i})}.\n\nThe interpretation of the β parameter estimates is as a multiplicative effect on the odds ratio. In the case of a dichotomous explanatory variable, for instance sex, eβ (the antilog of β) is the estimate of the odds-ratio of having the outcome for, say, males compared with females.\n\nThe parameters α β1, ..., βk are usually estimated by maximum likelihood.\n\nExtensions of the model exist to cope with multi-category dependent variables and ordinal dependent variables.
813845
A variable is used when ANY number can fit into the equation.  For instance, for the speed equation (s = d/t, where s is speed, d is distance, and t is time),  any number we choose for d, and any number we choose for t will give us s.  A variable is just that, something that vaires.
199081
Sorry, but I think you need to do this yourself.  Here are some guidelines though; give them a try.\n1.  Make a line drawing so you can see the info you're given and the path to the required answer.\n2.  Make a list of ALL the related formulae you were given.  Check each one against the given data; if you find one in which you have all the variables EXCEPT one, substitute and solve for the unknown.\n3.  Sometimes, you may have to use one formula to find a variable and THEN use that in ANOTHER formula to arrive at your ansver.\n\nGood luck!!
830458
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
296819
I would say trial and error. Look at the r^2 of the regressions you get from using various internatl standards and report the one that yielded the best regression.
1171251
confounding variables are outside things that can effect results. example: examining the relationship between fish growth and food, salinity, and temperature all in the same tank. can't tell what's making the fish grow, can ya?\n\nexperiments reduce variability by controlling the environment more thoroughly.\ntake a statistics class, that helps with bio
34162
I'm guessing that you are trying to graph some cell division data and are trying to figure out which variable is the independent (x axis) and dependent (y axis) variables. Let's first start with the a rule of thumb for you: I'm assuming your graphing cell divisions over time. Anytime you are graphing something over time, time is almost always the independent variable.\n\nIt is the same in this case also. The number of cells is the dependent variable. (The number of cells present is dependent on how much time has passed).
693679
Regression (any kind of regression) is used when there is some kind of relationship between the outcome (response) and predictor variables. There will be a change in the standard error estimates of the regression coefficients. But I wouldn't expect much of a change in other aspects of the regression. The best way to check to see if there is a big change: try fitting the model with and without the predictor that uses the same question as the reponse. If there is a big change, then you'll need to decide if the predictor is of significance to the survey. In other words, how important is this predictor to the understanding of the survey? If it's important, then keep it. Otherwise, don't keep it. But you'll have to decide. Model building is an art and science.
885608
Using a simple TI-83, the data in #3 gives a linear regression equation of\ny = 0.4668x - 31.737, where y is absences and x is temperature.  r = 0.977.\n\nSound like you've been partying too much and ignoring your classes.  Well, you get what you choose.
175888
Independent variables are just that, independent of other variables. Dependent variables rely on one or more independent variables.\n\nFor example, your salary is determined by the number of hours worked. The number of hours is independent of any other variable. However, salary is dependent on the number of hours.