MGS 4020 Business Intelligence Regression Analysis By Using Minitab Jul 14, 2011. Overview of the Regression. Agenda. Statistical Significance. Minitab. What is the Regression Analysis?.
PowerPoint Slideshow about 'MGS 4020 Business Intelligence Regression Analysis By Using Minitab Jul 14, 2011' - cicero
An Image/Link below is provided (as is) to download presentation
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
In the multivariate case, when there is more than one independent variable, the regression line cannot be visualized in the two dimensional space, but can be computed just as easily.
For example, if in addition to IQ we had additional predictors of achievement (e.g., Motivation, Self- discipline) we could construct a linear equation containing all those variables. In general then, multiple regression procedures will estimate a linear equation of the form:
The regression line expresses the best prediction of the dependent variable (Y), given the independent variables (X).
However, nature is rarely (if ever) perfectly predictable, and usually there is substantial variation of the observed points around the fitted regression line (as in the scatterplot shown earlier). The deviation of a particular point from the regression line (its predicted value) is called the residual value.
The smaller the variability of the residual values around the regression line relative to the overall variability, the better is our prediction.
For example, if there is no relationship between the X and Y variables, then the ratio of the residual variability of the Y variable to the original variance is equal to 1.0. If X and Y are perfectly related then there is no residual variance and the ratio of variance would be 0.0. In most cases, the ratio would fall somewhere between these extremes, that is, between 0.0 and 1.0.
1.0 minus this ratio is referred to as R-square or the coefficient of determination. This value is immediately interpretable in the following manner. If we have an R-square of 0.4 then we know that the variability of the Y values around the regression line is 1-0.4 times the original variance; in other words we have explained 40% of the original variability, and are left with 60% residual variability.
Ideally, we would like to explain most if not all of the original variability. The R-square value is an indicator of how well the model fits the data (e.g., an R-square close to 1.0 indicates that we have accounted for almost all of the variability with the variables specified in the model).
Adjusted R-square is the adjusted value for R-square will be equal or smaller than the regular R-square. The adjusted R-square adjusts for a bias in R-square.
R-square tends to over estimate the variance accounted for compared to an estimate that would be obtained from the population. There are two reasons for the overestimate, a large number of predictors and a small sample size. So, with a small sample and with few predictors, adjusted R-square should be very similar the R-square value. Researchers and statisticians differ on whether to use the adjusted R-square. It is probably a good idea to look at it to see how much your R-square might be inflated, especially with a small sample and many predictors.
Customarily, the degree to which two or more predictors (independent or X variables) are related to the dependent (Y) variable is expressed in the correlation coefficient R, which is the square root of R-square. In multiple regression, R can assume values between 0 and 1.
To interpret the direction of the relationship between variables, one looks at the signs (plus or minus) of the regression or B coefficients. If a B coefficient is positive, then the relationship of this variable with the dependent variable is positive (e.g., the greater the IQ the better the grade point average); if the B coefficient is negative then the relationship is negative (e.g., the lower the class size the better the average test scores). Of course, if the B coefficient is equal to 0 then there is no relationship between the variables.
In general, the purpose of analysis of variance (ANOVA) is to test for significant differences between means.
At the heart of ANOVA is the fact that variances can be divided up, that is, partitioned. Remember that the variance is computed as the sum of squared deviations from the overall mean, divided by n-1 (sample size minus one). Thus, given a certain n, the variance is a function of the sums of (deviation) squares, or SS for short. Partitioning of variance works as follows. Consider the following data set:
The statistic s square is a measure on a random sample that is used to estimate the variance of the population from which the sample is drawn.
Numerically, it is the sum of the squared deviations around the mean of a random sample divided by the sample size minus one.
Regardless of the size of the population, and regardless of the size of the random sample, it can be algebriacally shown that if we repeatedly took random samples of the same size from the same population and calculated the variance estimate on each sample, these values would cluster around the exact value of the population variance. In short, the statistic s squared is an unbiased estimate of the variance of the population from which a sample is drawn.
When the regression model is used for prediction, the error (the amount of uncertainty that remains) is the variability about the regression line, . This is the Residual Sum of Squares (residual for left over). It is sometimes called the Error Sum of Squares. The Regression Sum of Squares is the difference between the Total Sum of Squares and the Residual Sum of Squares. Since the total sum of squares is the total amount of variability in the response and the residual sum of squares that still cannot be accounted for after the regression model is fitted, the regression sum of squares is the amount of variability in the response that is accounted for by the regression model.
ANOVA is a good example of why many statistical test represent ratios of explained to unexplained variability . It refers to an estimate of the population variance based on the variability among a given set of measures. It is an estimate of the population variance based on the average of all s-square within the several samples.
The statistical significance of a result is the probability that the observed relationship (e.g., between variables) or a difference (e.g., between means) in a sample occurred by pure chance ("luck of the draw"), and that in the population from which the sample was drawn, no such relationship or differences exist. Using less technical terms, one could say that the statistical significance of a result tells us something about the degree to which the result is "true" (in the sense of being "representative of the population").
More technically, the value of the p-value represents a decreasing index of the reliability of a result (see Brownlee, 1960). The higher the p-value, the less we can believe that the observed relation between variables in the sample is a reliable indicator of the relation between the respective variables in the population.
Specifically, the p-value represents the probability of error that is involved in accepting our observed result as valid, that is, as "representative of the population."
For example, a p-value of .05 (i.e.,1/20) indicates that there is a 5% probability that the relation between the variables found in our sample is a "fluke." In other words, assuming that in the population there was no relation between those variables whatsoever, and we were repeating experiments like ours one after another, we could expect that approximately in every 20 replications of the experiment there would be one in which the relation between the variables in question would be equal or stronger than in ours. (Note that this is not the same as saying that, given that there IS a relationship between the variables, we can expect to replicate the results 5% of the time or 95% of the time; when there is a relationship between the variables in the population, the probability of replicating the study and finding that relationship is related to the statistical power of the design.).
In many areas of research, the p-value of .05 is customarily treated as a "border-line acceptable" error level. It identifies a significant trend.
The F test employs the statistic (F) to test various statistical hypotheses about the mean (or means) of the distributions from which a sample or a set of samples have been drawn. The t test is a special form of the F test.
F-value is the ratio of MSR/MSE. This shows the ratio of the average error that is explained by the regression to the average error that is still unexplained. Thus, the higher the F, the better the model, and the more confidence we have that the model that we derived from sample data actually applies to the whole population, and is not just an aberration found in the sample.
Significance of F
The value was computed by looking at standardized tables that consider the F-value and your sample size to make that determination.
If the significance of F is lower than an alpha of 0.05, the overall regression model is significant
The t test employs the statistic (t) to test a given statistical hypothesis about the mean of a population (or about the means of two populations).