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Course: INDE 2333, Fall 2010
School: U. Houston
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Word Count: 451

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AGENDA Exam 3 Review EXAM 3 REVIEW Chi-squared goodness of fit test U and H Rank Sum Tests Linear Regression GOODNESS OF FIT TESTS Based on a comparison of observations between Observed data Theoretical data The comparison utilizes a set of intervals or cells Each cell has a lower and upper boundary values The determination of the boundaries are a function of Theoretical distribution Number of observations...

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AGENDA Exam 3 Review EXAM 3 REVIEW Chi-squared goodness of fit test U and H Rank Sum Tests Linear Regression GOODNESS OF FIT TESTS Based on a comparison of observations between Observed data Theoretical data The comparison utilizes a set of intervals or cells Each cell has a lower and upper boundary values The determination of the boundaries are a function of Theoretical distribution Number of observations in the sample 2 different approaches Equi-interval Equi-probable CELLS AND BOUNDARIES Maximum number of cells not to exceed 100 Expected number of observations needs to be at least 5 To calculate observed values in each cell, we must determine the actual x cell boundaries from the equiprobable cells X cell boundaries depend on distribution Normal Exponential Uniform TEST STATISTIC RANK SUM TESTS Used when the sample data is either Non-normal To small to determine normality U test H test RANK SUM U-TEST Comparing 2 data sets Alternative to: Independent t test Smith-Satherwaithe test U test Based on the rank order of sorted data sample rather than mean and standard deviation Uses Z standard normal distribution for critical value PERFORM CALCULATIONS Calculate rank sums W1 is the sum of the ranks of data from set 1 W2 is the sum of the ranks of data from set 2 Calculate Us U1=W1-n1(n1+1)/2 U2=W2-n2(n2+1)/2 U1=min (U1,U2) Next NEXT Calculate the mean of the ranks u = n1*n2/2 Calculate the variance the of ranks Sigma1 squared = n1*n2*(n1+n2+1)/12 Calculate the test statistic Z Z = (U1-u) / sigma1 RANK SUM H-TEST Comparing three or more data sets Alternative to: ANOVA (not covered in this class) H test Based on the rank order of sorted data sample rather than mean and standard deviation Uses chi-squared distribution for critical value H-TEST STATISTIC LINEAR REGRESSION Statistical method for determining a linear relationship between two variables assuming normality and independence Two types of variables Independent Dependent Method of least squares Intercept and Coefficients Significance R squared PREDICTION LINE DETERMINING PREDICTION LINE Calculate the total squared error between the Actual value Predicted value according to the prediction line NORMAL EQUATIONS With some rearrangement Resulting linear equations are called normal equations INTERCEPT AND SLOPE Substitute x, y, xy, and x squared values in normal equations Solve for a Intercept with y axis at x=0 Solve for b Coefficient of slope for x values TEST FORMAT 20 2 point multiple choice 15 4 point multiple choice requiring calculations Mostly eliminated none of the above You can write in a different answer if you strongly disagree with the provided choices Do not memorize equations with the exception of the sample variance STUDY SUGGESTIONS Get in a study group Review class notes Review homework problems Lastly do practice test
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U. Houston - INDE - 2333
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U. Houston - INDE - 2333
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