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by: Velma Toy

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# INTRO TO BUS STATS ISDS 2000

Velma Toy
LSU
GPA 3.88

J. Shreve

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COURSE
PROF.
J. Shreve
TYPE
Class Notes
PAGES
2
WORDS
KARMA
25 ?

## Popular in Info Systems & Decision Sciences

This 2 page Class Notes was uploaded by Velma Toy on Tuesday October 13, 2015. The Class Notes belongs to ISDS 2000 at Louisiana State University taught by J. Shreve in Fall. Since its upload, it has received 57 views. For similar materials see /class/222489/isds-2000-louisiana-state-university in Info Systems & Decision Sciences at Louisiana State University.

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Date Created: 10/13/15
ISDS FINAL EXAM STUDY GUIDE Know how to conduct ANOVAfor testing differences in multiple population means 105 D 5790 In any testing situation be able to identify the DEPENDENT and INDEPENDENT variables Know the assumptions of conducting an ANOVA The dependent variable is measured from observations randomly1 selected from independent2 normal3 populations with equal variances4 Know how to COMPLETE a given ANOVA Table SSA SSW demong dfwmm MSA MSW F nd critical F value based upon alpha demong dfwithin make decision and be able to interpret for decisionmaking purposes use pvalue to make a decision REMEMBER ifF gt CV then REJECT H0 ifp lt or REJECT H0 Be able to calculate Critical Range for conducting Tukey Kramer Procedure Be able to nd mean difference and compare to critical range in order to carry out a TukeyKramer procedure and make decisions In Simple Linear Regression Chapter 13 know how to E 0 extquotrquot identify the dependent and independent variables calculate the least squares slope b1 and intercept b0 SSR SSE SST and the coef cient of determination r2 use the regression equation for prediction calculate the standard error SYX understand that relatively large r2 and relatively small standard error are desirable in order to have a signi cant relationship between X and Y calculate ttest statistic and nd critical values in order to determine whether or not the population slope is signi cantly different from zero Also be able to use the pvalue to make your decision calculate Ftest statistic by lling out ANOVA table SSR SSE SST degrees of freedom MSR MSE F and nd critical value in order to determine whether or not the population slope is signi cantly different from zero Also be able to use the pvalue to make your decision calculate the correlation coef cient identify the assumptions of linear regression analysis L I N E use residual plots to detect violations of the assumptions use standardize residuals andor residual plots in order to detect outliers understand that when you reject the null hypothesis H0 510 you can conclude 1 there is evidence that the population slope is signi cantly different than zero 2 there is evidence that X is a signi cantly good predictor of Y 3 there evidence that X and Y are signi cantly related 4 there is evidence that the population correlation coef cient is signi cantly different than zero 5 there is evidence that the population p2 is signi cantly different than zero in other words there is evidence that there is a signi cantly good t of the data around the regression line In Multiple Linear Regression Chapter 14 know how to a complete a partial regression output in order to get the estimated regression equation and use for prediction b complete a partial ANOVA table in order to get an Ftest statistic c nd critical FValue in order to determine whether or not the population slope is signi cantly different from zero Also be able to use the pValue to make your decision d complete a partial ANOVA table in order to get a ttest statistic for each of the predictors e nd critical tValues in order to determine whether or not the population slopesare signi cantdifferent from zero Also be able to use the pValue to determine if Xs are good predictors In other words given two predictors determine if both predictors are good only X1 is a good predictor only X2 is a good predictor or none of the predictors are good f calculate rsquared and adjusted rsquared g de nemulticollinearity know that it is bad and how to detect it from inconsistencies in the regression analysis printout

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