Define coordinate covalent bond

1of 23Solving Linear RegressionProblems as a General Linear ModelHomework problems are multiple answer rather than multiple choice. The format for multiple answer questions is shown in the examplebelow.The directions for the problems instruct you to mark the check boxes for all of the statements that are true. One or more answersmust be marked for each problem. Full or partial credit is computed for each question. To receive full credit, you must mark all of the correct answers and not mark any of the incorrect answers. Partial credit is computed by summing the points for each correct response and subtracting points for each incorrect answer.If the computation for partial credit results in a negative number, zero credit is assigned.Level of Measurement Requirementand Sample Size RequirementMultiple regression requires that the dependent variable be interval and the independent variables be interval or dichotomous. If one of thevariablesis ordinal level, we will follow the common convention of treating ordinal variables as interval level, but we should consider notingthe use of an ordinal variable as a limitation toour findings.These problems usethe rule of thumb from Tabachnick and Fidell that the required number of cases should be the largerof the number of independent variables x 8 + 50 or the number of independentvariables + 105.If the sample size requirement (along with the level of measurement requirement) is satisfied, the check box “The level of measurement requirement and the sample size requirement are satisfied” should be marked. In many of problemswe have worked, failing to meet sample size implies that it is an inappropriate application of the statisticand we halted all further work on the problem. We will not apply that policy to these problems.If our sample size is less than theminimum requirement, we leave the check box unmarked and continue with the problem, mention the sample size issuesas a limitation for the analysis.
2of 23The Assumption of NormalityRegression assumesthat the residual are normally distributed. We will meet this assumption if each of theinterval variablesisnormally distributed, but there is general consensus that violations of this assumption do not seriously affect the probabilities needed for statistical decision making, especially when the sample size is large.The problems evaluate normality based on the criteria that the skewness and kurtosis of eachvariable fallswithin the range from -1.0 to +1.0. If the variablessatisfies these criteria for skewness and kurtosis, the check box “The skewness and kurtosis of the variablessatisfy the assumption of normality” should be marked. If the criteria for normality are not satisfied, the check boxshould remain unmarked and we should consider includinga statement about the violation of this assumption in the discussion of our results.In these problems we will not test transformations or consider removing outliers to improve the normality of the variables.The Assumption of HomoscedasticityRegressionassumes that the variance of the residualsis homogeneous across predicted values of the dependent variable. SPSS does notcomputeLevene’s test for equality of variance when all of the variables are interval(or ordinal treated as interval).The checkbox “The regression analysis satisfies assumption of homoscedasticity” will remain unchecked for these problems.The Assumption of LinearityThe assumption oflinearity is tested with the lack of fit test in the Univariate General Linear Model procedure. If the test is significant, it implies that there is a non-linear component that should be added to the model. If the test is not significant, we assume that a linear model is present and is an adequate representation of the relationship between the dependent and independent variables.If the lack of fit test is not significant at the alpha level for diagnositic statistics, the check box “The regression analysis satisfies the assumption of linearity” is marked.The Assumption of Independence of ErrorsSPSS does not compute the Durbin-Watson statistic in the Univariate General Linear Model procedure. In these problems, we will acknowledge that factand not mark the check box “The regression analysis satisfies the assumption of independence of errors”.The Assumption of Independence of VariablesSPSS does not compute tolerance for VIF in the Univariate General Linear Model procedure. In these problems, we will acknowledge that factand not mark the check box “The regression analysis satisfies the assumption of independence of variables”.I have included the complete list of assumptions in the list of possible answers even though some will not ever be marked in this assignment because of limitations in the univariate general linear procedure. In the future, we will develop a strategy for testing all of the assumptions.