Received some great help on here. I have another question. Could someone help me with Question 8, problem B and C? See problem below and work for problem A:

626 PART 2 / Multiple Regression Analysis: Explanation dependent Variables-1 627 5. What is meant by "attribute-treatment interaction"? Males ANSWERS Give examples of research problems in which the Achievement study of ATI may be important. Motivation Achievement 1. (a) b, = 1.28; b2 = 1.18; b3 = 1.19 6. Suppose that a researcher regresses Y on a continu- 17 (b) be = 1.21 ous variable, X, and on a categorical variable, A, 21 (c) SSreg = 295.86 without using the product(s) of X and A. What is he 22 (d) ssres = 295.53 4. X = 13.66 assuming? 7. In an ATI study with three treatments, A,, A2, and Females 6. The researcher is assuming that the difference between the regression coefficient for Y on X in the separate groups is 43, and an attribute, X, effect coding was used to Achievement statistically not significant, or that the use of a common regression coefficient is tenable. 7. (a) Y'A, = 26.07 + 3.49X code the treatments as follows: in El, subjects receiv- Motivation Achievement Y'A, = 16.65 + 3.13X ing treatment A , were identified (i.e., assigned 1's); in vector E2, subjects receiving treatment A2 were 12 Y'A = 18.33 +.49X (b) X, EI, E2 identified; subjects receiving treatment A3 were as- 14 13 8. (a) .836 for males, and .770 for females signed -I's in both vectors. Product vectors were (b) .69101 generated: XEl and XE2. The overall regression 15 (c) .10357 AQUAWWE 16 equation was (d) 8.71471 with 1 and 26 df, p <.01 y .76xe2 .730350x .301779xe what are the separate regression equations for three groups .428571x b vectors should be included in a analysis if one wishes to calculate common c="-7.25519" coefficient region of nonsignificance ranges from hence males and females whose scores on researcher wished determine whether regres- achievement motivation differ significantly achievement. obtained preceding results by applying karpman layout spss as i showed earlier this chapter. sion is same females. sample notice relative similarity two correlations coefficients contrasted with difference between present example equal their respective stan- she mea- dardized discussion properties recommendations when sures motivation. use or other see chapters particularly chapter following data correlation each groups. proportion variance accounted sex product. prod- uct f ratio product vector. overall equation mo- tivation vector assigned effect coded g point intersection lines. simultaneous regions significance at .05 level. plot lines interpret results.answer: explanation: problem we going formula which is: r="E(x-xmean)" ve _ let begin males: achievemen motivationt achievemet x-mean y-mean x="Achievement" get mean add all divide it subtract value its column multiply result up iymean now mmecm ymean our v zw find bottom part formula. square itself. values root therefore i5 o r_w mmean urgmmv : e>