2.1) (Ch. 7) Explain what a residual is (also known as residual of prediction).
e idea of “least squares” in regression (you need to fully read pp. 200-208 to understand).
3) What does it mean if b = 0?
4) What does it mean when r-squared is 0? What does it mean when r-squared is 1?
5) What is the difference in an unstandardized regression coefficient and the standardized regression coefficient?
6) If a report says test performance was predicted by number of cups of coffee (b = .94), what does the .94 mean? Interpret this. (For every one unit increase in ___,There is an increase in ___ )
7) If F (2,344) = 340.2, p < .001, then what is this saying in general about the regression model? (see p. 217)
8) Why should you be cautious in using unstandardized beta? (p. 218)
9) (Ch. 8) Explain partial correlation in your own words. In your explanation, explain how it is different from zero-order correlation (aka Pearson r).
10) (Ch. 9) What is the F statistic used to determine in multiple regression?
11) What is F when the null hypothesis is true?
12) In Table 9.4, which variable(s) are statistically significant predictors?
13) In Table 9.4, explain what it means if health motivation has b = .36 in terms of predicting number of exercise sessions per week.
14) What is the benefit of interpreting standardized beta weights? (see p. 264).
15) What happens if your predictor variables are too closely correlated?
16) Reflect on your learning. What has been the most difficult? How did you get through it? What concepts are still fuzzy to you? Is there anything you could share with me that would help me address how you learn best?
4.This is my lab on optical isomers and crystal field theory. I need help answering these post lab questions:
Based on the energies you found in c and d, which of these complexes has the largest predicted splitting between the t2g orbitals and the eg orbital. Remember--not every element has occupied d-orbitals. How is that going to impact your next answers?
Which one would absorb the longer wavelength of light? Explain why.
Transition metal complexes are often vibrantly colored due to the splitting of the d-orbitals caused by the ligands. Based on your results would you expect that both of these complexes have any significant color? Explain.
7.PLAN AND DESIGN CHAPTER
Hi, my thesis is on the locus of control and psychological well-being of adolescents. I have completed
adolescents. I have completed the Literature review. I have to plan a research design for the thesis. ABOUT THE THESIS:
Population- 13+ to 19 years adolescents
No of IV s- 2 that is, Gender is the First IV and Locus of Control ( LOC) is the second IV
Levels of IV- Gender has 2 levels ( male and female). LOC has two levels ( Internal Locus of Control and External Locus of Control). Both IV s are categorical variables.
List of DV s-
1. Self Esteem
4. Academic motivation
5. Exam Anxiety
6. Life Style
Each DV will be measured by using appropriate Statistical scales. All DVs will be taken as continuous variables. All the scales measure the quantitative aspect only.
DV MEASUREMENT: Each of the scales that will be used to measure DV contains several areas or dimensions or sub-categories. For example, the self-esteem scale contains 78 items divided into 6 categories like personal, social, emotional, academic, intellectual and moral.
THESIS AIM- To check the impact of IV s on each of these DV s in isolation and also investigate the interaction effects between gender and locus of external. I will be using the SPSS package for calculations.
Problem 1: Which is the most appropriate Statistical test or design that should be used here? I believe a 2x2 ANOVA will be best suited here.
Problem 2: If I am measuring the impact of IV s on each DV in isolation, should I use several Two Way AONVA tables or a single MANCOVA table?
Problem 3: Each of the DV is measured using scales containing several dimensions. Are such dimensions of the scales need to be treated as the levels of the dependent variables? In other words, do the levels of DV are decided as per the dimensions of the scale that was used to measure the DV? If this is so, then even if I am measuring the DV s in isolation; each DV will have multiple levels, which in turn will change my design from Two Way ANOVA to perhaps MANCOVA? What is the right approach here?
Note: I have not intentionally divided DV into any levels.
Kindly help me to arrive at a statistically significant research design! If possible, kindly briefly explain the type of the design as well as the rational or suitability of the sign for my research problem.