I am looking for a paper pertaining the following ASAP:

Computer Lab: Statistical ModelingResources

• Computer Lab: Statistical Modeling Scoring Guide.
• Writing Feedback Tool.
• Comprehensive Exam Rubric Feedback.
• Discovering Statistics Using IBM SPSS Statistics.

For this assignment, you will analyze the relationship between a dependent variable and independent variables, and analyze a data model created by SPSS. Complete the following:

• Load the SPSS file, Child Aggression.sav, from the datasets section of the companion Web site of your Discovering Statistics Using IBM SPSS Statistics Companion text (given in the resources).
• Analyze the research question completely: What is the relationship between the dependent variable aggression, and the independent variables parenting style, time spent playing computer games, time spent watching television, diet, and sibling aggression?

Based on the theoretical literature, both parenting style and sibling aggression were good predictors of level of aggression. Answer all of the following questions and compare your results with Smart Alex's Answers for Task 5 of Chapter 8 on the companion Web site of your Discovering Statistics Using IBM SPSS Statistics text. Step-by-step instructions are given in Chapter 8 of your Discovering Statistics Using IBM SPSS Statistics text.

• Explore and discuss whether the assumptions for regression analysis hold, showing evidence.
• Run an SPSS model using a two-stage hierarchical multiple regression.
• Complete the following:
  • State explicitly the estimated regression model.
  • Describe the hypotheses is being tested.
  • Support the decisions being made on rejecting the null hypothesis.
  • Discuss the inferences that can be made to the population parameters and the population model.
  • Discuss how much of the variation in the dependent variable is explained with this model.
  • Explain how the ANOVA table is related to regression analysis. What hypothesis does the F test answer?
  • Decide whether there is evidence of multicollinearity, and demonstrate your reasoning.
  • Conduct a residual analysis, providing graphs and interpretation.
  • Explain whether the errors are normally distributed and whether there is evidence of homoscedasticity.
  • Describe how reliable, valid, and generalizable the model is.
  • Discuss the independent variables you found to be significant at the .05 level and the r-squared value of your best model.