Use the dependent variable (labeled Y) and one of the independent variables (labeled X1, X2, and X3) in the data file. Select and use one independent variable throughout this analysis. Use Excel to perform the regression and correlation analysis to answer the following. The week 6 spreadsheet can be helpful in this work.

• Generate a scatterplot for the specified dependent variable (Y) and the selected independent variable (X), including the graph of the “best fit” line. Interpret.
• Determine the equation of the “best fit” line, which describes the relationship between the dependent variable and the selected independent variable.
• Determine the correlation coefficient. Interpret.
• Determine the coefficient of determination. Interpret.
• Test the utility of this regression model by completing a hypothesis test of b=0 using ?=0.10. Interpret results, including the p-value.
• Based on the findings in steps 1-5, analyze the ability of the independent variable to predict the dependent variable.
• Compute the confidence interval for b, using a 95% confidence level. Interpret this interval.
• Compute the 99% confidence interval for the dependent variable, for a selected value of the independent variable. Each student can choose a value to use for the independent variable (use same value in the next step). Interpret this interval.
• Using the same chosen value for part (8), estimate the 99% prediction interval for the dependent variable. Interpret this interval.
• What can be said about the value of the dependent variable for values of the independent variable that are outside the range of the sample values? Explain.
• Describe a business decision that could be made based on the results of this analysis. In other words, how might the business operations change based on these statistical results.

I’m working on a statistics multi-part question and need a sample draft to help me learn.

You want to develop aregression model to predict the number of wins achieved byeach NBA team, based on the average point difference and the average field goal percentage difference between the team and itsopponent. Using the Data Analysis add-in, follow the steps listed below and answer the following questions.

6.1Perform multiple linear regression analysis. State the regression equation. 

6.2Interpret the meaning of the slope coefficientsobtained.Explain whether the intercept coefficient hasapractical interpretation or not in the context of this study.

6.3Based on your model, predict wins for a teamwith average point difference of 6, and average field goal % difference of 3.5.

6.4Based  on  the  residual  plots received in  your  model,  comment  on  the plausible satisfaction  of  the normal residual (noise) model assumptions.

6.5 Do you have to use the Durbin-Watson statistic? 

6.6  Repeat  steps  6.1  to  6.4  to  develop  a multiple  linearregression model  that  also  considers  possible interaction between average point difference, and average field goal % difference. Draw your conclusion.

6.7 Based on the results received so far, select and develop a simple linear regression model, repeating steps6.1 to 6.4 for this model. Draw your final conclusion.Submit both your answers summarized in a Word doc file, and your complete Excel solution file. In the Excel file, use the Data Analysis add-in, and store the results in separate (appropriately named)tabs.

The chi-square test is the most important and most used nonparametric test. According to the Green and Salkind text, a one-sample chi-square test “evaluates whether the proportions of individuals who fall into categories of a variable are equal to hypothesized values” (p. 257). Additionally, the Mann-Whitney U test “evaluates whether the medians on a test variable differ significantly between two groups” (p. 270). The Mann-Whitney U test is a nonparametric alternative to the independent-sample t test.

This week, you will use the IT Security Dataset to run the One-Sample Chi-Square Test and the Mann-Whitney U Test.

Use the Assignment Exemplar.

Your paper must include the following elements:

• An APA Results Section for each nonparametric test. (See examples of APA Results Sections in Lessons 40: One-Sample Chi-Square Test (p. 260) and Mann-Whitney U Test (p. 270) of the Green and Salkind text.
• The critical elements of your SPSS output, including:
  • A properly formatted research question
  • A properly formatted H1₀ (null) and H1_a (alternate) hypothesis
  • A descriptive statistics narrative and properly formatted descriptive statistics table (see page 261 and page 272 of the Green and Salkind text)
  • A properly formatted frequency distribution graph for the one-sample chi-square (see page 262) and a boxplot for the Mann-Whitney U Test (see page 270)
  • A properly formatted inferential APA Results Section (see page 260 and page 270 of the Green and Salkind text)
  • An appendix including the SPSS output generated for descriptive and inferential statistics (for SPSS output, see page 261 for the one-sample chi-square and page 272 for the Mann Whitney U test)

There are two team questions in the “exploration” section at the end of Chapter 3 in “Finite Mathematics”. Each question has multiple parts so you should find a way to contribute supporting evidence, graphs, arguments, etc. for either of the questions and either sides of the possible issue. As your response for the week the class should be using the evidence that was posted to try to wrap up a summary of the issue.

Two questions from teams discussion:

1. Find a graph that illustrates population and another that illustrates food production or availability. Make sure the two graphs are for the same population (whatever population you choose). Where do these two graphs intersect? Does that “solution” imply that the population should or should not have sufficient food?

2. Find graphs or equations that illustrate two of either women’s wages, overall wages, and/or men’s wages over time in the same type of jobs. Analyze the data to see if genders appear to be paid the same amount for the same job. Is the experience considered in the data? Find a study comparing men’s and women’s pay and look at the information used to determine if other variables are held constant within the study so that the results are a true comparison of men and women in the same job with the same education and experience.